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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
23 of 80 cited sources checked · 3 supported · 0 flagged · 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)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe 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)
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesThe 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- 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)
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- 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)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe 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)
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- 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
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- 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)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe 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)
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- 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
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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)
Strength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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)
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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)
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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)
Strength:hedged· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✗ Based: failsProbably 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
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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.
Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✓ Based: passesProbably 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
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✓ Based: passesProbably 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.
Strength:plain· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✓ Based: passesProbably 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
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably 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
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably 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
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✗ Based: failsProbably 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
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably 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
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably 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
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes? Based: unclearIt 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably 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
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably 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
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✗ Based: failsProbably 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
Cites
(2008) · Journal of Machine Learning Research · Cited by 33,051
Vincent, J. L., Moreno, R., Takala, J., Willatts, S., De Mendonça, A., Bruining, H., Reinhart, C. K., Suter, P. M., & Thijs, L. G. (1996) · Intensive Care Medicine · doi:10.1007/bf01709751 · Cited by 11,674 · cited 1×
“24,45,57 Parametric regression prognostic scoring systems assume that relationships among input variables are linear.”
BackgroundDecision aids for people facing health treatment or screening decisions
Stacey, D., Légaré, F., Lewis, K., Barry, M. J., Bennett, C. L., Eden, K. B., Holmes-Rovner, M., Llewellyn-Thomas, H., Lyddiatt, A., Thomson, R., et al. (2017) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd001431.pub5 · Cited by 6,685
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
Bechara, A., Damasio, H., Tranel, D., & Damasio, A. R. (1997) · Science · doi:10.1126/science.275.5304.1293 · Cited by 3,547
The neural basis of economic decision-making in the Ultimatum Game.
Sanfey, A. G., Rilling, J. K., Aronson, J. A., Nystrom, L. E., & Cohen, J. D. (2003) · Science · doi:10.1126/science.1082976 · Cited by 3,375 · cited 1×
“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
Flagged first, then by citations
6 of the 82 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- Kahneman, 2013, Thinking, Fast and Slow
Cited by
Sorted by citations
Items 101–200 of 605
Re-Aiming Equity Evaluation in Clinical Decision Support
Ingraham, N. E., Jones, E., King, S., Dries, J., Phillips, M., Loftus, T. J., Evans, H. L., Melton, G. B., & Tignanelli, C. J. (2022) · Annals of Surgery · doi:10.1097/sla.0000000000005661 · Cited by 23
Self-citationShould We Rely on AI to Help Avoid Bias in Patient Selection for Major Surgery?
Binkley, C. E., Kemp, D. S., & Scully, B. B. (2022) · The AMA Journal of Ethic · doi:10.1001/amajethics.2022.773 · Cited by 23
“51,52 In other settings, standardizing clinical decisions and postoperative pathways has been shown to reduce racial disparities among surgical patients.”
BackgroundArtificial Intelligence in Modern Orthopaedics
Hui, A. T., Alvandi, L. M., Eleswarapu, A. S., & Fornari, E. D. (2022) · JBJS Reviews · doi:10.2106/jbjs.rvw.22.00086 · Cited by 27
Artificial Intelligence Applications in the Treatment of Colorectal Cancer: A Narrative Review
Yang, J., Huang, J., Han, D., & Ma, X. (2024) · Clinical Medicine Insights Oncology · doi:10.1177/11795549231220320 · Cited by 25
“102 They also have the potential to screen the appropriate populations for immunotherapy and targeted therapy.”
BackgroundSosa, B. R., Cung, M., Suhardi, V. J., Morse, K. W., Thomson, A. L., Yang, H. S., Iyer, S., & Greenblatt, M. B. (2024) · Journal of Orthopaedic Research® · doi:10.1002/jor.25782 · Cited by 25
Goh, W. W. B., Chia, K. Y. A., Cheung, M. F. K., Kee, K. M., Lwin, M. O., Schulz, P. J., Chen, M., Wu, K., Ng, S. S. M., Lui, R. N.-S., et al. (2023) · JMIR AI · doi:10.2196/50525 · Cited by 23
“…intelligence (AI) has made groundbreaking technological advancements in medical image interpretation [1]; diagnosis assistance; risk assessment for various conditions [2]; outcome prognostication [3]; and in certain areas, treatment suggestion [4] and partaking in surgical intervention [5].”
