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.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
23 of 80 cited sources checked · 3 supported · 0 flagged · accuracy not yet measured
- Premisecitation
Traditional clinical decision support tools such as the NSQIP Surgical Risk Calculator can reduce variability and mitigate risks.
“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”
Quote in the paperSubstantial claim1 of 6 sources checked- cites [3] source
- cites [4] source
- cites [5] source
- cites [6] source
- cites [7] source
- cites [8] source
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)
Experimental — accuracy not yet measured
- Premisecitation
Parametric regression prognostic scoring systems assume that relationships among input variables are linear.
“Parametric regression prognostic scoring systems assume that relationships among input variables are linear.22,29”
Quote in the paperNot a substantial claim1 of 2 sources checked- cites [22] source
- cites [29] source
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)
Experimental — accuracy not yet measured
- Premisecitation
Even the most successful and widely used static variable cutoff values do not accurately represent individual patient pathophysiology, as reflected by their suboptimal accuracy.
“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”
Quote in the paperSubstantial claim1 of 3 sources checked- cites [34] source
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)
Experimental — accuracy not yet measured - cites [43] source
- cites [56] source
- Premisecitation
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.
“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”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites [42] source
- Premisecitation
Clinical adoption of traditional clinical decision support tools is hindered by time-consuming manual data acquisition and entry requirements.
“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”
Quote in the paperSubstantial claim1 of 6 sources checked- cites [3] source
- cites [4] source
- cites [5] source
- cites [6] source
- cites [7] source
- cites [8] source
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)
Experimental — accuracy not yet measured
- Premisecitation
When relationships among input variables are nonlinear, a parametric regression prognostic scoring system performs similarly to a coin toss.
“When the relationships are nonlinear, the scoring system is similar to a coin toss.11”
Quote in the paperSubstantial claim1 of 1 sources checked- cites [11] source
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)
Experimental — accuracy not yet measured
- Premisecitation
Machine learning has been used to accurately predict sepsis, in-hospital mortality, and acute kidney injury using intraoperative time-series data.
“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”
Quote in the paperNot a substantial claim1 of 4 sources checked- cites [9] source
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)
Experimental — accuracy not yet measured - cites [21] source
- cites [27] source
- cites [32] source
- Premisecitation
Clinical adoption of traditional clinical decision support tools is hindered by suboptimal accuracy.
“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”
Quote in the paperSubstantial claim1 of 6 sources checked- cites [3] source
- cites [4] source
- cites [5] source
- cites [6] source
- cites [7] source
- cites [8] source
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)
Experimental — accuracy not yet measured
- Premisecitation
Academic intensivists make approximately 56 patient care and resource use decisions per day.
“Academic intensivists make approximately 56 patient care and resource use decisions per day.36”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites [36] source
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)
Experimental — accuracy not yet measured
- Premisecitation
Traditional clinical decision-support systems require time-consuming manual data acquisition and entry, which impairs their adoption.
“Traditional clinical decision-support systems require time-consuming manual data acquisition and entry, which impairs their adoption.8,33”
Quote in the paperSubstantial claim1 of 2 sources checked- cites [8] source
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)
Experimental — accuracy not yet measured - cites [33] source
- Premisecitation
In an assessment of medical student and resident ICU patient presentations, potentially important data were omitted from all 157 presentations.
“In an assessment of medical student and resident intensive care unit (ICU) patient presentations, potentially important data were omitted from 157 of 157 presentations.10”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites [10] source
- Premisecitation
In a prospective study, an algorithm predicted postoperative complications with greater accuracy than physicians.
“In a prospective study, the algorithm predicted postoperative complications with greater accuracy than physicians.17”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites [17] source
The Methods describe a prospective pilot study using a simulated workflow. The Results report that MySurgeryRisk was more accurate than physicians’ initial assessments for the postoperative complications tested except cardiovascular complications. The claim omits that exception and the simulated-workflow setting. (LLM, conf=0.88)
Experimental — accuracy not yet measured
- Premisecitation
A model trained on Framingham heart study data may reflect racial and ethnic bias because associations between cardiovascular risk factors and events differ by race and ethnicity.
“For example, the Framingham heart study primarily included white participants. A model trained on this data may reflect racial and ethnic bias because associations between cardiovascular risk factors and events differ by race and ethnicity.25”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites [25] source
- Premisecitation
Intuition can identify patients with life-threatening conditions that would be underappreciated by traditional clinical parameters alone.
“Intuition can also identify patients with life-threatening conditions that would be underappreciated by traditional clinical parameters alone.47,48”
Quote in the paperNot a substantial claim1 of 2 sources checked- cites [47] source
The Results (Diagnostic added value of gut feeling) show that clinicians’ gut feeling flagged serious infections in children whose clinical assessment suggested a non-serious illness. The Discussion (Implications for practice) says the data are restricted to children in primary care and cannot necessarily be extrapolated to adults or other settings. The broader claim about patients with life-threatening conditions therefore exceeds what this study establishes. (LLM, conf=0.86)
Experimental — accuracy not yet measured - cites [48] source
- Premisecitation
Surgeons report that lapses in judgment are the most common cause of their major errors.
“Surgeons report that lapses in judgment are the most common cause of their major errors.”
- Also stated in: § CONCLUSIONS AND RELEVANCE · plain
“Surgeons report that lapses in judgment are the most common cause of their major errors.2”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [2] source
- Also stated in: § CONCLUSIONS AND RELEVANCE · plain
- Premisecitation
Diagnostic and judgment errors are the second most common cause of preventable harm among surgical patients.
“Diagnostic and judgment errors are the second most common cause of preventable harm incurred by surgical patients.1”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [1] source
- Premisecitation
Human disease is too broad and complex to be explained and interpreted by rules.
“Herein lies the strength of artificial intelligence for predictive analytics in medicine: human disease is simply too broad and complex to be explained and interpreted by rules.59,60”
Quote in the paperNot a substantial claim0 of 2 sources checked - Interpretationnone given
Deep learning provides only outcome probabilities that loosely correspond to specific decisions and actions.
