The DOI matches its Crossref record; 75 of the 82 references it cites were found in a registry or an index; 23 of the 80 sources its claims cite were checked for support.
Artificial Intelligence and Surgical Decision-making.
Open accessAbstract
Review of AI as an augmentation to complex, high-stakes surgical decision-making, emphasizing interpretability, careful implementation, ethical challenges, accountability, and preservation of human intuition and bedside assessment.
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Claims the paper makes
24 Own interpretation · 18 No full text of the sources · 13 Partly supported · 4 Couldn’t confirm · 4 Sources not found · 3 Supported · 1 Not checked yet · accuracy not yet measured
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Traditional clinical decision support tools, such as the National Surgical Quality Improvement Program (NSQIP) Surgical Risk Calculator, can reduce variability and mitigate risks, but their clinical adoption is hindered by suboptimal accuracy and time-consuming manual data acquisition and entry requirements.3–8”
§ Artificial Intelligence and Surgical Decision-Making > Abstract > CONCLUSIONS AND RELEVANCE
What the sources say
- Not checked — no full text of the sourcecites [3] source
- Not checked — no full text of the sourcecites [4] source
- Not checked — no full text of the sourcecites [5] source
- Not checked — no full text of the sourcecites [6] source
- Not checked — no full text of the sourcecites [7] source
- Partly supportedcites [8] sourceExperimental — accuracy not yet measured
The Introduction (page unavailable) says the NSQIP Risk Calculator produces more consistent assessments of surgical complications, supporting the variability claim. It says enhanced use may improve care through risk profiling and targeted interventions, but does not establish that the calculator mitigates surgical risks. (LLM, conf=0.83)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“Parametric regression prognostic scoring systems assume that relationships among input variables are linear.22,29”
§ Artificial Intelligence and Surgical Decision-Making > Observations > Traditional Predictive Analytics and Clinical Decision Support
What the sources say
- Not checked — no full text of the sourcecites [22] source
- Could not verifycites [29] sourceExperimental — accuracy not yet measured
The Methods section identifies APACHE III as a logistic-regression-based prediction model, and the Results compare it with machine-learning models. Neither section states that parametric prognostic models assume linear relationships among input variables; searches for linearity, nonlinearity, and model assumptions found no passage addressing that point. (LLM, conf=0.72)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Even the most successful and widely used static variable cutoff values do not accurately represent individual patient pathophysiology, as reflected by their suboptimal accuracy.34,43,56”
§ Artificial Intelligence and Surgical Decision-Making > Observations > Artificial Intelligence Predictive Analytics and Augmented Decision-Making
What the sources say
- Partly supportedcites [34] sourceExperimental — accuracy not yet measured
The Introduction describes the Strate rule as a robust model for severe acute lower intestinal bleeding, and the Results report that it was less accurate than a neural network (AUC 0.656 versus 0.979). The paper does not establish that static cutoffs generally fail to represent individual patient pathophysiology; its comparison is limited to this prediction rule and patient population. Page numbers were not available in our copy. (LLM, conf=0.85)
- Not checked — no full text of the sourcecites [43] source
- Not checked — no full text of the sourcecites [56] source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“When applied to the same variable set used to calculate SOFA scores, deep models outperform traditional SOFA modeling in predicting in-hospital mortality for ICU patients.42”
§ Artificial Intelligence and Surgical Decision-Making > Observations > Artificial Intelligence Predictive Analytics and Augmented Decision-Making
What the sources say
- Supportedcites [42] source
The Methods (Data Processing) state that the deep-learning models were limited to SOFA input variables to permit direct comparison. The Results (External validation of DeepSOFA model) report that DeepSOFA significantly outperformed traditional SOFA models in predicting in-hospital mortality in ICU cohorts. (LLM, conf=0.94)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Traditional clinical decision support tools, such as the National Surgical Quality Improvement Program (NSQIP) Surgical Risk Calculator, can reduce variability and mitigate risks, but their clinical adoption is hindered by suboptimal accuracy and time-consuming manual data acquisition and entry requirements.3–8”
§ Artificial Intelligence and Surgical Decision-Making > Abstract > CONCLUSIONS AND RELEVANCE
What the sources say
- Not checked — no full text of the sourcecites [3] source
- Not checked — no full text of the sourcecites [4] source
- Not checked — no full text of the sourcecites [5] source
- Not checked — no full text of the sourcecites [6] source
- Not checked — no full text of the sourcecites [7] source
- Partly supportedcites [8] sourceExperimental — accuracy not yet measured
The Results (Table 2) identify slow, cumbersome information entry as a barrier to using surgical risk calculators, and the Discussion identifies poor integration with electronic records and workflows. The paper does not specifically establish time-consuming manual data acquisition as a separate barrier. (LLM, conf=0.83)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“When the relationships are nonlinear, the scoring system is similar to a coin toss.11”
§ Artificial Intelligence and Surgical Decision-Making > Observations > Traditional Predictive Analytics and Clinical Decision Support
What the sources say
- Partly supportedcites [11] sourceExperimental — accuracy not yet measured
The Discussion describes the evaluated scores as regression-based, and the Performance of mortality prediction models in the entire dataset section reports poor discrimination in colorectal surgery, with several AUCs near 0.5. But the paper does not examine nonlinear relationships among input variables or attribute the scores’ performance to them; its finding is also limited to the surgical patients studied. Page numbers were not provided in our copy. (LLM, conf=0.84)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Machine learning has been used to accurately predict sepsis, in-hospital mortality, and acute kidney injury using intraoperative time-series data.9,21,27,32”
§ Artificial Intelligence and Surgical Decision-Making > Observations > Artificial Intelligence Predictive Analytics and Augmented Decision-Making
What the sources say
- Partly supportedcites [9] sourceExperimental — accuracy not yet measured
The Abstract Methods and Conclusions describe a machine-learning model that uses intraoperative physiological time-series data to improve prediction of postoperative acute kidney injury. The paper does not report using that model to predict sepsis or in-hospital mortality; its Results list sepsis as an observed outcome, not a prediction target. (LLM, conf=0.88)
- Not checked — no full text of the sourcecites [21] source
- Not checked — no full text of the sourcecites [27] source
- Not checked — no full text of the sourcecites [32] source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Traditional clinical decision support tools, such as the National Surgical Quality Improvement Program (NSQIP) Surgical Risk Calculator, can reduce variability and mitigate risks, but their clinical adoption is hindered by suboptimal accuracy and time-consuming manual data acquisition and entry requirements.3–8”
§ Artificial Intelligence and Surgical Decision-Making > Abstract > CONCLUSIONS AND RELEVANCE
What the sources say
- Not checked — no full text of the sourcecites [3] source
- Not checked — no full text of the sourcecites [4] source
- Not checked — no full text of the sourcecites [5] source
- Not checked — no full text of the sourcecites [6] source
- Not checked — no full text of the sourcecites [7] source
- Partly supportedcites [8] sourceExperimental — accuracy not yet measured
In Results (Table 2; page unavailable), residents identify lack of evidence as a barrier, including one concern that the database cannot accurately stratify risk. The paper documents a perceived accuracy concern among surgical trainees, but does not establish that the calculator’s accuracy is suboptimal or that this is a general cause of poor clinical adoption. (LLM, conf=0.85)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Academic intensivists make approximately 56 patient care and resource use decisions per day.36”