BackgroundArtificial intelligence in musculoskeletal conditions
Román-Belmonte, J. M., la Corte‐Rodríguez, H. D., & Rodriguez-Merchan, E. C. (2021) · Frontiers in Bioscience-Landmark · doi:10.52586/5027 · Cited by 23
Binkley, C. E., Reynolds, J. M., & Shuman, A. G. (2022) · New England Journal of Medicine · doi:10.1056/nejmms2207408 · Cited by 25
Abbaker, N., Minervini, F., Guttadauro, A., Solli, P., Cioffi, U., & Scarci, M. (2024) · Frontiers in Oncology · doi:10.3389/fonc.2024.1347464 · Cited by 25
Parchmann, N., Hansen, D., Orzechowski, M., & Steger, F. (2024) · GeroScience · doi:10.1007/s11357-024-01229-6 · Cited by 19
“Therefore, suggestions for patient groups, which were not included into the data, will lack in accuracy [37].”
BackgroundSurvey on medical image encryption: From classical to deep learning-based approaches
Prasad, S., & Singh, A. K. (2025) · Computers and Electrical Engineering · doi:10.1016/j.compeleceng.2024.110011 · Cited by 21
Cui, Z., Dong, Y., Yang, H., Li, K., Li, X., Ding, R., & Yin, Z. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110028 · Cited by 23
Artificial Intelligence: Singularity Approaches
TerKonda, S. P., TerKonda, A. A., Sacks, J. M., Kinney, B. M., Gurtner, G. C., Nachbar, J. M., Reddy, S., & Jeffers, L. L. C. (2023) · Plastic & Reconstructive Surgery · doi:10.1097/prs.0000000000010572 · Cited by 24
Next generation phenotyping for diagnosis and phenotype–genotype correlations in Kabuki syndrome
Hennocq, Q., Willems, M., Amiel, J., Arpin, S., Attié‐Bitach, T., Bongibault, T., Bouygues, T., Cormier‐Daire, V., Corre, P., Dieterich, K. D., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-52691-3 · Cited by 24
Shi, G., Huang, X., Li, X., Liang, F., Gao, Q., Zhang, D., Lu, J., Ji, Y., Hu, Z., Chen, Y., et al. (2025) · The Lancet Oncology · doi:10.1016/s1470-2045(25)00376-6 · Cited by 22
Salman, S. G., Phadke, R. A., Kumar, R., Momin, A. A., & Tavakkoli, A. (2026) · Spine Deformity · doi:10.1007/s43390-026-01365-3 · Cited by 24
Impact of artificial intelligence in transforming the doctor–cancer patient relationship
Heudel, P., Crochet, H., & Blay, J. (2024) · ESMO Real World Data and Digital Oncology · doi:10.1016/j.esmorw.2024.100026 · Cited by 28
New frontiers for cartilage repair, joint preservation and prevention
Brittberg, M. (2022) · Journal of Cartilage & Joint Preservation · doi:10.1016/j.jcjp.2022.100060 · Cited by 20
AI-based diagnosis and phenotype – Genotype correlations in syndromic craniosynostoses
Hennocq, Q., D, G. P. M., Collet, C., Amiel, J., Bongibault, T., Bouygues, T., Cormier‐Daire, V., Douillet, M., Dunaway, D. J., Jeelani, O., et al. (2024) · Journal of Cranio-Maxillofacial Surgery · doi:10.1016/j.jcms.2024.02.010 · Cited by 19
Lima, D. L., Kasakewitch, J. P. G., Nguyen, D. Q., Nogueira, R., Cavazzola, L. T., Heniford, B. T., & Malcher, F. (2024) · Hernia · doi:10.1007/s10029-024-03069-x · Cited by 19
“Machine learning is a subfield of AI in which a computer is able to perform tasks with no previous instructions [1, 2].”
Predicting surgical decision-making in vestibular schwannoma using tree-based machine learning
Gadot, R., Anand, A., Lovin, B. D., et al. (2022) · Neurosurgical FOCUS · doi:10.3171/2022.1.focus21708 · Cited by 19
“19 The relatively limited sample size is overcome by RF methods of internal cross-validation that decrease overfitting of the data.”
