This randomized controlled trial assessed the impact of LLM assistance (GPT-4) on physician performance in management reasoning tasks. Physicians using the LLM scored significantly higher (43.0% vs 35.7%, difference = 6.5%, 95% CI = 2.7% to 10.2%, P < 0.001) than those using conventional resources, but also spent significantly more time per case (801.5s vs 690.2s, difference = 119.3s, 95% CI = 17.4 to 221.2, P=0.022). The LLM alone performed comparably to LLM-assisted physicians.
The study provides compelling evidence that LLM assistance, specifically using GPT-4, significantly improves physician performance on management reasoning tasks compared to using conventional resources alone. The randomized controlled trial design and statistically significant results (p < 0.001) support a causal link between LLM use and improved scores. However, it's crucial to note that this improvement comes with a statistically significant increase in time spent per case. The study also found that the LLM alone performed comparably to LLM-assisted physicians, suggesting potential for independent use, although this finding requires cautious interpretation due to the smaller sample size in the LLM-alone arm.
The practical utility of these findings is promising, suggesting that LLMs could serve as valuable decision support tools in clinical practice, particularly for complex management decisions. The study is well-contextualized within existing literature, acknowledging the growing body of research on LLMs in diagnostic reasoning and highlighting the novelty of its focus on management reasoning. However, the study's reliance on clinical vignettes, while ecologically valid to a degree, limits the direct applicability of the findings to real-world clinical settings. The increased time spent per case also raises questions about the efficiency of LLM use in time-constrained environments.
While the study demonstrates a clear benefit in terms of improved scores, the guidance for practitioners must be nuanced. LLMs like GPT-4 show potential as decision support tools, but their implementation should be approached with caution. The increased time investment, potential for bias, and risk of hallucinations/misinformation need careful consideration. The study strongly suggests that LLMs should be used as *assistive* tools, augmenting physician judgment rather than replacing it. Further research is needed to optimize the integration of LLMs into clinical workflows and to minimize potential risks.
Several critical questions remain unanswered. The long-term effects of LLM use on physician skills and clinical judgment are unknown. Will over-reliance on LLMs lead to deskilling or reduced critical thinking? The study's limitations, particularly the use of clinical vignettes and the lack of external validity evidence for the scoring rubrics, raise concerns about the generalizability of the findings. While the study addresses potential harm and finds no significant difference between groups, the long-term safety implications of LLM use in real-world clinical practice require further investigation. Future research should focus on real-world clinical trials, longitudinal studies, and the development of robust methods for detecting and mitigating potential biases and harms associated with LLM use.
The abstract clearly states the research question, the study design (randomized controlled trial), the intervention (GPT-4 assistance), the control group (conventional resources), the primary outcome (difference in total score), and the main findings, including statistical significance.
The abstract provides key statistical data (mean difference, confidence interval, p-value) for the primary outcome, lending quantitative support to the findings.
The abstract concisely mentions secondary outcomes, including time spent per case, and provides relevant statistical data for these as well.
The abstract includes the ClinicalTrials.gov registration number, promoting transparency and reproducibility.
This high-impact improvement would enhance the abstract's clarity and completeness by providing a brief overview of the participant characteristics. The abstract is the first point of contact for most readers, and quickly conveying the type of physicians involved is crucial for contextualizing the results. It helps readers assess the generalizability and relevance of the findings to their own practice or research.
Implementation: Add a sentence summarizing the key demographics of the physician participants, such as their specialties and years of experience. For example: 'Participants were 92 practicing physicians (primarily internal medicine, with an average of 7.6 years of experience) randomized to...'
This medium-impact improvement would strengthen the abstract by explicitly stating the direction of the effect on time spent per case. The abstract currently mentions a statistically significant difference, but it's not immediately clear whether LLM users spent more or less time. Explicitly stating this enhances clarity and avoids potential misinterpretations. This information belongs in the abstract as it is a key finding that contextualizes the primary outcome.
Implementation: Modify the sentence about time spent to clearly indicate that LLM users spent *more* time per case. For example, change to: 'LLM users spent significantly more time per case...'
This low-impact improvement would provide a more complete and nuanced understanding of the study's findings within the abstract. While the abstract mentions that LLM-augmented physicians performed similarly to the LLM alone, adding the direction of the trend compared to conventional resources adds important context. It allows readers to quickly grasp the relative performance of all three groups (physicians + LLM, physicians + conventional resources, and LLM alone).