“Despite their utility for predictive analytics, deep learning only provides outcome probabilities that loosely correspond to specific decisions and actions.”
Quote in the paperNot a substantial claim - Premisecitation
Emotions surrounding an acute surgical condition may create a sense of urgency and pressure on surgeons to perform futile operations.
“Emotions surrounding an acute surgical condition may also create a sense of urgency and pressure on surgeons to perform futile operations.37”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [37] source
- Interpretationreasoning
Artificial intelligence models fed with livestreaming intraoperative and electronic health record data may overcome the weaknesses of traditional approaches and augment surgical decision-making across preoperative, intraoperative, and postoperative phases of care.
“The weaknesses of traditional approaches may be overcome by artificial intelligence models fed with livestreaming intraoperative and EHR data to augment surgical decision-making through hpreoperative, intraoperative, and postoperative phases of care (Figure 2).”
Quote in the paperSubstantial claim - Interpretationreasoning
Decision aids do not consider individual patient physiology and risk factors because they apply to heterogeneous patient populations with one common clinical presentation or choice.
“However, because decision aids apply to heterogeneous patient populations with 1 common clinical presentation or choice, they do not consider individual patient physiology and risk factors.”
Quote in the paperNot a substantial claim - Premisecitation
A meta-analysis found that the optimal postoperative day 3 C-reactive protein cutoff value for anastomotic leak after colorectal surgery was 172 mg/L.
“A meta-analysis43 found that the optimal postoperative day 3 CRP cutoff value was 172 mg/L (to convert to nanomoles per liter, multiply by 9.524).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [43] source
- Premisecitation
Each step in the hypothetical-deductive decision-making model introduces variability and opportunities for error.
“Each step introduces variability and opportunities for error.40”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [40] source
- Premisecitation
Elevated serum C-reactive protein levels are associated with anastomotic leak after colorectal surgery.
“For example, elevated serum levels of C-reactive protein (CRP) are associated with anastomotic leak after colorectal surgery.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [43] source
- Interpretationreasoning
Surgical decision-making is often hindered by uncertainty due to missing or incomplete data.
“Surgical decision-making is often hindered by uncertainty owing to missing or incomplete data.”
Quote in the paperSubstantial claim - Interpretationnone given
When algorithms are trained on data sets influenced by bias, algorithm outputs will likely reflect similar bias.
“When algorithms are trained on data sets that are influenced by bias, algorithm outputs will likely reflect similar bias.”
Quote in the paperNot a substantial claim - Premisecitation
A model designed to augment judicial decision-making by predicting the likelihood of crime recidivism demonstrated a predilection for racial and ethnic discrimination.
“In 1 prominent example, a model designed to augment judicial decision-making by predicting the likelihood of crime recidivism demonstrated predilection for racial/ethnic discrimination.75”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [75]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Interpretationreasoning
When data used to train an algorithm are predominantly derived from patient populations with different demographics than the patient for whom the algorithm is applied, accuracy may suffer.
“When data used to train an algorithm are predominantly derived from patient populations with different demographics than the patient for whom the algorithm is applied, accuracy may suffer.”
Quote in the paperNot a substantial claim - Premisecitation
Human intuition seems to arise from dopaminergic limbic system neurons that modify their connections when a pattern or situation leads to reward or penalty.
“Human intuition seems to arise from dopaminergic limbic system neurons that modify their connections with one another when a certain pattern or situation leads to a reward or penalty such as pleasure or pain.69,70”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [69]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [70] source
- Premisecitation
Within 6 years of the Health Information Technology for Economic and Clinical Health Act of 2009, more than four of five US hospitals adopted electronic health records.
“Within 6 years, more than 4 of 5 US hospitals adopted EHRs.66”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [66] source
- Interpretationreasoning
Accountability for errors poses a challenge for artificial intelligence in surgical decision-making.
“Accountability for errors poses another challenge.”
Quote in the paperNot a substantial claim - Interpretationreasoning
Automated artificial intelligence models fed with livestreaming electronic health record data can address the weaknesses of traditional decision-support systems.
“Automated artificial intelligence models fed with livestreaming EHR data can address these weaknesses.”
Quote in the paperNot a substantial claim - Premisecitation
Efforts to use manual data entry to feed machine learning models for surgical risk prediction on mobile devices have been successful.
“However, efforts to use manual data entry to feed machine learning models for surgical risk prediction on mobile devices have been successful.14”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [14] source
- Interpretationreasoning
Because electronic health records are continuously updated as patient data become available, artificial intelligence models can provide real-time predictions and recommendations.
“Because EHRs are continuously updated as patient data become available, artificial intelligence models can provide real-time predictions and recommendations.”
Quote in the paperSubstantial claim
- May combine two claims. 21
- Classified as probably not a substantial claim. 18
May combine two claims.
PremisecitationSurgical diseases may evoke fear and anger, which influence perceptions of risks and benefits.
“Surgical diseases may evoke fear and anger, which influence perceptions of risks and benefits.23,51”
Quote in the paperNot a substantial claim1 of 2 sources checked- cites [23] source
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)
Experimental — accuracy not yet measured - cites [51] source
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationAcute and chronic sleep deprivation occurs in as many as two-thirds of all acute care surgeons taking in-house call.
“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”
Quote in the paperNot a substantial claim1 of 2 sources checked- cites [52] source
- cites [53] source
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)
Experimental — accuracy not yet measured
May combine two claims.
PremisecitationSuper Learner ranks candidate algorithms by performance and applies an optimal weight to each, creating ensemble algorithms that can accurately predict transfusion requirements and mortality among trauma patients.
“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”
Quote in the paperNot a substantial claim4 of 4 sources checked- cites [20] source
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)
Experimental — accuracy not yet measured - cites [28] source
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)
Experimental — accuracy not yet measured - cites [38] source
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)
Experimental — accuracy not yet measured - cites [39] source
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)
Experimental — accuracy not yet measured
May combine two claims.