BackgroundMoazzam, Z., Alaimo, L., Endo, Y., Lima, H. A., Woldesenbet, S., Rueda, B. O., Yang, J., Ratti, F., Marques, H. P., Cauchy, F., et al. (2023) · Annals of Surgery · doi:10.1097/sla.0000000000006056 · Cited by 18
“35 In this context, OST models can identify complex nonlinear effects and interactions between covariates in an easily interpretable fashion, 36 and may be particularly useful for the .”
BackgroundExplainability Pitfalls: Beyond Dark Patterns in Explainable AI
Ehsan, U., & Riedl, M. O. (2021) · arXiv · doi:10.48550/arxiv.2109.12480 · Cited by 99
The cardiac surgeon's guide to artificial intelligence
Nedadur, R., Wang, B., & Yanagawa, B. (2021) · Current Opinion in Cardiology · doi:10.1097/hco.0000000000000888 · Cited by 19
Christou, C. D., & Tsoulfas, G. (2022) · World Journal of Gastrointestinal Oncology · doi:10.4251/wjgo.v14.i4.765 · Cited by 22
Manjula, H. M., & Anandaraj, S. P. (2021) · International Conference on Advances in Computing, Communication Control and Networking (ICAC3N) · doi:10.1109/icac3n53548.2021.9725473 · Cited by 18
“The Decision Tree algorithm will also be tested using a questionnaire that replicates the Darshana and Prashna phases [12].”
BackgroundMethodLiu, Y.-X., Zhu, C., Zhi-xiong, W., Lu, L., & Yu, Y. (2022) · Annals of Translational Medicine · doi:10.21037/atm-22-913 · Cited by 19
“Thus, current regulatory oversight is quite necessary and “double check” is recommended (34,35).”
BackgroundSchonfeld, E., Pant, A., Shah, A., et al. (2024) · Journal of Clinical Medicine · doi:10.3390/jcm13030656 · Cited by 18
“…rsID ReferenceAllele AlternateAlleles OR p Gene CodingVariant Significance rs76038188 G A 2.77567 5.3 × 10 − 6 SLC6A9 No Benign rs188509294 C T 4.31614 × − 7 SLC6A9 No × This strategy relies on standard predictive factors (demographic, surgical, radiographic) and traditional machine learning model…”
BackgroundMethodInfluentialMultiple organ failure: What you need to know
Brakenridge, S. C., Kornblith, L. Z., & Cuschieri, J. (2024) · The Journal of Trauma: Injury, Infection, and Critical Care · doi:10.1097/ta.0000000000004419 · Cited by 22
Self-citationComplications and Failure to Rescue After Inpatient Pediatric Surgery
Portuondo, J. I., Shah, S. R., Raval, M. V., Pan, I.-W. E., Zhu, H., Fallon, S. C., Harris, A. H. S., Singh, H., & Massarweh, N. N. (2020) · Annals of Surgery · doi:10.1097/sla.0000000000004463 · Cited by 18
The automation of doctors and machines: A classification for AI in medicine (ADAM framework)
Kazzazi, F. (2021) · Future Healthcare Journal · doi:10.7861/fhj.2020-0189 · Cited by 17
“27 In a study on complex breast cancer patients, the AI decision-making tool was compared with oncologists with the long-term aim of incorporating it in a fashion analogous to a multidisciplinary team (MDT) meeting to aid lone physicians.”
MethodAssociation of prematurity with complications and failure to rescue in neonatal surgery
Mehl, S. C., Portuondo, J. I., Pettit, R. W., Fallon, S. C., Wesson, D. E., Shah, S. R., Vogel, A. M., López, M. E., & Massarweh, N. N. (2021) · Journal of Pediatric Surgery · doi:10.1016/j.jpedsurg.2021.10.050 · Cited by 19
Pantelis, A. G., Panagopoulou, P. A., & Lapatsanis, D. P. (2022) · Diagnostics · doi:10.3390/diagnostics12040874 · Cited by 17
An automatic facial landmarking for children with rare diseases
Hennocq, Q., Bongibault, T., Bizière, M., et al. (2023) · American Journal of Medical Genetics Part A · doi:10.1002/ajmg.a.63126 · Cited by 17
“The applications of machine learning are increasing in healthcare (Rajkomar et al., 2019), in diverse fields such as radiology (Choy et al., 2018), dermatology (Novoa et al., 2019), and surgery (Loftus et al., 2020).”