Implementation: Add a phrase indicating the direction of the trend when comparing LLM alone to conventional resources. For example: 'There was no significant difference between LLM-augmented physicians and LLM alone (−0.9%, 95% CI = −9.0 to 7.2, P = 0.8), although the LLM alone trended towards higher scores than the conventional resources group.'
The introduction clearly establishes the research gap by highlighting the known capabilities of LLMs in diagnostic reasoning and contrasting it with the unknown impact on management reasoning. This sets the stage for the study's central question.
The introduction effectively differentiates between diagnostic and management reasoning, providing context for the study's focus. It highlights the complexity of management reasoning, involving multiple factors and trade-offs, unlike diagnostic reasoning, which often has a single correct answer.
The introduction provides a concise overview of the study design, mentioning the prospective, randomized, controlled trial approach and the use of clinical vignettes derived from real patient encounters. This gives readers a quick understanding of the study's methodology.
The introduction connects the research to existing literature on clinical reasoning, mentioning the history of diagnostic reasoning research and the more recent focus on management reasoning. This contextualizes the study within the broader field.
This medium-impact improvement would strengthen the introduction by providing a more explicit justification for focusing on *physician* performance. While the ultimate goal is likely improved patient care, the introduction could briefly explain why studying physician performance is a crucial intermediate step. This is important because it clarifies the study's direct focus and links it to the broader goal of patient well-being. It also helps readers understand the chain of reasoning: better physician performance is expected to lead to better patient outcomes.
Implementation: Add a sentence or phrase explaining the link between physician performance and patient outcomes. For example: 'By focusing on physician performance, a key determinant of care quality, this study aims to contribute to the broader goal of improving patient outcomes.'
This low-impact improvement would add a brief mention of the specific LLM used (GPT-4) in the introduction. While this is mentioned in the abstract, including it in the introduction provides immediate context for readers. It clarifies the technology being evaluated and helps readers familiar with different LLMs understand the scope of the study.
Implementation: Add "(GPT-4)" after the first mention of LLM in the introduction. For instance, 'While large language models (LLMs) (GPT-4) have shown promise...'
This low-impact improvement would enhance the introduction by briefly mentioning the potential benefits of LLMs in management reasoning *before* stating the research question. This creates a more compelling narrative by first suggesting the potential and then highlighting the need for research to confirm it. It frames the study as addressing a promising but unproven area.
Implementation: Add a sentence or phrase before the statement of the research question, hinting at the potential benefits. For example: 'Given the potential of LLMs to assist with complex decision-making, this study investigated whether...' or 'If LLMs can indeed improve management reasoning, this could have significant implications for clinical practice. Therefore, this study...'
The Results section clearly presents the participant demographics, including career stage, specialty, years in training, and prior experience with GPT. This provides essential context for interpreting the study findings and assessing their generalizability.
The section reports the primary outcome (total score) with appropriate statistical detail, including the difference between groups, confidence interval, and p-value. This allows for a clear understanding of the magnitude and significance of the effect.
The Results section includes secondary outcomes, such as performance in different question domains (management, diagnostic, specific, general, factual) and time spent per case. This provides a more nuanced understanding of the LLM's impact.
The section presents a comparison between the performance of physicians using the LLM, physicians using conventional resources, and the LLM alone. This provides valuable insight into the potential of the LLM as an independent tool.
The section reports inter-rater reliability statistics (pooled kappa) for the scoring of cases, indicating substantial agreement between graders. This enhances the credibility of the scoring process.
The section includes post-hoc sensitivity analyses adjusting for time spent and response length, addressing potential confounding factors and strengthening the main findings.
The Results section briefly addresses the potential for harm, reporting similar patterns of likelihood and severity of harm between the two groups. This is an important consideration for the ethical implementation of LLMs.
This low-impact improvement would enhance the clarity of the Results section. While the text mentions "400 cases were scored in total," and later refers to n values for different groups and outcomes, it's not immediately obvious how these numbers relate to the 92 physicians. Explicitly stating the number of cases completed *per physician* (on average or the range) would improve clarity. This aligns with the Results section's purpose of transparently reporting the data.
Implementation: Add a sentence clarifying the average or range of cases completed per physician. For example: 'The 92 physicians completed a total of 400 cases, with an average of X cases per physician (range Y-Z).'
This medium-impact improvement would provide a more complete picture of the participant demographics and their experience with LLMs. While Table 1 provides percentages for past GPT experience, it doesn't explicitly state the *number* of physicians in each category. Adding the raw numbers (n values) alongside the percentages would give a clearer sense of the distribution. The Results section is where this level of detail is expected, providing context for the study's findings.