§ Artificial Intelligence and Surgical Decision-Making > Observations > Challenges in Surgical Decision-Making
What the sources say
- Partly supportedcites [36] sourceExperimental — accuracy not yet measured
The Results report almost 56 ad hoc decisions per intensivist in two university-hospital ICUs, covering patient care and ICU operations. But 56 is a count per observed intensivist, not a daily rate: recordings lasted two to six hours per person, and the paper gives a separate per-day estimate for ICU shift leaders collectively. (LLM, conf=0.88)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Traditional clinical decision-support systems require time-consuming manual data acquisition and entry, which impairs their adoption.8,33”
§ Artificial Intelligence and Surgical Decision-Making > Observations > Artificial Intelligence Predictive Analytics and Augmented Decision-Making
What the sources say
- Partly supportedcites [8] sourceExperimental — accuracy not yet measured
In Results (Table 2; page unavailable), a surgical trainee identifies entering information into a risk calculator as “way too slow and cumbersome,” and the Discussion links underuse to poor integration with clinical workflows. This supports time-consuming entry as an adoption barrier for surgical risk calculators, but the paper does not establish that traditional clinical decision-support systems generally require manual data acquisition. (LLM, conf=0.85)
- Not checked — no full text of the sourcecites [33] source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
- It may state two separate claims as one. 21
- Probably not a substantial claim. 18
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Surgical diseases may evoke fear and anger, which influence perceptions of risks and benefits.23,51”
§ Artificial Intelligence and Surgical Decision-Making > Observations > Challenges in Surgical Decision-Making
What the sources say
- Partly supportedcites [23] sourceExperimental — accuracy not yet measured
The Introduction says that changes in health status may evoke fear or anger, and the Discussion reports small, preliminary effects of those emotions on some risk perceptions. The study used a hypothetical medication decision, not surgical diseases, and did not establish an effect on perceptions of benefits. (LLM, conf=0.86)
- Not checked — no full text of the sourcecites [51] source
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Partly supported
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“These impairments are exacerbated by acute and chronic sleep deprivation, which occurs in as many as two-thirds of all acute care surgeons taking in-house call.52,53”
§ Artificial Intelligence and Surgical Decision-Making > Observations > Challenges in Surgical Decision-Making
What the sources say
- Not checked — no full text of the sourcecites [52] source
- Could not verifycites [53] sourceExperimental — accuracy not yet measured
The Sleep-dependent memory enhancement section discusses how sleep deprivation affects volunteers’ performance on memory tasks, not how often surgeons are sleep-deprived. Searches for acute care surgeons, in-house call, and the two-thirds figure found no passage addressing the claimed prevalence. (LLM, conf=0.76)
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“To capitalize on this phenomenon, Super Learner ranks a set of candidate algorithms20,28,38,39 by their performance and applies an optimal weight to each, creating ensemble algorithms that can accurately predict transfusion requirements and mortality among trauma patients.20,28,38,39”
§ Artificial Intelligence and Surgical Decision-Making > Observations > Artificial Intelligence Predictive Analytics and Augmented Decision-Making
What the sources say
- Partly supportedcites [20] sourceExperimental — accuracy not yet measured
The Methods (SuperLearner Prediction; page not available) describe evaluating multiple algorithms and using cross-validation to form an optimally weighted ensemble. The Results (SL Discrimination of Mortality; page not available) report accurate mortality prediction in three trauma cohorts. The paper does not report prediction of transfusion requirements. (LLM, conf=0.88)
- Partly supportedcites [28] sourceExperimental — accuracy not yet measured
The Methods (page unavailable) say SuperLearner uses cross-validation to combine candidate algorithms and select the best-performing approach. The Abstract Results and Results > Prediction report predictions of ongoing transfusion and death in trauma patients. Our copy does not specify that SuperLearner applies an optimal weight to each candidate algorithm. (LLM, conf=0.82)
- Partly supportedcites [38] sourceExperimental — accuracy not yet measured
The Introduction and Prediction Algorithms section describe ranking candidate algorithms by predictive performance and combining them with optimal weights. The Results report accurate ICU mortality prediction, but the study included a mixed ICU population rather than evaluating trauma patients separately, and it did not assess transfusion requirements (Results > Patients; Patients and Methods > Data Collection and Patients). (LLM, conf=0.90)
- Partly supportedcites [39] sourceExperimental — accuracy not yet measured
The Super Learner section describes comparing candidate algorithms by cross-validated risk and combining them with weights chosen to minimize estimated risk. The paper studies propensity-score estimation in simulated data; it does not establish accurate prediction of transfusion requirements or mortality among trauma patients. (LLM, conf=0.88)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Parametric regression equations also fail to represent the complex, nonlinear associations among input variables, further limiting the accuracy of traditional multivariable regression models.22,29”
§ Artificial Intelligence and Surgical Decision-Making > Observations > Artificial Intelligence Predictive Analytics and Augmented Decision-Making
What the sources say
- Not checked — no full text of the sourcecites [22] source
- Partly supportedcites [29] sourceExperimental — accuracy not yet measured
The Results (III.3; page not provided) report that the decision-tree model had a larger ROC area than logistic regression, offering some support for a limitation in its predictive performance in this ICU comparison. The paper does not say that parametric regression equations fail to represent complex, nonlinear associations among inputs or identify that as the reason for the difference; it also reports that all four models had good discriminatory performance. (LLM, conf=0.78)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“Approximately 153 billion GB of data were generated in 2013, with projected growth of 48% per year.67”
§ Artificial Intelligence and Surgical Decision-Making > Observations > Implementation
What the sources say
- Could not verifycites [67] sourceExperimental — accuracy not yet measured
The Introduction discusses growth in publications about emotion, while Global Organismic States discusses methods for measuring brain activity; neither addresses data generated in 2013. Searches for “153 billion GB” and “48% per year” found no passage supporting either figure in our copy. (LLM, conf=0.78)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Could not verify
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“This would require several elements that communicate with one another reliably and efficiently, including robust quality filters, a public key infrastructure, and encryption that can only be deciphered by the intended receiver.68”
§ Artificial Intelligence and Surgical Decision-Making > Observations > Implementation
What the sources say
- Could not verifycites [68] sourceExperimental — accuracy not yet measured
The passages from the paper’s Abstract discuss amygdala-related learning and social decision-making, not a system for delivering model outputs to mobile devices. Searches for quality filters, public key infrastructure, encryption, and secure delivery found no passage addressing those requirements. (LLM, conf=0.77)
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Could not verify
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Surgical decision-making is dominated by hypothetical deductive reasoning and individual judgment, which are highly variable and ill-suited to remedy these errors.”
§ Artificial Intelligence and Surgical Decision-Making > Abstract > CONCLUSIONS AND RELEVANCE
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: Surgical decision-making is dominated by hypothetical-deductive reasoning, individual judgment, and heuristics.