PremisecitationParametric regression equations fail to represent complex, nonlinear associations among input variables, further limiting the accuracy of traditional multivariable regression models.
“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”
Quote in the paperSubstantial claim1 of 2 sources checked- cites [22] source
- cites [29] source
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)
Experimental — accuracy not yet measured
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationApproximately 153 billion GB of data were generated in 2013, with projected growth of 48% per year.
“Approximately 153 billion GB of data were generated in 2013, with projected growth of 48% per year.67”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites [67] source
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)
Experimental — accuracy not yet measured
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
InterpretationcitationProviding automated model outputs to mobile devices would require robust quality filters, a public key infrastructure, and encryption that can only be deciphered by the intended receiver.
“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”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites [68] source
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)
Experimental — accuracy not yet measured
May combine two claims.
InterpretationreasoningSurgical decision-making is highly variable and poorly suited to correct diagnostic and judgment errors.
“Surgical decision-making is dominated by hypothetical deductive reasoning and individual judgment, which are highly variable and ill-suited to remedy these errors.”
Quote in the paperSubstantial claim- Rests on an assumption: Surgical decision-making is dominated by hypothetical-deductive reasoning, individual judgment, and heuristics.
May combine two claims.
PremisecitationHigh decision-making volume begets decision fatigue, manifesting as procrastination, less persistence, decreased physical stamina, and lower quality and quantity of mathematic calculations.
“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”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [49] source
May combine two claims.
PremisecitationThe NSQIP Surgical Risk Calculator requires manual entry of input variables and has suboptimal predictive accuracy, especially for nonelective operations.
“However, input variables must be entered manually, and its predictive accuracy is suboptimal, especially for nonelective operations, representing opportunities for improvement.4–7”
Quote in the paperSubstantial claim0 of 4 sources checkedMay combine two claims.
PremisecitationThe negative predictive value of the day 3 C-reactive protein cutoff was 97%, and the positive predictive value was 21%.
“The negative predictive value was 97%, such that a low value usually indicates no leak, but the positive predictive value was 21%.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [43] source
May combine two claims.
PremisecitationReinforcement learning has been used to recommend optimal fluid resuscitation and vasopressor administration strategies for patients with sepsis, and ninety-day mortality was lowest when clinician care was concordant with model recommendations.
“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.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [31] source
May combine two claims.
PremisecitationMost diseases are not driven by a single physiologic parameter, so prognostic scoring systems often incorporate multiple parameters.
“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”
Quote in the paperNot a substantial claim0 of 3 sources checkedProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).
InterpretationreasoningFor 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.
“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.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationUnderstanding and incorporating patient values is essential to an effective shared decision-making process.
“Understanding and incorporating these values is essential to an effective shared decision-making process.50”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [50] source
May combine two claims.
PremisecitationIn a systematic review 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.
“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.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [44]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
May combine two claims.
PremisecitationReinforcement learning has been used to recommend basal and bolus insulin administration for virtual type 1 diabetics, and the algorithm performed as well as standard intermittent self-monitoring and continuous glucose monitoring methods but with fewer episodes of hypoglycemia.
“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.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [46]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
May combine two claims.
PremisecitationIn 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.
“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”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [30] source
Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).
InterpretationreasoningBecause patient values are individualized, no criterion standard of optimal decision-making can be created.
“Patient values are individualized by nature, precluding the creation of a criterion standard of optimal decision-making.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).
InterpretationreasoningSimilar reinforcement learning methods could be applied to augment the decision to operate.
“Similar methods could be applied to augment the decision to operate.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing). May combine two claims.
InterpretationcitationInitial prospective implementation of artificial intelligence decision-support tools 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.
“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”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [74]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
May combine two claims.
PremisecitationThe My Surgery Risk platform uses electronic health record data for 285 variables to predict 8 postoperative complications with an area under the curve of 0.82-0.94 and to predict mortality at 1, 3, 6, 12, and 24 months with an area under the curve of 0.77-0.83.
“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”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [15] source
Probably not a substantial claim: may not pass S2 (checkable).
Interpretationnone givenIf 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.
“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.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Interpretationnone givenTo produce models that may be integrated with any electronic health record in any setting, data must be standardized.
“To produce models that may be integrated with any EHR in any setting, data must be standardized.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S4 (based).
Interpretationnone givenArtificial 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 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.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Interpretationnone givenArtificial intelligence models trained on erroneous or misrepresentative data are likely to obscure the truth.
“Artificial intelligence models trained on erroneous or misrepresentative data are likely to obscure the truth.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable).
InterpretationreasoningPrior to clinical implementation, machine and deep learning models must be rigorously analyzed in a retrospective fashion and externally validated to ensure generalizability.
“Prior to clinical implementation, machine and deep learning models must be rigorously analyzed in a retrospective fashion and externally validated to ensure generalizability.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable). May combine two claims.
InterpretationreasoningSuccessful 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.
“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.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.
Interpretationnone givenThe justice system is well-equipped to address scenarios in which an individual clinician is responsible for an errant decision, but it may prove difficult to assign blame to a computer program and its developers.
“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.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationreasoningSurgical decision-making is impaired by time constraints, uncertainty, complexity, decision fatigue, hypothetical-deductive reasoning, and bias, leading to preventable harm.
“Surgical decision-making is impaired by time constraints, uncertainty, complexity, decision fatigue, hypothetical-deductive reasoning, and bias, leading to preventable harm.”
Quote in the paperNot a substantial claim- Rests on an assumption: Surgical decision-making is dominated by hypothetical-deductive reasoning, individual judgment, and heuristics.
Probably not a substantial claim: may not pass S4 (based). May combine two claims.
Interpretationnone givenTraditional decision-support systems are compromised by time-consuming manual data entry and suboptimal accuracy.
“Traditional decision-support systems are compromised by time-consuming manual data entry and suboptimal accuracy.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Interpretationnone givenBecause studies with positive results are more likely to be submitted and published, artificial intelligence literature may be overly optimistic.