BackgroundBenhenneda, R., Brouard, T., Dordain, F., Gadéa, F., Charousset, C., & Berhouet, J. (2023) · Orthopaedics & Traumatology Surgery & Research · doi:10.1016/j.otsr.2023.103648 · Cited by 18
Ethical and legal issues regarding artificial intelligence (AI) and management of surgical data
Ferreres, A. R. (2024) · European Journal of Surgical Oncology · doi:10.1016/j.ejso.2024.108279 · Cited by 17
Surgical video workflow analysis via visual-language learning
Li, P., Shu, X., Feng, C.-M., Feng, Y., Zuo, W., & Tang, J. (2025) · npj Health Systems · doi:10.1038/s44401-024-00010-3 · Cited by 21
Wohlgemut, J. M., Pisirir, E., Kyrimi, E., Stoner, R. S., Marsh, W., Perkins, Z., & Tai, N. R. M. (2023) · JAMIA Open · doi:10.1093/jamiaopen/ooad051 · Cited by 19
Chen, S., Jin, T., Xia, Y., & Li, X. (2023) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2023.3313593 · Cited by 16
Ji, G., Wang, K., Xia, Y., Wang, J., Wang, X., & Li, X. (2020) · Annals of Surgical Oncology · doi:10.1245/s10434-020-09374-w · Cited by 17
Computational diagnostic methods on 2D photographs: A review of the literature
Hennocq, Q., Khonsari, R. H., Benoît, V., Rio, M., & Garcelon, N. (2021) · Journal of Stomatology Oral and Maxillofacial Surgery · doi:10.1016/j.jormas.2021.04.003 · Cited by 15
Advances in artificial intelligence and deep learning systems in ICU-related acute kidney injury
Ozrazgat-Baslanti, T., Loftus, T. J., Ren, Y., Ruppert, M. M., & Bihorac, A. (2021) · Current Opinion in Critical Care · doi:10.1097/mcc.0000000000000887 · Cited by 20
“Second, the quality of model features and predictive performance must be analysed systematically, optimized and carefully monitored over time, initially in ‘silent’ analytic-only phases and subsequently in clinical implementation phases, using processes similar to those of Phases 1, 2 and 3 for standard clinical trial…”
MethodSelf-citationGamification for Machine Learning in Surgical Patient Engagement
Balch, J. A., Efron, P. A., Bihorac, A., & Loftus, T. J. (2022) · Frontiers in Surgery · doi:10.3389/fsurg.2022.896351 · Cited by 17
“This is particularly important in preventing failure to rescue—the inability to prevent a death secondary to a post-operative complication—and improving postoperative triage decisions (18, 27).”
BackgroundSelf-citationOh, M., Kim, H.-S., Jung, Y. M., Lee, H., Lee, S.-B., & Lee, S. M. (2024) · Journal of Medical Internet Research · doi:10.2196/58021 · Cited by 16
Bayesian Statistics for Surgical Decision Making
Hatton, G. E., Pedroza, C., & Kao, L. S. (2021) · Surgical Infections · doi:10.1089/sur.2020.391 · Cited by 15
Chen, J., Li, M., Han, H., Zhao, Z., & Chen, X. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3341948 · Cited by 17
“In the past few years, great efforts have been made on navigation technologies for robot-assisted MIS to overcome the limited context issues during operation, aiming to achieve real-time intraoperative navigation [2] and automatic surgery [3].”
BackgroundHow Being Outvoted by AI Teammates Impacts Human-AI Collaboration
Hu, M., Zhang, G., Chong, L., Cagan, J., & Goucher-Lambert, K. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2345980 · Cited by 18
“Additionally, humans are prone to decision fatigue, in which their decision quality decreases as the decision quantity increases (Loftus et al., 2020; Vohs et al., 2014), which may also deteriorate human-AI team performance even with good AI suggestions.”