In the paper
“Even when data collection and analysis are complete, high decision-making volume begets decision fatigue, manifesting as procrastination, less persistence when facing adversity, decreased physical stamina, and lower quality and quantity of mathematic calculations.49”
§ Artificial Intelligence and Surgical Decision-Making > Observations > Challenges in Surgical Decision-Making
What the sources say
- Not checked — no full text of the sourcecites [49] source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“However, input variables must be entered manually, and its predictive accuracy is suboptimal, especially for nonelective operations, representing opportunities for improvement.4–7”
§ Artificial Intelligence and Surgical Decision-Making > Observations > Traditional Predictive Analytics and Clinical Decision Support
What the sources say
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“The negative predictive value was 97%, such that a low value usually indicates no leak, but the positive predictive value was 21%.”
§ Artificial Intelligence and Surgical Decision-Making > Observations > Traditional Predictive Analytics and Clinical Decision Support
What the sources say
- Not checked — no full text of the sourcecites [43] source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Reinforcement learning has been used to recommend optimal fluid resuscitation and vasopressor administration strategies for patients with sepsis.31 Ninety-day mortality was lowest when care provided by clinicians was concordant with model recommendations.”
§ Artificial Intelligence and Surgical Decision-Making > Observations > Artificial Intelligence Predictive Analytics and Augmented Decision-Making
What the sources say
- Not checked — no full text of the sourcecites [31] source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“Most diseases are not driven by a single physiologic parameter; therefore, prognostic scoring systems often incorporate multiple parameters for tasks such as measuring illness severity and predicting stroke and severe gastrointestinal bleeding.24,45,57”
§ Artificial Intelligence and Surgical Decision-Making > Observations > Traditional Predictive Analytics and Clinical Decision Support
What the sources say
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For a surgical oncologist with a busy outpatient clinic, automated production of prognostic data from artificial intelligence models could improve efficiency and preserve face-to-face patient-surgeon interactions by obviating manual data acquisition and entry into prognostic models.”
§ Artificial Intelligence and Surgical Decision-Making > Observations > Challenges in Surgical Decision-Making
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“Understanding and incorporating these values is essential to an effective shared decision-making process.50”
§ Artificial Intelligence and Surgical Decision-Making > Observations > Challenges in Surgical Decision-Making
What the sources say
- Not checked — no full text of the sourcecites [50] source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“In a systematic review44 including 31 043 patients facing screening or treatment decisions, patients exposed to decision aids felt more knowledgeable and played a more active role in the decision-making process.”
§ Artificial Intelligence and Surgical Decision-Making > Observations > Traditional Predictive Analytics and Clinical Decision Support
What the sources say
- Not matchedcites [44]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaincorrelationaln = 31,043- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
In the paper
“Reinforcement learning has also been used to recommend basal and bolus insulin administration for virtual type 1 diabetics.46 The algorithm performed as well as standard intermittent self-monitoring and continuous glucose monitoring methods, but with fewer episodes of hypoglycemia.”
§ Artificial Intelligence and Surgical Decision-Making > Observations > Artificial Intelligence Predictive Analytics and Augmented Decision-Making
What the sources say
- Not matchedcites [46]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
In the paper
“In a systematic review of 17 studies investigating decisions made by surgical patients, decision aids were associated with more knowledge regarding treatment options, preference for less invasive treatments, and no observable differences in anxiety, quality of life, morbidity, or mortality.30”
§ Artificial Intelligence and Surgical Decision-Making > Observations > Traditional Predictive Analytics and Clinical Decision Support
What the sources say
- Not checked — no full text of the sourcecites [30] source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Patient values are individualized by nature, precluding the creation of a criterion standard of optimal decision-making.”
§ Artificial Intelligence and Surgical Decision-Making > Observations > Challenges in Surgical Decision-Making
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Similar methods could be applied to augment the decision to operate.”
§ Artificial Intelligence and Surgical Decision-Making > Observations > Artificial Intelligence Predictive Analytics and Augmented Decision-Making
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
In the paper
“Initial prospective implementation should occur on a small scale under close monitoring, similar to phase 1 and 2 clinical trials for experimental medications, with analysis of how decision-support tools affect decisions across populations and among individual patients.74”
§ Artificial Intelligence and Surgical Decision-Making > Observations > Challenges to Adoption
What the sources say
- Not matchedcites [74]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
In the paper
“The My Surgery Risk platform uses EHR data for 285 variables to predict 8 postoperative complications with an area under the curve (AUC) of 0.82–0.94 and to predict mortality at 1, 3, 6, 12, and 24 months with an AUC of 0.77–0.83.15”
§ Artificial Intelligence and Surgical Decision-Making > Observations > Implementation
What the sources say
- Not checked — no full text of the sourcecites [15] source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“If model inputs are flawed or model outputs are not carefully monitored by data scientists and interpreted by astute clinicians, many patients could be harmed in a short time frame.”
§ Artificial Intelligence and Surgical Decision-Making > Observations > Challenges to Adoption
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“To produce models that may be integrated with any EHR in any setting, data must be standardized.”
§ Artificial Intelligence and Surgical Decision-Making > Observations > Challenges to Adoption
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Artificial intelligence models must be rigorously analyzed in a retrospective fashion with robust external validation prior to prospective clinical application under the close scrutiny of astute clinicians and data scientists.”
§ Artificial Intelligence and Surgical Decision-Making > Conclusions
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Artificial intelligence models trained on erroneous or misrepresentative data are likely to obscure the truth.”
§ Artificial Intelligence and Surgical Decision-Making > Observations > Challenges to Adoption
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Prior to clinical implementation, machine and deep learning models must be rigorously analyzed in a retrospective fashion and externally validated to ensure generalizability.”
§ Artificial Intelligence and Surgical Decision-Making > Observations > Challenges to Adoption
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Successful integration of artificial intelligence with surgical decision-making would require data standardization, advances in model interpretability, careful implementation and monitoring, attention to ethical challenges, and preservation of bedside assessment and human intuition in the decision-making process.”
§ Artificial Intelligence and Surgical Decision-Making > Conclusions
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Our justice system is well-equipped to address scenarios in which an individual clinician is responsible for making an errant decision, but it may prove difficult to assign blame to a computer program and its developers.”
§ Artificial Intelligence and Surgical Decision-Making > Observations > Challenges to Adoption
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Surgical decision-making is impaired by time constraints, uncertainty, complexity, decision fatigue, hypothetical-deductive reasoning, and bias, leading to preventable harm.”
§ Artificial Intelligence and Surgical Decision-Making > Conclusions
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: unclear — It is unclear why the paper says it holds.
It may state two separate claims as one.
- Rests on an assumption: Surgical decision-making is dominated by hypothetical-deductive reasoning, individual judgment, and heuristics.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Traditional decision-support systems are compromised by time-consuming manual data entry and suboptimal accuracy.”
§ Artificial Intelligence and Surgical Decision-Making > Conclusions
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Because studies with positive results are more likely to be submitted and published, artificial intelligence literature may be overly optimistic.”
§ Artificial Intelligence and Surgical Decision-Making > Observations > Challenges to Adoption
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
- Rests on an assumption: Studies with positive results are more likely to be submitted and published.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Properly applied, artificial intelligence has the potential to transform surgical care by augmenting the decision to operate, the informed consent process, identification and mitigation of modifiable risk factors, recognition and management of complications, and shared decisions regarding resource use.”
§ Artificial Intelligence and Surgical Decision-Making > Conclusions
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds. It may state two separate claims as one.
The paper relies on this without support.
Surgical decision-making is dominated by hypothetical-deductive reasoning, individual judgment, and heuristics.