“Because studies with positive results are more likely to be submitted and published, artificial intelligence literature may be overly optimistic.”
Quote in the paperNot a substantial claim- Rests on an assumption: Studies with positive results are more likely to be submitted and published.
Probably not a substantial claim: may not pass S2 (checkable), S4 (based). May combine two claims.
InterpretationreasoningProperly 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.
“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.”
Quote in the paperNot a substantial claim
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?
MySurgeryRisk demonstrated that ML-based prediction of postoperative complications could complement physician judgment.
“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]
These approaches did not establish a structured benchmark that translates subjective physician judgment into reproducible, imbalance-aware performance metrics.
“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]
The Observations section discusses subjective surgical judgment and the limitations of traditional predictive tools, but it does not assess whether these approaches established a structured benchmark. A search for benchmark, reproducibility, and imbalance-aware performance metrics found no passage addressing that specific claim. (LLM, conf=0.72)
Experimental — accuracy not yet measured
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“10 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.”
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“To capitalize on this phenomenon, Super Learner ranks a set of candidate algorithms 20,28,38,39 by their performance and applies an optimal weight to each, creating ensemble algorithms that can accurately predict transfusion requirements and mortality among trauma patients.”
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“34,43,56 Parametric regression equations also fail to represent the complex, nonlinear associations among input variables, further limiting the accuracy of traditional multivariable regression models.”
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“11 To facilitate clinical adoption, prognostic scoring systems have been implemented as online risk calculators.”
MethodDisagreement Between Clinicians and Score in Decision-Making Capacity of Critically Ill Patients*
Bertrand, P.-M., Pereira, B., Adda, M., Timsit, J., Wolff, M., Hilbert, G., Gruson, D., Garrouste-Orgeas, M., Argaud, L., Constantin, J., et al. (2018) · Critical Care Medicine · doi:10.1097/ccm.0000000000003550 · Cited by 23 · see citing works
Effects of emotion on medical decisions involving tradeoffs.
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The effects of deep network topology on mortality prediction
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Singh (2014) · British journal of surgery · doi:10.1002/bjs.2014.101.issue-4 · Cited by 6 · see citing works
Strate (2005) · doi:10.1111/ajg.2005.100.issue-8
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
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Expanding Explainability: Towards Social Transparency in AI systems
Ehsan, U., Liao, Q. V., Muller, M., Riedl, M. O., & Weisz, J. D. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445188 · Cited by 538 · see citing works
“being employed in high stakes decision-making scenarios in industries such as healthcare [63], finance [76], college admissions [79], hiring [19], and criminal justice [37], the need for explainability becomes paramount.”
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Artificial intelligence in surgery
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“The advantages of AI in hypothetico-deductive surgical decision-making are expertly overviewed by Loftus et al. 25 .”
How human–AI feedback loops alter human perceptual, emotional and social judgements
Glickman, M., & Sharot, T. (2024) · Nature Human Behaviour · doi:10.1038/s41562-024-02077-2 · Cited by 289 · see citing works
Artificial Intelligence in Dermatology Image Analysis: Current Developments and Future Trends
Li, Z., Koban, K. C., Schenck, T. L., Giunta, R. E., Li, Q., & Sun, Y. (2022) · Journal of Clinical Medicine · doi:10.3390/jcm11226826 · Cited by 279 · see citing works
Harnessing the Power of Clinical Decision Support Systems: Challenges and Opportunities
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“15 While CDSS have demonstrated significant potential, their adoption and optimisation remain a challenge.”
Gama, F., Tyskbo, D., Nygren, J., Barlow, J., Reed, J., & Svedberg, P. (2022) · Journal of Medical Internet Research · doi:10.2196/32215 · Cited by 216 · see citing works
“Ethics indicates medical moral principles for beneficence and nonmaleficence at a population level, reflecting the need for formal regulation and consideration of the health equality impact of new technologies at the population level [26,29].”
BackgroundInfluentialArtificial Intelligence Applications in Health Care Practice: Scoping Review
Sharma, M., Savage, C., Nair, M., Larsson, I., Svedberg, P., & Nygren, J. M. (2022) · Journal of Medical Internet Research · doi:10.2196/40238 · Cited by 198 · see citing works
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Mas, F. D., Massaro, M., Rippa, P., & Secundo, G. (2023) · Technovation · doi:10.1016/j.technovation.2023.102716 · Cited by 195 · see citing works
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The impact of surgical simulation and training technologies on general surgery education
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“A case study from the Mayo Clinic demonstrated the efficacy of AI-enhanced simulations in refining surgical techniques and decision-making processes [134].”
Humans inherit artificial intelligence biases
Vicente, L., & Matute, H. (2023) · Scientific Reports · doi:10.1038/s41598-023-42384-8 · Cited by 158 · see citing works
Ethics of Artificial Intelligence in Medicine and Ophthalmology
Abdullah, Y. I., Schuman, J. S., Shabsigh, R., Caplan, A. L., & Al‐Aswad, L. A. (2021) · Asia-Pacific Journal of Ophthalmology · doi:10.1097/apo.0000000000000397 · Cited by 159 · see citing works
“Bias in machine judgment is thought to stem from human bias.(56,58) When biases are more subtle, they will be harder to anticipate and more damaging to resolve.”
BackgroundBhuyan, S. S., Sateesh, V., Mukul, N., Galvankar, A., Mahmood, A., Nauman, M., Rai, A., Bordoloi, K., Basu, U., & Samuel, J. (2025) · Journal of Medical Systems · doi:10.1007/s10916-024-02136-1 · Cited by 145 · see citing works
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“35 AI-powered decision-making tools show tremendous potential in improving surgical outcomes by augmenting these processes via automated data acquisition, predictive analytics, and appropriate integration with human surgical intuition.”
BackgroundThe Who in XAI: How AI Background Shapes Perceptions of AI Explanations
Ehsan, U., Passi, S., Liao, Q. V., Chan, L., Lee, I.-H., Muller, M., & Riedl, M. O. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642474 · Cited by 123 · see citing works
“Quantitatively, we share user As AI-driven systems increasingly power high-stakes decision-perceptions along fve dimensions.”