Xiao, H., Liang, X., Li, H., Chen, X., & Li, Y. (2024) · Ecotoxicology and Environmental Safety · doi:10.1016/j.ecoenv.2024.117238 · Cited by 15
Liu, G., Li, N., Chen, L., Yang, Y., & Zhang, Y. (2021) · Frontiers in Medicine · doi:10.3389/fmed.2021.634197 · Cited by 15
“Thus, using AI tools to fastly and accurately diagnostic will help a lot (10), especially to assist in uncertainty (24) or to further developing strategies (25).”
MethodHan, Q., Dong, B., Yuan, J., Yin, F., Wang, Z., Wang, H.-N., Wang, H.-S., Tian, D., Li, W.-H., Zhang, B., et al. (2021) · Frontiers in Medicine · doi:10.3389/fmed.2021.695185 · Cited by 15
“For example, the application of the AI in the EHR has been effectively developed and it has been used to enhance the surgical decision-making (9), healthcare (10), outcome prediction (11), heart failure prediction (12), and suicide risk stratification (13).”
MethodComputer in education in the 21st century. A scientific mapping of the literature in Web of Science
Moreno-Guerrero, A.-J., Marín, J. A. M., González, M. E. P., & López-Belmonte, J. (2022) · Campus Virtuales · doi:10.54988/cv.2022.1.1019 · Cited by 16
Knowledge in Motion: A Comprehensive Review of Evidence-Based Human Kinetics
Ramalho, A. L. G., & Petrica, J. (2023) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph20116020 · Cited by 16
“By leveraging client data and current research, AI-based decision support systems can help practitioners make more accurate and efficient diagnoses and intervention decisions, which could improve outcomes while reducing diagnostic errors and costs [85,86].”
BackgroundDiagnosis to dissection: AI’s role in early detection and surgical intervention for gastric cancer
Pattilachan, T. M., Christodoulou, M., & Ross, S. B. (2024) · Journal of Robotic Surgery · doi:10.1007/s11701-024-02005-6 · Cited by 16
“This could lead to the development of best practices and standardized protocols for AI utility in gastrectomy, likely benefiting patients on a larger scale [55].”
Artificial intelligence applications in endometriosis imaging
Mittal, S., Tong, A. Y., Young, S. W., & Jha, P. (2025) · Abdominal Radiology · doi:10.1007/s00261-025-04897-w · Cited by 18
Limon, D., Satish, V., Raghavan, N., Morris, M. X., Muir, M. T., & Rajesh, A. (2025) · The American Surgeon · doi:10.1177/00031348251393934 · Cited by 19
Bridging the artificial intelligence valley of death in surgical decision-making
Balch, J. A., Upchurch, G. R., Bihorac, A., & Loftus, T. J. (2021) · Surgery · doi:10.1016/j.surg.2021.01.008 · Cited by 17
“Investigators are racing to produce artificial intelligence-enabled clinical decision-support tools, but few are implemented clinically, and even fewer change health care delivery or human behavior.(2, 3) Experienced surgeons are appropriately wary of hyped, novel solutions that are unsubstantiated by academic rigor a…”
BackgroundSelf-citationCognitive bias and neurosurgical decision making
Little, A. S., & Wu, S. J. (2022) · Journal of neurosurgery · doi:10.3171/2021.9.jns212058 · Cited by 20
Pang, J., Xiu, W., & Ma, X. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12082818 · Cited by 15
Limitations of Artificial Intelligence in Plastic Surgery
Dhawan, R., & Brooks, K. D. (2023) · Aesthetic Surgery Journal · doi:10.1093/asj/sjad357 · Cited by 14
Maraqa, N., Samargandi, R., Poichotte, A., Berhouet, J., & Benhenneda, R. (2024) · Orthopaedics & Traumatology Surgery & Research · doi:10.1016/j.otsr.2024.104080 · Cited by 14
Gao, B., & Duan, W. (2025) · Digital Health · doi:10.1177/20552076251325418 · Cited by 19
Mohamedahmed, A. Y., Zaman, S., Agrof, M., Adam, M., Husain, N., & Yassin, N. (2025) · International Journal of Surgery · doi:10.1097/js9.0000000000003067 · Cited by 15
van Eijck, S. C., Janssen, D. M., van der Steen, M. C., Delvaux, E. J. L. G., Hendriks, J. G. E., & Janssen, R. P. A. (2022) · Journal of Medical Internet Research · doi:10.2196/40504 · Cited by 13
“Clinical decision-making is a complex process that requires information from different sources such as patient history, physical examination, imaging, and laboratory investigations [33].”