“Surgical decision-making is dominated by hypothetical-deductive reasoning, individual judgment, and heuristics.”
The paper relies on this without support.
There is a relative paucity of research investigating surgical decision-making and strategies to improve it.
“Although decision-making is one of the most difficult and important tasks that surgeons perform, there is a relative paucity of research investigating surgical decision-making and strategies to improve it.”
The paper relies on this without support.
The volume of data generated by electronic health records is ideal for artificial intelligence models, which thrive on large data sets.
“This data volume is ideal for artificial intelligence models, which thrive on large data sets.”
The paper relies on this without support.
Automated surgical risk predictions with mobile device outputs have not yet been reported.
“To our knowledge, automated surgical risk predictions with mobile device out puts have not yet been reported.”
The paper relies on this without support.
Diligent clinicians and informed patients will want to know why a computer program made a certain prediction or recommendation.
“Diligent clinicians and informed patients will want to know why a computer program made a certain prediction or recommendation.”
The paper relies on this without support.
Studies with positive results are more likely to be submitted and published.
“Because studies with positive results are more likely to be submitted and published, artificial intelligence literature may be overly optimistic.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“In the perioperative domain, MySurgeryRisk [30] demonstrated that ML-based prediction of postoperative complications could complement physician judgment”
From Prediction of Postoperative ICU Requirements: Closing the Translational Gap with a Real-World Clinical Benchmark for Artificial Intelligence Approaches (2025) · cites this paper as [30]
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“However, these approaches did not establish a structured benchmark that translates subjective physician judgment into reproducible, imbalance-aware performance metrics.”
From Prediction of Postoperative ICU Requirements: Closing the Translational Gap with a Real-World Clinical Benchmark for Artificial Intelligence Approaches (2025) · cites this paper as [30]
Experimental — accuracy not yet measured
Citations
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BackgroundDecision aids for people facing health treatment or screening decisions
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A guide to deep learning in healthcare
Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., & Dean, J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0316-z · Cited by 5,312 · cited 1×
“60,62,63 Deep models have successfully performed patient phenotyping, disease prediction, and mortality prediction tasks.”
BackgroundDeciding advantageously before knowing the advantageous strategy
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“69,70 Subsequently, similar patterns or situations evoke positive and negative emotions, or gut feelings, which are powerful and effective decision-making tools.”
BackgroundBurnout and Medical Errors Among American Surgeons
Shanafelt, T. D., Balch, C. M., Bechamps, G., Russell, T., Dyrbye, L., Satele, D., Collicott, P., Novotny, P. J., Sloan, J., & Freischlag, J. (2010) · Annals of Surgery · doi:10.1097/sla.0b013e3181bfdab3 · Cited by 2,099 · cited 2×
“Articles were screened by reviewing their abstracts for the following criteria: (1) published in English, (2) published in a peer-reviewed journal, and (3) primary literature or a review article.”
MethodSleep-dependent memory consolidation
Stickgold, R. (2005) · Nature · doi:10.1038/nature04286 · Cited by 1,857 · cited 1×
“52,53 For a surgical oncologist with a busy outpatient clinic, automated production of prognostic data from artificial intelligence models could improve efficiency and preserve face-to-face patient-surgeon interactions by obviating manual data acquisition and entry into prognostic models.”
BackgroundShickel, B., Tighe, P. J., Bihorac, A., & Rashidi, P. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2767063 · Cited by 1,688 · cited 1×
“61 Automatic feature extraction also promotes discovery of novel patterns and phenotypes that may have been overlooked by handcrafted feature selection techniques.”
BackgroundSelf-citationRethinking the Emotional Brain
LeDoux, J. (2012) · Neuron · doi:10.1016/j.neuron.2012.02.004 · Cited by 1,594 · cited 1×
“71, 72 Intuition can also identify patients with life-threatening conditions that would be underappreciated by traditional clinical parameters alone.”
Background
Research works printed but not linked: 1
- Kahneman, 2013, Thinking, Fast and Slow
Cited by
Sorted by citations
Items 201–300 of 605
Artificial intelligence as an adjunctive tool in hand and wrist surgery: a review
Dababneh, S., Colivas, J., Dababneh, N. E., et al. (2024) · Artificial Intelligence Surgery · doi:10.20517/ais.2024.50 · Cited by 9
Lim, J., Chang, S., Kim, K., et al. (2025) · Journal of Orthopaedic Surgery and Research · doi:10.1186/s13018-025-05830-z · Cited by 9
“Surgical decision-making is a complex process influenced by various factors, including the emotions and values of patients and caregivers, medical resource availability, rapport between patients and surgeons, time constraints, and individual judgments [33].”
InfluentialApplication of 5G robotic telesurgery in urology: a multicenter study in the real world
Yu, S., Tao, J., Bohong, G., et al. (2025) · Surgical Endoscopy · doi:10.1007/s00464-025-11969-x · Cited by 9
“Emerging technological innovations, such as AI-driven decision support systems, have the potential to further enhance the capabilities and safety of remote surgery [21].”
Stanikzai, M. E., & Mittal, E. (2025) · Cogent Business & Management · doi:10.1080/23311975.2025.2560650 · Cited by 26
“…to create more efficient and sustainable processes by integrating them into day-to-day operations. ai and automation tools play a vital role in various operational activities, alleviating managerial burdens and enhancing decision-making processes across different departments (loftus et al., 2020).”
Implementing predictive tools in surgery: A narrative review in the context of orthopaedic surgery
Zhou, Y., Gould, D. J., Choong, P., Dowsey, M. M., & Schilling, C. G. (2022) · ANZ Journal of Surgery · doi:10.1111/ans.18044 · Cited by 10
“However, for every successful predictive tool used in clinical practice, there are many more tools that have been developed but are never utilized in clinical practice.(33) Therefore, this review aims to explore the factors needed to implement predictive tools into clinical practice.”
MethodLi, J., Jin, Y., Chen, Y., et al. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43996-4_47 · Cited by 9
Jain, H., Marsool, M. D. M., Odat, R. M., Noori, H., Jain, J., Shakhatreh, Z., Patel, N., Goyal, A., Gole, S., & Passey, S. (2024) · Cardiology in Review · doi:10.1097/crd.0000000000000708 · Cited by 10
Stam, W. T., Ingwersen, E. W., Ali, M., Spijkerman, J. T., Geert Kazemier, M., Bruns, E. R. J., & Daams, F. D. (2023) · Surgery Today · doi:10.1007/s00595-023-02662-4 · Cited by 14
“Multiple studies suggest that these models will play a critical role in future surgical decision-making [51].”
BackgroundBuilding resilient surgical systems that can withstand external shocks
Glasbey, J. C., Ademuyiwa, A. O., Chu, K., Dare, A. J., Harrison, E. M., Hutchinson, P., Hyman, G., Lawani, I., Martin, J. E., Martínez, L., et al. (2024) · BMJ Global Health · doi:10.1136/bmjgh-2024-015280 · Cited by 9
“64 Important conclusions, however, can be drawn from simple but high-quality data.”