InfluentialEisenhofer, G., Durán, C., Cannistraci, C. V., Peitzsch, M., Williams, T. A., Riester, A., Burrello, J., Buffolo, F., Prejbisz, A., Beuschlein, F., et al. (2020) · JAMA Network Open · doi:10.1001/jamanetworkopen.2020.16209 · Cited by 116 · see citing works
Technical and ethical considerations in telesurgery
Patel, V., Saikali, S., Moschovas, M. C., Patel, E., Satava, R. M., Prokar Dasgupta, A., Döhler, M., Collins, J. W., Albala, D. M., & Marescaux, J. (2024) · Journal of Robotic Surgery · doi:10.1007/s11701-023-01797-3 · Cited by 120 · see citing works
“It’s use in surgical data science has been explored and studied in various applications [57, 58] AI has the potential of analyzing surgical data in real-time and providing the surgeon with valuable decision support [59].”
Pressman, S. M., Borna, S., Gomez-Cabello, C. A., Haider, S. A., Haider, C. R., & Forte, A. J. (2024) · Healthcare · doi:10.3390/healthcare12080825 · Cited by 111 · see citing works
“Nevertheless, the emergence of these models has stirred considerable debate within the academic and medical community, primarily due to concerns over inherent biases [13–16], patient data privacy [17,18], and the propagation of misinformation [19].”
BackgroundApplications of artificial intelligence in orthopaedic surgery
Farhadi, F., Barnes, M. R., Sugito, H. R., Sin, J. M., Henderson, E. R., & Levy, J. (2022) · Frontiers in Medical Technology · doi:10.3389/fmedt.2022.995526 · Cited by 107 · see citing works
Artificial Intelligence and Surgery: Ethical Dilemmas and Open Issues
Cobianchi, L., Verde, J. M., Loftus, T. J., Piccolo, D., Mas, F. D., Mascagni, P., Vazquez, A. G., Ansaloni, L., Marseglia, G. R., Massaro, M., et al. (2022) · Journal of the American College of Surgeons · doi:10.1097/xcs.0000000000000242 · Cited by 108 · see citing works
Self-citationArtificial intelligence-enabled decision support in nephrology
Loftus, T. J., Shickel, B., Ozrazgat‐Baslanti, T., Ren, Y., Glicksberg, B. S., Cao, J., Singh, K., Chan, L., Nadkarni, G. N., & Bihorac, A. (2022) · Nature Reviews Nephrology · doi:10.1038/s41581-022-00562-3 · Cited by 109 · see citing works
“Despite great potential for AI-enabled decision support systems to augment care, few such systems have been implemented clinically 94,95 .”
BackgroundSelf-citationAI in the hands of imperfect users
Kostick, K. M., & Gerke, S. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00737-z · Cited by 109 · see citing works
Adhikari, L., Ozrazgat-Baslanti, T., Ruppert, M., Madushani, R. W. M. A., Paliwal, S., Hashemighouchani, H., Zheng, F., Tao, M., Lopes, J. M., Li, X., et al. (2019) · PLOS ONE · doi:10.1371/journal.pone.0214904 · Cited by 98 · see citing works
Self-citationA Review on the Current Applications of Artificial Intelligence in the Operating Room
Birkhoff, D. C., van Dalen, A. S. H. M., & Schijven, M. P. (2021) · Surgical Innovation · doi:10.1177/1553350621996961 · Cited by 96 · see citing works
“” The term AI is often used to describe the study of algorithms that enables machines to reason and perform cognitive functions such as learning, problem-solving, and decision-making.(10,11) Recently, AI has made its introduction into medicine and, even more recently, into the OR.”
BackgroundIgaki, T., Kitaguchi, D., Matsuzaki, H., Nakajima, K., Kojima, S., Hasegawa, H., Takeshita, N., Kinugasa, Y., & Ito, M. (2023) · JAMA Surgery · doi:10.1001/jamasurg.2023.1131 · Cited by 88 · see citing works
Charting the Sociotechnical Gap in Explainable AI: A Framework to Address the Gap in XAI
Ehsan, U., Saha, K., Choudhury, M. D., & Riedl, M. O. (2023) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3579467 · Cited by 92 · see citing works
“Given their sociotechnical nature and the increasing trend of being deployed in high-stakes domains like healthcare [29, 76, 78, 96], finance [102, 117], criminal justice [68, 80, 126], we need to account for both social and technical factors to mitigate biases and promote accountability in XAI [142].”
BackgroundArtificial Intelligence in Medicine and Dentistry
Vodanović, M., Subašić, M., Milošević, D., & Pavičin, I. S. (2023) · Acta Stomatologica Croatica · doi:10.15644/asc57/1/8 · Cited by 88 · see citing works
“To uključuje: · medicinsko snimanje: UI može pomoći radiolozima i ostalim medicinskim stručnjacima u analizi slika kao što su CT i RTG snimke kako bi uspješnije ustanovili i potvrdili dijagnozu (19 – 22) · dijagnoza i liječenje: UI može procesuirati veliku količinu pacijentovih podataka kako bi se mogla što preciznije…”
Mess, S. A., Mackey, A., & Yarowsky, D. (2025) · Plastic & Reconstructive Surgery Global Open · doi:10.1097/gox.0000000000006450 · Cited by 82 · see citing works
Future direction of total neoadjuvant therapy for locally advanced rectal cancer
Kagawa, Y., Smith, J. J., Fokas, E., Watanabe, J., Cercek, A., Greten, F. R., Bando, H., Shi, Q., García‐Aguilar, J., Romesser, P. B., et al. (2024) · Nature Reviews Gastroenterology & Hepatology · doi:10.1038/s41575-024-00900-9 · Cited by 87 · see citing works
Cobianchi, L., Piccolo, D., Mas, F. D., Agnoletti, V., Ansaloni, L., Balch, J. A., Biffl, W. L., Butturini, G., Catena, F., Coccolini, F., et al. (2023) · World Journal of Emergency Surgery · doi:10.1186/s13017-022-00467-3 · Cited by 80 · see citing works
“Given the potential of AI in clinical practice [7, 20, 30] and the speed of development, the role of scientific societies like the WSES and surgical training programs are crucial to expanding knowledge regarding AI-enabled decision aids, disseminate new approaches, and encompass them in training modules and clinical g…”
BackgroundSelf-citationPedro, A. R., Dias, M. B., Laranjo, L., Cunha, A. S., & Cordeiro, J. V. (2023) · PLOS ONE · doi:10.1371/journal.pone.0290613 · Cited by 74 · see citing works
Explainability pitfalls: beyond dark patterns in explainable AI
Ehsan, U., & Riedl, M. O. (2024) · Patterns · doi:10.1016/j.patter.2024.100971 · Cited by 72 · see citing works
Artificial Intelligence and Computer Vision in Low Back Pain: A Systematic Review
D’Antoni, F., Russo, F., Ambrosio, L., et al. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph182010909 · Cited by 72 · see citing works
Nam, J. Y., Lee, J., Bae, J. H., Chang, Y. W., Cho, Y., Sinn, D. H., Kim, B. H., Kim, S. H., Yi, N., Lee, K., et al. (2020) · Cancers · doi:10.3390/cancers12102791 · Cited by 73 · see citing works
“There are also attempts to support clinical decision making using AI [9].”