Jung, S., Park, K., Ihn, K., Kim, S. J., Kim, M. S., Chae, D., & Koo, B. (2022) · Scientific Reports · doi:10.1038/s41598-022-25900-0 · Cited by 13
Alaimo, L., Moazzam, Z., Endo, Y., et al. (2023) · Annals of Surgical Oncology · doi:10.1245/s10434-023-13349-y · Cited by 13
Defining digital surgery: a SAGES white paper
The SAGES Digital Surgery Working Group, Ali, J., Yang, G., Green, C. A., Reed, B. L., Madani, A., Ponsky, T. A., Hazey, J. W., Rothenberg, S. S., Schlachta, C. M., et al. (2024) · Surgical Endoscopy · doi:10.1007/s00464-023-10551-7 · Cited by 13
Kovoor, J. G., Bacchi, S., Sharma, P., Sharma, S., Kumawat, M., Stretton, B., Gupta, A. K., Chan, W. O., Abou‐Hamden, A., & Maddern, G. J. (2024) · The Medical Journal of Australia · doi:10.5694/mja2.52225 · Cited by 14
“24 Current malpractice guidelines are unlikely to adequately consider the complexities introduced by AI, making it difficult to determine responsibility and assign blame where human and AI factors both contribute to adverse surgical outcomes.”
BackgroundGlobal trends and hotspots of ChatGPT in medical research: a bibliometric and visualized study
Liu, L., Qu, S., Zhao, H., Kong, L., Xie, Z., Jiang, Z., & Zou, P. (2024) · Frontiers in Medicine · doi:10.3389/fmed.2024.1406842 · Cited by 14
“Moreover, AI-driven health information systems facilitate data management, analysis, and sharing, promoting seamless collaboration among healthcare providers and enhancing clinical decision-making processes (62).”
BackgroundTowards multimodal graph neural networks for surgical instrument anticipation
Wagner, L., Schneider, D. N., Mayer, L., Jell, A., Müller, C., Lenz, A., Knoll, A., & Wilhelm, D. (2024) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-024-03226-8 · Cited by 16
Maleš, I., Kumrić, M., Maleš, A. H., Cvitković, I., Šantić, R., Pogorelić, Z., & Božić, J. (2025) · Diagnostics · doi:10.3390/diagnostics15070866 · Cited by 14
“Other AI applications in surgery include automated robotic procedures, real-time decision support, surgical education, and performance evaluation [6–16].”
Enhancing Surgical Education Through Artificial Intelligence in the Era of Digital Surgery
Aurello, P., Pace, M., Goglia, M., Pavone, M., Petrucciani, N., Carrano, F. M., Cicolani, A., Chiarini, L., Silecchia, G., & D’Andrea, V. (2025) · The American Surgeon · doi:10.1177/00031348251346539 · Cited by 15
Augmented intelligence in pediatric anesthesia and pediatric critical care
Görges, M., & Ansermino, J. M. (2020) · Current Opinion in Anaesthesiology · doi:10.1097/aco.0000000000000845 · Cited by 13
“The increasing recognition of the potential advantages of artificial intelligencesupported approaches to surgical decision making have yet to generate tangible results [45].”