Electronic health record use and perceptions among urologic surgeons
Tan, H.-J., Chung, A. E., Gotz, D., Deal, A. M., Heiling, H. M., Teal, R., Vu, M. B., Meeks, W. D., Fang, R., Bennett, A. V., et al. (2023) · Applied Clinical Informatics · doi:10.1055/s-0043-1763513 · Cited by 8
van Varsseveld, O. C., ten Broeke, A., Chorus, C. G., Heyning, N., Kooi, E. M. W., & Hulscher, J. B. F. (2023) · Frontiers in Pediatrics · doi:10.3389/fped.2023.1122188 · Cited by 8
“accordance with two potentially different thought-processes between the two specialist groups: (1) neonatologists leaning more towards the consideration whether the child will be majorly impaired in the long-term (birth weight, gestational age) and; (2) pediatric surgeons leaning more towards the consideration whether…”
BackgroundMethodInfluentialRole of surgery in T4N0-3M0 esophageal cancer
Chen, Q., Hu, L., Zhang, C., et al. (2023) · World Journal of Surgical Oncology · doi:10.1186/s12957-023-03239-8 · Cited by 8
“What’s more, artificial intelligence models fed with live-streaming electronic health record data can be used to help doctors in decision-making perioperatively [22, 23].”
BackgroundNew possibilities of artificial intelligence in medicine: a narrative review
Litvin, A., Стома, И. О., Михайловна, Ш. Т., Rumovskaya, S. B., & Kyovalev, A. A. (2024) · Health and Ecology Issues · doi:10.51523/2708-6011.2024-21-1-01 · Cited by 9
Kasai, S., Shiomi, A., Shimizu, H., Aoba, M., Kinugasa, Y., Miura, T., Uehara, K., Watanabe, J., Kawai, K., & Ajioka, Y. (2024) · BJS Open · doi:10.1093/bjsopen/zrae073 · Cited by 8
“Improving diagnostic performance is essential in various medical fields, and the use of artificial intelligence (AI) has attracted attention in recent years 20,21 .”
BackgroundLimitations of artificial intelligence in healthcare
Booven, D. V., Chen, C., & Meenakshy, M. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-15504-8.00008-9 · Cited by 9
Loftus, T. J., Haider, A. H., & Upchurch, G. R. (2025) · JAMA Surgery · doi:10.1001/jamasurg.2024.6025 · Cited by 9
Self-citationYaseen, M. M., Alsharif, F. H., Altaf, R. A., Asiri, T. W., Bagies, R. M., Alharbi, S. B., & Altaf, B. A. S. (2025) · Nursing Research and Practice · doi:10.1155/nrp/9371969 · Cited by 16
Human vs Machine: The Future of Decision-making in Plastic and Reconstructive Surgery
Duran, A., Demiröz, A., Çörtük, O., Ok, B., Özten, M., & Eroğlu, S. (2025) · Aesthetic Surgery Journal · doi:10.1093/asj/sjaf015 · Cited by 8
Deep Learning Models to Detect Anterior Cruciate Ligament Injury on MRI: A Comprehensive Review
Mercurio, M., Denami, F., Melissaridou, D., Corona, K., Cerciello, S., Laganà, D., Gasparini, G., & Minici, R. (2025) · Diagnostics · doi:10.3390/diagnostics15060776 · Cited by 11
“…that are capable of performing tasks that mimic human cognitive functions such as learning and problem solving by analyzing and comparing data [12,13]. to a branch of computer systems that are capable of that mimic human cognitive functions such as learning and problem and comparing data [12,13].”
Robot-Assisted Minimally Invasive Esophagectomy: Current Best Practice
Kooij, C. D., Goense, L., Kingma, B. F., et al. (2025) · Digestive Surgery · doi:10.1159/000546749 · Cited by 8
Weselá, P., Eid, M., Moravčík, P., Vlažný, J., Hlavsa, J., Procházka, V., Kala, Z., & Vaňhara, P. (2025) · Cell Division · doi:10.1186/s13008-025-00158-w · Cited by 10
“Another problem in the implementation of AI in medical practice is the absence of legal standards that address the issue of responsibility in case of misdiagnosis by ML-based tools, as they can have fatal consequences [19, 75].”
Greenberg, J. K., Otun, A., Ghogawala, Z., Yen, P., Molina, C. A., Limbrick, D. D., Foraker, R. E., Kelly, M. P., & Ray, W. Z. (2021) · Global Spine Journal · doi:10.1177/21925682211008424 · Cited by 10
“4 Each of these analytical techniques contains multiple nuances and variations, including approaches to handling clustered and longitudinal data.”
BackgroundMethodHuman-Machine Collaboration in AI-Assisted Surgery: Balancing Autonomy and Expertise
Shein, G. S., Brodie, R., & Mintz, Y. (2023) · Artificial intelligence · doi:10.5772/intechopen.111556 · Cited by 8
Zhang, D., Du, J., Shi, J. M., et al. (2024) · JOR Spine · doi:10.1002/jsp2.1342 · Cited by 8
Harbi, M. A., Alotaibi, A., Alanazi, A., Alsughayir, F., Alharbi, D., Qassim, A. B., Alkhwaiter, T., Olayan, L. H., Alzaid, M., & Alsabani, M. H. (2024) · Health Science Reports · doi:10.1002/hsr2.70099 · Cited by 9
“31 The current study demonstrated that awareness and basic comprehension of AI applications in clinical practice were moderate among anesthesiologists.”
BackgroundINTEGRATING ARTIFICIAL INTELLIGENCE IN HUMAN RESOURCE MANAGEMENT: A COMPREHENSIVE OVERVIEW
Bharadwaj, V. (2024) · JOURNAL OF MANAGEMENT · doi:10.34218/jom_11_03_006 · Cited by 16
Analysis of pulmonary artery variation based on 3D reconstruction of CT angiography
Ma, X., Lu, T., Qin, D., et al. (2023) · Frontiers in Physiology · doi:10.3389/fphys.2023.1156513 · Cited by 8
Artificial intelligence and perioperative medicine
Bignami, E. G., Cozzani, F., RIO, P. D., & Bellini, V. (2020) · Minerva Anestesiologica · doi:10.23736/s0375-9393.20.14999-x · Cited by 12
Cobianchi, L., Mas, F. D., & Ansaloni, L. (2022) · Frontiers in Surgery · doi:10.3389/fsurg.2022.933673 · Cited by 8
“As of today, AI- and Machine and Deep Learning-based applications in oncology and surgery have the primary aim of supporting clinical decision-making (3).”
BackgroundApplication progress of artificial intelligence in tumor diagnosis and treatment
Sun, F., Zhang, L., & Tong, Z. (2025) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1487207 · Cited by 10
Henn, J., Vandemeulebroucke, T., Hatterscheidt, S., Dohmen, J., Kalff, J. C., van Wynsberghe, A., & Matthaei, H. (2025) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-025-03326-z · Cited by 7
Lorenz, W. R., Holland, A. M., Sarac, B. A., Kerr, S. W., Wilson, H. H., Ayuso, S. A., Murphy, K., Scarola, G. T., Mead, B. S., Heniford, B. T., et al. (2025) · Journal of Abdominal Wall Surgery · doi:10.3389/jaws.2025.14371 · Cited by 7
Enabling Technology in Fracture Surgery
Farid, A. R., Comtesse, S., Sagi, H. C., Frosch, K., Weaver, M. J., Yoon, R. S., & Keudell, A. G. V. (2025) · Journal of Bone and Joint Surgery · doi:10.2106/jbjs.24.00938 · Cited by 11
Neurosurgery reimagined: How AI is redefining patient care and surgical excellence
Mansour, M. A., El-Salamoni, M. A.-F., Zohney, M., Elshaer, A. M., Abdelwahab, M., Aziz, M. M., Ayoub, B. M., Mostafa, H. N., & Elsamman, A. K. (2025) · Intelligent Surgery · doi:10.1016/j.isurg.2025.06.002 · Cited by 8
Ongün, P., KÖZE, B. Ş., & Altınbaş, Y. (2025) · Healthcare · doi:10.3390/healthcare13162021 · Cited by 8
“A variety of smart health applications are being developed for these purposes, including AI-powered robots, mobile applications, wearable sensors, and smart devices [3–5].”