BackgroundSurgical Simulation: Virtual Reality to Artificial Intelligence
Riddle, E. W., Kewalramani, D., Narayan, M., & Jones, D. B. (2024) · Current Problems in Surgery · doi:10.1016/j.cpsurg.2024.101625 · Cited by 71 · see citing works
Elhage, S. A., Deerenberg, E. B., Ayuso, S. A., Murphy, K. J., Shao, J. M., Kercher, K. W., Smart, N. J., Fischer, J. P., Augenstein, V. A., Colavita, P. D., et al. (2021) · JAMA Surgery · doi:10.1001/jamasurg.2021.3012 · Cited by 70 · see citing works
Artificial intelligence in perioperative management of major gastrointestinal surgeries
Solanki, S. L., Pandrowala, S., Nayak, A., BHANDARE, M. S., Ambulkar, R. P., & Shrikhande, S. V. (2021) · World Journal of Gastroenterology · doi:10.3748/wjg.v27.i21.2758 · Cited by 71 · see citing works
Artificial intelligence for oral and maxillo-facial surgery: A narrative review
Rasteau, S., Ernenwein, D., Savoldelli, C., & Bouletreau, P. J. (2022) · Journal of Stomatology Oral and Maxillofacial Surgery · doi:10.1016/j.jormas.2022.01.010 · Cited by 68 · see citing works
Giorgino, R., Bonetti, M. A., Luca, A., Migliorini, F., Rossi, N., Peretti, G. M., & Mangiavini, L. (2023) · Frontiers in Surgery · doi:10.3389/fsurg.2023.1284015 · Cited by 68 · see citing works
Pamporaki, C., Berends, A. M. A., Filippatos, A., Prodanov, T., Meuter, L., Prejbisz, A., Beuschlein, F., Faßnacht, M., Timmers, H. J. L. M., Nölting, S., et al. (2023) · The Lancet Digital Health · doi:10.1016/s2589-7500(23)00094-8 · Cited by 70 · see citing works
Enhancing head and neck tumor management with artificial intelligence: Integration and perspectives
Zhong, N., Wang, H., Huang, X., Li, Z., Cao, L., Huo, F., Liu, B., & Bu, L. (2023) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2023.07.002 · Cited by 71 · see citing works
Computer Vision in the Operating Room: Opportunities and Caveats
Kennedy-Metz, L. R., Mascagni, P., Torralba, A. B., DIAS, R. D., Perona, P., Shah, J., Padoy, N., & Zenati, M. A. (2020) · IEEE Transactions on Medical Robotics and Bionics · doi:10.1109/tmrb.2020.3040002 · Cited by 66 · see citing works
“The growing availability of recorded behavior in the OR is crucial to the development of new techniques for behavior analysis, especially related to surgical flow disruptions, in machine learning and artificial intelligence more broadly [32].”
MethodEthical, Legal, and Financial Considerations of Artificial Intelligence in Surgery
Morris, M. X., Song, E. Y., Rajesh, A., Asaad, M., & Phillips, B. T. (2022) · The American Surgeon · doi:10.1177/00031348221117042 · Cited by 63 · see citing works
“The field of surgery similarly involves the application of tools for complex problem-solving, using known data (ie, evidence-based standards of care) to apply predictions and find solutions for novel cases.(3,4)While AI has the potential to optimize and transformmany aspects of surgical decision-making, as with all ne…”
MethodInteligencia artificial en medicina: presente y futuro
Ortega, D. L., Carrillo‐Pérez, D. L., & Carrillo-Esper, R. (2022) · Gaceta Médica de México · doi:10.24875/gmm.m22000688 · Cited by 59 · see citing works
Lee, J.-T., Hsieh, C., Lin, C., Lin, Y.-J., & Kao, C. (2021) · Scientific Reports · doi:10.1038/s41598-021-98961-2 · Cited by 58 · see citing works
Ray, A., Das, J., & Wenzel, S. E. (2022) · Cell Reports Medicine · doi:10.1016/j.xcrm.2022.100857 · Cited by 61 · see citing works
Heart Coronary Artery Segmentation and Disease Risk Warning Based on a Deep Learning Algorithm
Xiao, C., Li, Y., & Jiang, Y. (2020) · IEEE Access · doi:10.1109/access.2020.3010800 · Cited by 58 · see citing works
Artificial intelligence in thoracic surgery: past, present, perspective and limits
Etienne, H., Hamdi, S., Le Roux, M., Camuset, J., Khalife-Hocquemiller, T., Giol, M., Debrosse, D., & Assouad, J. (2020) · European Respiratory Review · doi:10.1183/16000617.0010-2020 · Cited by 48 · see citing works
de Simone, B. D., Abu‐Zidan, F. M., Gumbs, A. A., Chouillard, E. K., Saverio, S. D., Sartelli, M., Coccolini, F., Ansaloni, L., Collins, T., Kluger, Y. S., et al. (2022) · World Journal of Emergency Surgery · doi:10.1186/s13017-022-00413-3 · Cited by 56 · see citing works
“[19] showed that traditional clinical-decision support systems are time-consuming because of manual data management and do not consider the nonlinear relationship among multiple non-static variables, decreasing accuracy.”