BackgroundFacial Recognition Intensity in Disease Diagnosis Using Automatic Facial Recognition
Wu, D., Chen, S., Zhang, Y., Zhang, H., Wang, Q., Li, J., Fu, Y., Wang, S., Yang, H., Du, H., et al. (2021) · Journal of Personalized Medicine · doi:10.3390/jpm11111172 · Cited by 13
Prehabilitation for general surgery: a systematic review of randomized controlled trials
Kovoor, J. G., Nann, S. D., Barot, D. D., Garg, D., Hains, L. R., Stretton, B., Ovenden, C. D., Bacchi, S., Chan, E., Gupta, A. K., et al. (2023) · ANZ Journal of Surgery · doi:10.1111/ans.18684 · Cited by 13
Cognitive Bias and Dissonance in Surgical Practice
Richburg, C. E., Dossett, L. A., & Hughes, T. M. (2023) · Surgical Clinics of North America · doi:10.1016/j.suc.2022.11.003 · Cited by 12
Influence of a surgeon’s exposure to operating room turnover delays on patient outcomes
Pasquer, A., Cordier, Q., Lifante, J., et al. (2024) · BJS Open · doi:10.1093/bjsopen/zrae117 · Cited by 12
“The current accessibility of large databases and powerful artificial intelligence modelling approaches now makes this goal achievable 28 .”
BackgroundAI aiding perioperative anaesthetic management: on the way but not ready yet
Lee, H., Chen, Q., & Ma, D. (2024) · Anesthesiology and Perioperative Science · doi:10.1007/s44254-024-00082-2 · Cited by 12
“However, decision-making strategies derived from big data studies improving perioperative outcomes, such as reduced postoperative complications [8], are not available yet and calling more studies into this area are urgent.”
Artificial Intelligence in Breast Reconstruction: A Narrative Review
Rugină, A., Ungureanu, A., Giuglea, C., & Marinescu, S. A. (2025) · Medicina · doi:10.3390/medicina61030440 · Cited by 17
“Also, it could refine choices such as the donor site for flaps, the recipient vessel, expander or implant selection, and surgical techniques [46].”
Li, C., Li, Y., Liu, R., Wang, G., Lv, J., Jin, Y., Si, W., & Heng, P. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3342222 · Cited by 11
“Semantic segmentation of surgical instruments is the prerequisite of tracking instruments during surgery for a lot of downstream applications, e.g., skill assessment [3], [4], instrument control [5], [6], and decision-making [7].”
BackgroundGiaccone, P., D’Antoni, F., Russo, F., Ambrosio, L., Papalia, G. F., d’Angelis, O., Vadalà, G., Comelli, A., Vollero, L., Merone, M., et al. (2025) · BMC Musculoskeletal Disorders · doi:10.1186/s12891-025-08356-x · Cited by 14
Anesthesia transformed: AI pioneering a new era in perioperative medicine
Xu, H., Fu, C., Zhao, W., Yan, Z., Song, S., Ji, F., & Liu, H. (2025) · Anesthesiology and Perioperative Science · doi:10.1007/s44254-025-00091-9 · Cited by 12
“The intricate and dynamic nature of managing patients before, during, and after surgery, characterized by numerous variables and ever-changing conditions, renders it an optimal domain for AI integration [2].”
Does Intraoperative Artificial Intelligence Decision Support Pose Ethical Issues?
Binkley, C. E., & Green, B. P. (2021) · JAMA Surgery · doi:10.1001/jamasurg.2021.2055 · Cited by 15
Rana, H., Umer, M., Hassan, U., Asgher, U., Jamal, F., Naseem, A., & Ehsan, N. (2023) · IISE Transactions on Healthcare Systems Engineering · doi:10.1080/24725579.2023.2215247 · Cited by 12
“:3) enech et al., 2013), and is governed by “ hypothetical rational thinking ” of both the physician and the patient, clinical judgment of the physician, patients ’ pathological reports, and other facts reported by the patient (Loftus et al., 2020).”
BackgroundAI-based diagnosis in mandibulofacial dysostosis with microcephaly using external ear shapes
Hennocq, Q., Bongibault, T., Marlin, S., et al. (2023) · Frontiers in Pediatrics · doi:10.3389/fped.2023.1171277 · Cited by 11
Zheng, H., Yang, L., Hu, J., & Yang, Y. (2024) · Health Expectations · doi:10.1111/hex.14019 · Cited by 16
“According to dual ‐ process theory 53 and Tyler's study, 54 when individuals face time constraints and uncertainty, their decision making may be influenced by heuristics or cognitive shortcuts.”