A Synopsis of Artificial Intelligence and its Applications in Surgery
Rajesh, A., Chartier, C., Asaad, M., & Butler, C. E. (2022) · The American Surgeon · doi:10.1177/00031348221109450 · Cited by 8
“In the preoperative setting, surgeons counsel patients on their candidacy for surgical management of their respective disease.(4) In its most primitive form, this assessment employs static cutoff values for laboratory test results (hemoglobin, international normalized ratio, C-reactive protein, base deficit, etc.”
BackgroundWilson, H. H., Ma, C., Ku, D., et al. (2024) · Surgical Endoscopy · doi:10.1007/s00464-024-10980-y · Cited by 7
“3 The mixed-value model incorporated the predictions of both the image-only and clinical-only models, performing better than the image-only model but not as well as the clinical-only model implementation [16].”
BackgroundMethodInfluentialAnania, G., Chiozza, M., Pedarzani, E., Resta, G., Campagnaro, A., Pedon, S., Valpiani, G., Silecchia, G., Mascagni, P., Cuccurullo, D., et al. (2024) · Cancers · doi:10.3390/cancers16162857 · Cited by 7
“Integrating MLTs into the predictive framework enhances the accuracy of postoperative hospital stay predictions, enabling personalized patient care and setting new standards in surgical research and practice [9].”
BackgroundReactive Artificial Intelligence Using Big Data in the Era of Precision Medicine
Kar, A., Subash, A., & Rao, V. U. S. (2020) · JAMA Surgery · doi:10.1001/jamasurg.2020.0839 · Cited by 7
Du, M., Tao, M., Jian, H., Zhou, D., & Wang, S. (2020) · IEEE Access · doi:10.1109/access.2020.3008000 · Cited by 7
Mlakar, I., Smrke, U., Flis, V., Bergauer, A., Kobilica, N., Kampič, T., Horvat, S., Vidovič, D., Musil, B., & Plohl, N. (2022) · Digital Health · doi:10.1177/20552076221129068 · Cited by 8
“the optimal solution, clinicians’ tiredness and uncertainty, proneness to heuristics and biases, information overload, inadequate staffing, and time constraints.(3,4) Hence, it is not very surprising that a range of errors with varying severity can occur.”
BackgroundAntimicrobial Challenge in Acute Care Surgery
Schena, C. A., de’Angelis, G. L., Carra, M. C., Bianchi, G., & de’Angelis, N. (2022) · Antibiotics · doi:10.3390/antibiotics11101315 · Cited by 8
Clinical Information Systems – Seen through the Ethics Lens
Hübner, U., Egbert, N., & Schulte, G. (2020) · Yearbook of Medical Informatics · doi:10.1055/s-0040-1701996 · Cited by 7
“In turn, clean, reliable, and valid data would bring AI into the position to mitigate injustice in decision making arising from human preconceptions [37].”
BackgroundTowards the governance of government data using artificial intelligencePreprint
Alqudah, M. A. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3992303 · Cited by 7
Shao, S., Li, Y., Cheng, H., Chen, C., Zeng, Y., Huang, W., Luo, H., Yu, X., Yin, X., Sun, X., et al. (2025) · International Journal of Surgery · doi:10.1097/js9.0000000000003025 · Cited by 7
“As computational technology continues to advance and applications expand, the role of machine learning in the medical field is expected to become increasingly central, potentially evolving into an important tool for supporting medical decision-making [ 16 , 18 , 19 ] .”
Xu, P., Liu, M., Liu, M., & Shen, A. (2025) · Intelligent Oncology · doi:10.1016/j.intonc.2025.09.001 · Cited by 8
Nakao, E., Igeta, M., Kobayashi, N., et al. (2025) · Surgery · doi:10.1016/j.surg.2025.109723 · Cited by 6
Ling, G., Zuo, H., Sun, Y., & Qian, C. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1665160 · Cited by 12
The Emerging Role of Multimodal Artificial Intelligence in Urological Surgery
Buck, L., Kohler, J., Risch, J., Incesu, R., Hügelmann, K., Weiß, M.-L., Weische, O., Schließer, P., Knobloch, H. C., Blessin, N. C., et al. (2025) · Current Oncology · doi:10.3390/curroncol32120665 · Cited by 6
“While AI systems optimize decision-making through objective data analysis and risk prediction, they must never replace the surgeon’s final clinical decision [75].”
Predicting Colonic Neoplasia Surgical Complications: A Machine Learning Approach
Nwaiwu, C. A., Perla, K. M. R., Abel, L., Sears, I., Barton, A. T., Peterson, R. C., Liu, Y. Z., Khatri, I. S., Sarkar, I. N., & Shah, N. J. (2024) · Diseases of the Colon & Rectum · doi:10.1097/dcr.0000000000003166 · Cited by 7
“16,17 ML, a branch of AI that teaches a machine how to learn, is based on the idea that systems can identify patterns in data and use those patterns to make decisions with minimal human intervention.”
BackgroundRevolutionizing patient care: the harmonious blend of artificial intelligence and surgical tradition
Stark, M. M. (2024) · International Journal of Clinical and Experimental Pathology · doi:10.62347/fric2771 · Cited by 6
Bektaş, M., Tan, C., Burchell, G. L., Daams, F. D., & van der Peet, D. L. (2024) · European Journal of Surgical Oncology · doi:10.1016/j.ejso.2024.108385 · Cited by 7
Ongün, P., Gül, B., Muslu, İ. E., Meşe, M. M., & Ergün, S. (2024) · Bandırma Onyedi Eylül Üniversitesi Sağlık Bilimleri ve Araştırmaları Dergisi · doi:10.46413/boneyusbad.1455856 · Cited by 6
Artificial Intelligence in Surgical Research: Accomplishments and Future Directions
Rogers, M. P., Janjua, H. M., Walczak, S. M., Baker, M. S., Read, M. D., Cios, K. J., Velanovich, V., Pietrobon, R. S., & Kuo, P. C. (2023) · The American Journal of Surgery · doi:10.1016/j.amjsurg.2023.10.045 · Cited by 6
Application of Artificial Intelligence in Shoulder Pathology
Cheng, C., Liang, X., Guo, D., & Xie, D. (2024) · Diagnostics · doi:10.3390/diagnostics14111091 · Cited by 6
Lorkowski, J., & Pokorski, M. (2022) · Journal of Clinical Medicine · doi:10.3390/jcm11071885 · Cited by 6
“In silico modeling uses AI algorithms that allow a program to be “thought” by raw data [37].”