BackgroundReinforcement learning in surgery
Datta, S., Li, Y., Ruppert, M. M., et al. (2021) · Surgery · doi:10.1016/j.surg.2020.11.040 · Cited by 54 · see citing works
Self-citationThe Potential of AI in Health Higher Education to Increase the Students’ Learning Outcomes
Sousa, M. J., Mas, F. D., Pesqueira, A. M., Lemos, C. M., Verde, J. M., & Cobianchi, L. (2021) · TEM Journal · doi:10.18421/tem102-02 · Cited by 57 · see citing works
Team dynamics in emergency surgery teams: results from a first international survey
Cobianchi, L., Mas, F. D., Massaro, M., Fugazzola, P., Coccolini, F., Kluger, Y. S., Leppäniemi, A. K., Moore, E. E., Sartelli, M., Angelos, P., et al. (2021) · World Journal of Emergency Surgery · doi:10.1186/s13017-021-00389-6 · Cited by 52 · see citing works
“Such results seem in contrast with the latest trends in surgery, which highlight, for instance, the increasing use of new technologies, e-health, telemedicine and online tools [27–29], including Artificial Intelligence and Machine Learning [30, 31] in supporting surgical decision making [32], and the recognized import…”
ResultMachine Learning and Artificial Intelligence in Neurosurgery: Status, Prospects, and Challenges
Dagi, T. F., Barker, F. G., & Glass, J. L. (2021) · Neurosurgery · doi:10.1093/neuros/nyab170 · Cited by 53 · see citing works
Christou, C. D., & Tsoulfas, G. (2021) · World Journal of Gastroenterology · doi:10.3748/wjg.v27.i37.6191 · Cited by 54 · see citing works
From Scalpels to Algorithms: The Risk of Dependence on Artificial Intelligence in Surgery
Adegbesan, A., Akingbola, A., Aremu, O., Adewole, O. A., Amamdikwa, J. C., & Shagaya, U. N. (2024) · Journal of Medicine Surgery and Public Health · doi:10.1016/j.glmedi.2024.100140 · Cited by 53 · see citing works
Cavazza, A., Mas, F. D., Paoloni, P., & Manzo, M. (2023) · British Food Journal · doi:10.1108/bfj-02-2023-0132 · Cited by 51 · see citing works
Exploring Intra- and Inter-Video Relation for Surgical Semantic Scene Segmentation
Jin, Y., Yu, Y., Chen, C., Zhao, Z., Heng, P., & Stoyanov, D. V. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3177077 · Cited by 49 · see citing works
“wise context awareness of tissues and instruments, which is fundamentally required for supporting several downstream tasks, such as surgical decision making [2], [3], surgical navigation [4] and skill assessment [5], [6].”
BackgroundLaios, A., Kalampokis, E., Johnson, R. L., Munot, S., Thangavelu, A., Hutson, R., Broadhead, T., Theophilou, G., Leach, C., Nugent, D., et al. (2022) · Cancers · doi:10.3390/cancers14143447 · Cited by 47 · see citing works
“The quality of surgical decision-making is influenced by patient values and emotions, patient– surgeon interactions, resource availability, uncertainty, hypothetico-deductive reasoning, and individual judgment [15].”
BackgroundArtificial Intelligence in Surgical Learning
Pakkasjärvi, N. A., Luthra, T., & Anand, S. (2023) · Surgeries · doi:10.3390/surgeries4010010 · Cited by 45 · see citing works
Chen, M. (2024) · American Journal of Translational Research · doi:10.62347/myhe3488 · Cited by 48 · see citing works
Kahr, P. K., Rooks, G., Willemsen, M. C., & Snijders, C. C. P. (2024) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3686164 · Cited by 49 · see citing works
“In conclusion, in many decision scenarios, people accepting AI advice has led to better outcomes [60, 61, 79].”
Choi, E., Leonard, K. W., Jassal, J. S., Levin, A. M., Ramachandra, V., & Jones, L. R. (2023) · Facial Plastic Surgery · doi:10.1055/s-0043-1770160 · Cited by 46 · see citing works
Schulz, P. J., Lwin, M. O., Kee, K. M., Goh, W. W. B., Lam, T. Y. T., & Sung, J. J. �. (2023) · Frontiers in Public Health · doi:10.3389/fpubh.2023.1301563 · Cited by 39 · see citing works
Singam, A. P. (2023) · Cureus · doi:10.7759/cureus.49887 · Cited by 40 · see citing works
“In this symbiotic interplay between AI and surgical practice, the potential for improved precision and efficiency and overall advancements in patient care come to the forefront [20].”
BackgroundArtificial Intelligence Literacy Levels of Perioperative Nurses: The Case of Türkiye
Kahraman, H., Akutay, S., Kaçmaz, H. Y., & Taşçı, S. (2025) · Nursing and Health Sciences · doi:10.1111/nhs.70059 · Cited by 46 · see citing works
Artifical intelligence use in orthopedics: an ethical point of view
Biçer, E. K., Fangerau, H., & Sur, H. (2023) · EFORT Open Reviews · doi:10.1530/eor-23-0083 · Cited by 37 · see citing works
“Finally, accountability is a serious concern in the use of AI technologies (20).”