MethodArtificial Intelligence: Technologies, Applications, and Policy Perspectives. Insights from Portugal
Sousa, M. J., Mas, F. D., de Barros, G. O., & Tavares, N. (2022) · EAI/Springer Innovations in Communication and Computing · doi:10.1007/978-3-030-82763-2_6 · Cited by 11
Advancements of Artificial Intelligence in Liver-Associated Diseases and Surgery
Taha, A., Ochs, V., Kayhan, L. N., Enodien, B., Frey, D. M., Krähenbühl, L., & Taha‐Mehlitz, S. (2022) · Medicina · doi:10.3390/medicina58040459 · Cited by 15
“Shared decision-making procedures that make use of AI provide for better patient compliance and satisfaction [14].”
BackgroundArtificial intelligence in nephrology: predicting CKD progression and personalizing treatment
Yuan, S., Guo, L., & Xu, F. (2025) · International Urology and Nephrology · doi:10.1007/s11255-025-04878-4 · Cited by 14
Progress in clinical application of artificial intelligence in orthopedics
Wang, Y., Li, R., & Zheng, P. (2022) · Digital Medicine · doi:10.4103/digm.digm_10_21 · Cited by 10
Marcaccini, G., Seth, I., Novo, J., McClure, V., Sacks, B. K., Lim, K., Ng, S., Cuomo, R., & Rozen, W. M. (2025) · Technologies · doi:10.3390/technologies13040142 · Cited by 11
Risk stratification tools in prostate cancer, where do we stand?
Clinckaert, A., Devos, G., Roussel, E., & Joniau, S. (2021) · Translational Andrology and Urology · doi:10.21037/tau-20-1211 · Cited by 10
“Data standardization, model interpretability, implementation and monitoring as well as ethical issues must be carefully considered (23).”
BackgroundInfluentialBAIT: A New Medical Decision Support Technology Based on Discrete Choice Theory
ten Broeke, A., Hulscher, J. B. F., Heyning, N., Kooi, E. M. W., & Chorus, C. G. (2021) · Medical Decision Making · doi:10.1177/0272989x211001320 · Cited by 14
Lan, L., Chen, F., Luo, J., Li, M., Hao, X., Hu, Y., Yin, J. Y., Zhu, T., & Zhou, X. (2022) · Digital Health · doi:10.1177/20552076221110543 · Cited by 11
“24–26 In this context, machine learning techniques are increasingly being used in various clinical fields(27) and artificial intelligence should be used to augment operative management.(28,29) In this study, we developed a highly discriminative machine learning model to predict ICU admission using the easily available…”
MethodRana, H., Umer, M., Hassan, U., et al. (2022) · Heliyon · doi:10.1016/j.heliyon.2022.e10339 · Cited by 10
A new machine learning-based prediction model for subtype diagnosis in primary aldosteronism
Shi, S., Tian, Y., Ren, Y., et al. (2022) · Frontiers in Endocrinology · doi:10.3389/fendo.2022.1005934 · Cited by 10
“Meanwhile, supervised machine learning technology has been used widely and is gaining recognition in medical research since it can automatically formulate computational models by processing the variables with complex relationships, and therefore, frequently presents optimal results compared with traditional methods (8…”
ResultParums, D. V. (2021) · Medical Science Monitor · doi:10.12659/msm.933675 · Cited by 12
Wang, H., Ding, S., Yang, S., Liu, C., Yu, S., & Zheng, X. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3108457 · Cited by 9
Artificial Intelligence in Surgery, Surgical Subspecialties, and Related Disciplines
Lee, R. Y., Ziehm, A. I., Ullrich, L. A., & Stawicki, S. P. (2023) · Artificial intelligence · doi:10.5772/intechopen.112691 · Cited by 10
Artificial Intelligence and Postoperative Monitoring in Plastic Surgery
Stein, M. J., & Rohrich, R. J. (2023) · Plastic Surgery · doi:10.1177/22925503231210873 · Cited by 10
Spiller, M., Esmaeili, N., Sühn, T., et al. (2024) · Diagnostics · doi:10.3390/diagnostics14151698 · Cited by 9
“In the future, AI-based systems could play a significant role in supporting surgical procedures and decision making [37].”
Background
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.
A language model drew the claims from the paper on 2026-09-28, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-09-28 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