MethodImplications of Artificial Intelligence in Stroke Intervention and Care
Yadav, J., More, A., Ghosh, B., Sinha, D., Chavane, N., Kumari, A., Datta, A., Borah, A., & Bhattacharya, P. (2025) · iRadiology · doi:10.1002/ird3.70005 · Cited by 6
Hsu, D., Persitz, J., Noori, A., Zhang, H., Mashouri, P., Shah, R., Chan, A. H. W., Madani, A., & Paul, R. (2025) · The Journal Of Hand Surgery · doi:10.1016/j.jhsa.2025.04.015 · Cited by 5
Applications and quality assurance of artificial intelligence in adult spinal deformity surgery
Sigurdarson, H., Joshi, A., Mohebi, A., & Hassanzadeh, H. R. (2025) · Artificial Intelligence Surgery · doi:10.20517/ais.2024.35 · Cited by 5
Zerrouki, Y., Baran, J. V., Bandi, R., Moothedan, E., Knecht, M. K., Follin, T. A., & Perumareddi, P. (2025) · Cureus · doi:10.7759/cureus.93793 · Cited by 5
“These innovations show promise in improving the accuracy of assessments, reducing training variability, and accelerating the development of surgical expertise [7].”
Surgical education reimagined: the convergence of learning theories and artificial intelligence
Lee, F., & Wong, S. W. (2025) · BMC Medical Education · doi:10.1186/s12909-025-08039-0 · Cited by 7
“Factors such as timing, risk, and resource limitations make it challenging to fully integrate AI into surgical education [3].”
Evolving surgical teams in the age of artificial intelligence and robotics
Granados, A., Khanna, R., Fischer, N., Raison, N., Ciabattini, M., Robertshaw, H. H., Boels, M., Malik, M., Granados, V., Vercauteren, T. K., et al. (2026) · Frontiers in Science · doi:10.3389/fsci.2026.1783803 · Cited by 5
Inteligencia artificial: futuro y desafíos en la medicina moderna
Aedo-Martín, D. (2023) · Revista Española de Cirugía Ortopédica y Traumatología · doi:10.1016/j.recot.2023.03.015 · Cited by 6
Gao, L., & Xing, B. (2024) · Journal of Orthopaedic Surgery and Research · doi:10.1186/s13018-024-04666-3 · Cited by 6
“The role of artificial intelligence (AI) in bone surgery represents a significant advancement in orthopedic surgery, particularly in the treatment of complex fractures and deformities [21, 22].”
BackgroundThe future of artificial intelligence in perioperative nursing
Nijkamp, N., & Wakefield, E. (2024) · Journal of Perioperative Nursing · doi:10.26550/2209-1092.1323 · Cited by 7
Davis, J. M. K., Niazi, M. K. K., Ricker, A. B., Tavolara, T. E., Robinson, J. N., Annanurov, B., Smith, K., Mantha, R., Hwang, J. J., Shrestha, R., et al. (2024) · Journal of Surgical Oncology · doi:10.1002/jso.27673 · Cited by 5
Liao, G., Duoji, J., Mu, L., et al. (2024) · Journal of Orthopaedic Surgery and Research · doi:10.1186/s13018-024-05010-5 · Cited by 5
“In recent years, the application of AI in fields of radiology diagnosis and surgical decision-making shows its potential capabilities [19, 20].”
BackgroundPatra, A., Biswas, P., Behera, S. K., Barpanda, N. K., Sethy, P. K., & Nanthaamornphong, A. (2024) · Journal of Intelligent Systems · doi:10.1515/jisys-2024-0172 · Cited by 8
LACOSTE: Exploiting stereo and temporal contexts for surgical instrument segmentation
Wang, Q., Zhao, S., Xu, Z., & Zhou, S. K. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103387 · Cited by 6
AI integration in pediatric surgery: bridging innovation, equity, and adaptive decision-making
Chen, K., & Yu, Z. (2025) · Pediatric Surgery International · doi:10.1007/s00383-025-05993-0 · Cited by 5
“However, such systems must prioritize surgeon autonomy, serving as collaborative tools rather than autonomous agents [8, 9].”
Wang, H., Long, Y., Chen, Y., Yip, H. C., Scheppach, M. W., Chiu, P. W. Y., Yam, Y., Meng, H. M. L., & Dou, Q. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103599 · Cited by 5
“Although deep learning-based approaches have demonstrated significant potential in surgical scene analysis [1, 2, 3], enhancing aspects such as intelligent workflow recognition [4, 5] and scene understanding [6, 7], research on advanced precise assitance for surgical procedures remains limited.”
Durand, X., Hedou, J. J., Bellan, G., et al. (2025) · Annals of Surgery Open · doi:10.1097/as9.0000000000000578 · Cited by 5
“12,13 Not only does this pipeline optimize the model’s predictive power, but it also derives an interpretable machine learning model.”
Wang, J., Wang, Y. P., Yao, C., & Huang, P.-J. (2020) · IEEE Access · doi:10.1109/access.2020.3001212 · Cited by 5
“Chloride ions mainly from hydrochloric acid with hydrogen ions, promote the absorption of iron in the body, and active amylase, inhibit the growth of bacteria in the stomach [10].”
BackgroundDevelopment of a Visual Guidance System for Laparoscopic Surgical Palpation using Computer Vision
Caballas, K. G., Bolingot, H. J. M., Libatique, N. J. C., & Tangonan, G. L. (2021) · IEEE-EMBS Conference on Biomedical Engineering and Sciences · doi:10.1109/iecbes48179.2021.9398796 · Cited by 5
Artificial Intelligence for Healthcare: Roles, Challenges, and Applications
Kafhali, S. E., & Lazaar, M. (2021) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-72588-4_10 · Cited by 6
The future implementation of artificial intelligence technology in esophageal surgery
Peek, G., & Ross, S. B. (2023) · Artificial Intelligence Surgery · doi:10.20517/ais.2023.56 · Cited by 5
Artificial intelligence‐based diagnosis in fetal pathology using external ear shapes
Hennocq, Q., Garcelon, N., Bongibault, T., et al. (2024) · Prenatal Diagnosis · doi:10.1002/pd.6577 · Cited by 5
Gonzalez, X. T., Steger-May, K., & Abraham, J. (2024) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocae257 · Cited by 4
Kermansaravi, M., Kermansaravi, A., Kroh, M., Shikora, S. A., & COHEN, R. V. (2025) · Obesity Surgery · doi:10.1007/s11695-025-07999-y · Cited by 7
“This is especially true in areas involving complex clinical decision-making by emulating human cognitive functions [1].”
InfluentialDyas, A. R., Colborn, K. L., Bronsert, M. R., Henderson, W. G., Mason, N. J., Rozeboom, P. D., Pradhan, N., Lambert‐Kerzner, A., & Meguid, R. A. (2021) · Seminars in Thoracic and Cardiovascular Surgery · doi:10.1053/j.semtcvs.2021.11.008 · Cited by 5
Liu, L., & Hu, Z. (2022) · Mobile Information Systems · doi:10.1155/2022/3052469 · Cited by 5
“*e use of AI technology to empower existing complex decision-making systems to improve their intelligent decision-making capabilities is known as AI-enabled systems [17].”