BackgroundGender stereotypes in AI-generated images
García-Ull, F. J., & Lázaro, M. M. (2023) · El Profesional de la Informacion · doi:10.3145/epi.2023.sep.05 · Cited by 42 · see citing works
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Koh, F. H.-X., Chua, J. M. W., Tan, J. L., Foo, F. J., Tan, W. J., Sivarajah, S. S., Ho, L. M. L., Teh, B. T., & Chew, M. (2021) · World Journal of Gastrointestinal Surgery · doi:10.4240/wjgs.v13.i8.734 · Cited by 34 · see citing works
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“Artificial intelligence (AI) is an umbrella term that describes the process through which a machine (computer) simulates human learning by incorporating (input) a large amount of data (big data), processing them and giving results that lead to conclusions, decisions or adjustments of function (output) [26].”
BackgroundThe paradigm of digital health: AI applications and transformative trends
Rashid, Z., Ahmed, H., Nadeem, N., Zafar, S. B., & Yousaf, M. Z. (2025) · Neural Computing and Applications · doi:10.1007/s00521-025-11081-0 · Cited by 37 · see citing works
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Machine learning in gastrointestinal surgery
Sakamoto, T., Goto, T., Fujiogi, M., & Lefor, A. K. (2021) · Surgery Today · doi:10.1007/s00595-021-02380-9 · Cited by 33 · see citing works
Lewandrowski, K., Elfar, J. C., Li, Z., Burkhardt, B. W., Lorio, M. P., Winkler, P., Oertel, J., Telfeian, A. E., Dowling, Á., Vargas, R. A. A., et al. (2023) · Journal of Personalized Medicine · doi:10.3390/jpm13050852 · Cited by 33 · see citing works
Artificial Intelligence in the Management of Rotator Cuff Tears
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“AI refers to a branch of computer systems able to execute tasks that imitate human cognitive functions, such as learning and problem-solving, by analysing and comparing data [8,9].”
BackgroundWu, S., Chen, Z., Liu, R., Li, A., Cao, Y. Q., Wei, A., Liu, Q., Liu, J., Wang, Y., Jiang, J., et al. (2023) · International Journal of Surgery · doi:10.1097/js9.0000000000000329 · Cited by 31 · see citing works
“In contrast, with the rapid advancement of AI technology, intelligent surgical quality control is now possible([28,29]).”
BackgroundArtificial intelligence versus surgeon gestalt in predicting risk of emergency general surgery
Moheb, M. N. E., Gebran, A., Maurer, L. R., Naar, L., Hechi, M. W. E., Breen, K., Dorken‐Gallastegi, A., Sinyard, R. D., Bertsimas, D., Velmahos, G. C., et al. (2023) · The Journal of Trauma: Injury, Infection, and Critical Care · doi:10.1097/ta.0000000000004030 · Cited by 34 · see citing works
“11 For instance, recent or eventful experiences affect surgical decision-making disproportionately more than remote or uneventful experiences.”
BackgroundThe automaton as a surgeon: the future of artificial intelligence in emergency and general surgery
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“The use of AI to perform diagnostic decisions in the emergency setting has been the main focus of many AI-based studies in the literature, aiming to compare how this technology can be a useful tool to stratify and screen patients in comparison with physicians [7].”
MethodExecutive summary of the artificial intelligence in surgery series
Loftus, T. J., Vlaar, A. P. J., Hung, A. J., Bihorac, A., Dennis, B. M., Juillard, C. J., Hashimoto, D. A., Kaafarani, H. M. A., Tighe, P. J., Kuo, P. C., et al. (2021) · Surgery · doi:10.1016/j.surg.2021.10.047 · Cited by 31 · see citing works
Self-citationA Cloud-Based Platform for Big Data-Driven CPS Modeling of Robots
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“and the Cyber system at the industrial robot level, providing important theoretical research significance and application value to better realize human-machine integration and promote better transformation and upgrading of the manufacturing industry [28]–[30].”
BackgroundArtificial intelligence in pediatric surgery
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“In the health care sector, the adoption of AI has markedly increased in recent years, demonstrating substantial potential [2–5].”
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“28 Decision tree-based methods are a subset of machine learning algorithms that uncover complex non-linear relationships between covariates.”
MethodPlease mind the gap—about equity and access to care in oncology
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“New technologies like artificial intelligence, machine learning,(28) and mixed and augmented reality(29) represent today the next frontier to ease and expedite cancer diagnosis(30) and surgical decision-making.(31) Searching for the best humanemachine interaction is one of the major challenges to enjoy the benefits pr…”
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Artificial intelligence in surgery: the emergency surgeon’s perspective (the ARIES project)
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“Loftus et al. [19] showed that traditional clinical-decision support systems are time-consuming because of manual data management and do not consider the nonlinear relationship among multiple non-static variables, decreasing accuracy.”
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“0 technologies [43] like robotics, virtual and augmented reality, artificial intelligence (AI) and machine learning [44, 45].”
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BackgroundMethodInfluentialEthical forethoughts on the use of artificial intelligence in medicine
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“The title “artificial intelligence” is an umbrella term which comprises concepts such as “machine learning” (ML) and “deep learning” (DL) [1].”
BackgroundArtificial Intelligence in Surgery and Its Potential for Gastric Cancer
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“We leave the detailed explanation of the principles of AI methodology and technology to other specialized reports [1,2,5-7].”
BackgroundCommunity-engaged artificial intelligence research: A scoping review
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- 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
- 611
- References
- 82
- Citations by year
- 2026: 113 · 2025: 130 · 2024: 118 · 2023: 90 · 2022: 67 · 2021: 54 · 2020: 27 · 2019: 1 · 2018: 1
- FWCI
- 9.99
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- JAMA Surgery (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (submittedVersion)
- PubMed Central (submittedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Surgery › Surgical Simulation and Training
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Keywords
- artificial intelligence, surgical decision-making, clinical decision support systems, predictive analytics, algorithmic bias, model interpretability, electronic health records, informed consent
- MeSH terms
- Clinical Decision-Making, Artificial Intelligence, Emotions, Humans, Surgical Procedures, Operative, Time Factors, Intuition, Decision Support Systems, Clinical, Uncertainty
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