BackgroundUsing Artificial Intelligence in Patient Care—Some Considerations for Doctors and Medical Regulators
Ooi, K. (2024) · Asian Bioethics Review · doi:10.1007/s41649-024-00291-8 · Cited by 7
“This is because the quality of the doctor-patient relationship and the clinical decisions made as a result 1 3 Asian Bioethics Review (2024) 16:483–499 depend crucially on how they discuss information and communicate with each other (Loftus et al. 2020; Quinn et al. 2022).”
Survey on the Impact of AI, Robotics and 6G Networks on the Remote Surgery
Mohammedali, N. A., Kanakis, T., & Agyeman, M. O. (2024) · International Conference on Software, Telecommunications and Computer Networks (SoftCOM) · doi:10.23919/softcom62040.2024.10721746 · Cited by 4
“[24].”
Fairness of machine learning readmission predictions following open ventral hernia repair
Zander, T. L., Kendall, M. A., Wolansky, R. L., Grimsley, E. A., Parikh, R., Sujka, J. A., & Kuo, P. C. (2025) · Surgical Endoscopy · doi:10.1007/s00464-025-11927-7 · Cited by 6
“Machine learning models can capture these complex relationships and may provide superior predictive performance [11, 12].”
Radulesco, T., Ebode, D., Maniaci, A., et al. (2025) · Aesthetic Plastic Surgery · doi:10.1007/s00266-025-05010-8 · Cited by 4
Zhu, D., Tulahong, A., Liu, C., Aierken, A., Tan, W., Ruze, R., Yuan, Z., Yin, L., Jiang, T., Lin, R., et al. (2025) · World Journal of Gastroenterology · doi:10.3748/wjg.v31.i37.111038 · Cited by 4
Oh, M., Kim, H.-S., Jung, Y. M., Lee, H.-C., Lee, S.-B., & Lee, S. M. (2025) · Scientific Reports · doi:10.1038/s41598-025-24826-7 · Cited by 4
“These findings may assist clinicians in understanding this prediction model and may support clinical decision-making based on the model 16,24 .”
Chen, Z., Gao, Y., Gu, S., Yong, Z., Li, L., Wang, R., Lei, Q., & Zeng, S. (2026) · Anesthesiology and Perioperative Science · doi:10.1007/s44254-025-00140-3 · Cited by 4
“We present a comparative analysis of ChatGPT, a general-purpose LLM, and OpenBioLLM, a domain-specific model, evaluating their performance under different prompting strategies [8].”
Atkova, I., Jansson, M., Ahokangas, P., Härkönen, H., Rana, T., & Vesty, G. M. (2026) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2026.124711 · Cited by 6
Tan, H., Spratte, B. N., Deal, A. M., Heiling, H., Nazzal, E. M., Meeks, W., Fang, R., Teal, R., Vu, M. B., Bennett, A. V., et al. (2024) · Urology · doi:10.1016/j.urology.2024.04.033 · Cited by 4
The transformative role of artificial intelligence in cleft surgery
Choudhary, A. N., & Kumar, S. (2023) · Journal of Cleft Lip Palate and Craniofacial Anomalies · doi:10.4103/jclpca.jclpca_18_23 · Cited by 4
Yang, C., Chen, Y., Qian, C., Shi, F., & Guo, Y. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1461863 · Cited by 6
Artificial Intelligence and Plastic Surgery Resident Education
Luce, E. A. (2025) · Plastic & Reconstructive Surgery Global Open · doi:10.1097/gox.0000000000006924 · Cited by 4
Qahtani, A. H., Shams, S. A. A., Ahmed, B. M., Alsharari, W. A. A., Alhadab, A. H., Alanazi, W. S., Alanazi, A. S., & Albinsaad, L. S. (2025) · Journal of Advanced Trends in Medical Research · doi:10.4103/atmr.atmr_61_25 · Cited by 4
Artificial intelligence in drug discovery and clinical practice
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Artificial Intelligence Algorithms for Expert Identification in Medical Domains: A Scoping Review
Borna, S., Barry, B. A., Makarova, S., Parte, Y., Haider, C. R., Sehgal, A., Leibovich, B. C., & Forte, A. J. (2024) · European Journal of Investigation in Health Psychology and Education · doi:10.3390/ejihpe14050078 · Cited by 5
“Traditional, non-AI algorithms may fall short of fully capturing the nuances of an individual’s expertise [30,31].”
BackgroundZander, T. L., Kendall, M. A., Grimsley, E. A., Harry, S. C., Torikashvili, J. V., Parikh, R., Sujka, J. A., & Kuo, P. C. (2024) · Shock · doi:10.1097/shk.0000000000002490 · Cited by 4
Emerging trends in robotic breast surgery in the era of artificial intelligence
Li, T., & Li, C. (2025) · Plastic and Aesthetic Research · doi:10.20517/2347-9264.2024.143 · Cited by 5
Surgical treatment options for benign prostatic obstruction: beyond prostate volume
Deyirmendjian, C., Elterman, D., Chughtai, B., et al. (2021) · Current Opinion in Urology · doi:10.1097/mou.0000000000000937 · Cited by 4
“The decision-making process may be further streamlined with the integration of artificial intelligence [60].”
BackgroundDeterioration Index in Critically Injured Patients: A Feasibility Analysis
Wu, R., Smith, A. A., Brown, T., Hunt, J. P., Greiffenstein, P. P., Taghavi, S., Tatum, D. M., Jackson‐Weaver, O., & Duchesne, J. C. (2022) · Journal of Surgical Research · doi:10.1016/j.jss.2022.08.019 · Cited by 5
Metadata
- Authors
- Loftus, T. J., Tighe, P. J., Filiberto, A. C., Efron, P. A., Brakenridge, S. C., Mohr, A. M., Rashidi, P., Upchurch, G. R., & Bihorac, A.
- Affiliations
- University of Florida Health (US)
- University of Florida (US)
- ORCID
- Venue
- JAMA Surgery
- Publisher
- American Medical Association (AMA)
- Published
- 2020-02-01
- Volume
- 155
- Issue
- 2
- Pages
- 148
- ISSN
- 2168-6254 (ISSN-L)
- PMC
- PMC7286802
- OpenAlex
- W2995887567
- Cited by
- 650
- References
- 82
- Citations by year
- 2026: 141 · 2025: 147 · 2024: 120 · 2023: 91 · 2022: 65 · 2021: 54 · 2020: 28
- FWCI
- 1.97
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- JAMA Surgery (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (5r01gm113945-05)
- National Institute on Aging (K07AG066813, R01AG055337)
- National Institute of General Medical Sciences (R01 GM-113945, R01GM114290, R01GM105893, P50GM111152, T32GM008721, R01GM110240)
- National Institute of Biomedical Imaging and Bioengineering (R21EB027344)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Keywords
- artificial intelligence, surgical decision-making, clinical decision support systems, predictive analytics, algorithmic bias, model interpretability, electronic health records, informed consent, shared decision-making, postoperative care, surgical risk factors, human intuition
- MeSH terms
- Clinical Decision-Making, Artificial Intelligence, Emotions, Humans, Surgical Procedures, Operative, Time Factors, Intuition, Decision Support Systems, Clinical, Uncertainty, Electronic Health Records
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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