Implementation: In Table 1, add the raw number (n) for each category of past GPT experience, in addition to the percentages. For example: 'I use it frequently (weekly or more) 22 (24%) 11 (24%) 11 (24%)'
This low-impact improvement would enhance the clarity and readability of Table 2. Currently, the table presents the number of cases (n) for each outcome, but these numbers vary. Providing a brief explanation for *why* the 'n' values differ across outcomes would improve reader understanding. This is important for transparency and aligns with the Results section's role in presenting the data clearly.
Implementation: Add a footnote to Table 2 explaining the varying 'n' values. For example: 'The 'n' values vary across outcomes due to some physicians not completing all questions within each case, or not all questions being applicable to every case.'
This medium-impact improvement would enhance the Results section by providing more specific information about the potential for harm. While the section mentions similar patterns between groups, it lacks detail. Reporting the actual percentages for *each* category of likelihood and severity of harm would provide a more complete and nuanced understanding of this important aspect. This belongs in the Results section as it is a direct finding of the study.
Implementation: Include the specific percentages for each category of likelihood (medium, high) and severity (mild-to-moderate, severe) of harm for both groups. For example: 'In the LLM-assisted group, 8.5% and 4.2% of physician responses carried medium and high likelihood of harm, respectively, compared to 11.4% and 2.9% in the conventional resources group. Regarding harm severity, mild-to-moderate harm was observed in 4.0% of LLM-assisted responses compared to 5.3% in the conventional resources group. Severe harm ratings were nearly identical between groups (LLM = 7.7%; conventional = 7.5%).'
Fig. 1| Study flow diagram. The study included 92 practicing attending physicians and residents with training in internal medicine, family medicine or emergency medicine. Five expert-developed cases were presented, with scoring rubrics created using a Delphi process. Physicians were randomized to use either GPT-4 via ChatGPT plus in addition to conventional resources (for example, UpToDate, Google), or conventional resources alone. The primary outcome was the difference in total score between groups on expert-developed scoring rubrics. Secondary outcomes included domain-specific scores and time spent per case.
Table 2 | Comparisons of the primary and secondary outcomes for physicians with LLM and with conventional resources only (scores standardized to 0-100)
Fig. 2 | Comparison of the primary outcome for physicians with LLM and with conventional resources only (total score standardized to 0-100). Ninety-two physicians (46 randomized to the LLM group and 46 randomized to conventional resources) completed 375 cases (178 in the LLM group, 197 in the conventional resources group). The center of the box plot represents the median, with the boundaries representing the first and third quartiles. The whiskers represent the furthest data points from the center within 1.5 times the IQR.
Fig. 3 | Comparison of the primary outcome according to GPT alone versus physician with GPT-4 and with conventional resources only (total score standardized to 0-100). The GPT-alone arm represents the model being prompted by the study team to complete the five cases, with the models prompted five times for each case for a total of 25 observations. The physicians with GPT-4 group included 46 participants that completed 178 cases, while the physician with conventional resources group included 46 participants that completed 197 cases. The center of the box plot represents the median, with the boundaries representing the first and third quartiles. The whiskers represent the furthest data points from the center within 1.5 times the IQR.
Fig. 4 | Comparison of the time spent per case by physicians using GPT-4 and physicians using conventional resources only. Ninety-two physicians (46 randomized to the LLM group and 46 randomized to the conventional resources) completed 375 cases (178 in the LLM group, 197 in the conventional resources group). The center of the boxplot represents the median, with the boundaries representing the first and third quartiles. The whiskers represent the furthest data points from the center within 1.5 times the IQR.
Extended Data Fig. 1 | Correlation between Time Spent in Seconds and Total Score. This figure demonstrates a sample medical management case with multi-part assessment questions, scoring rubric and example responses. The case presents a 72-year-old post-cholecystectomy patient with new-onset atrial fibrillation. The rubric (23 points total) evaluates clinical decision-making across key areas: initial workup, anticoagulation decisions, and outpatient monitoring strategy. Sample high-scoring (21/23) and low-scoring (8/23) responses illustrate varying depths of clinical reasoning and management decisions.
Extended Data Table 2 | Post-hoc Analysis for the Associations between the Primary and Secondary Outcomes Overall
The Discussion section effectively summarizes the main finding of the study: that LLM assistance improved physician management reasoning compared to conventional resources. This provides a clear and concise restatement of the primary result.
The section appropriately places the findings in the context of existing literature, comparing and contrasting the results with previous studies on diagnostic support systems and LLM use in diagnostic reasoning. This helps to situate the study within the broader field of research.
The Discussion section explores potential mechanisms underlying the observed effects, such as the possibility that LLM interaction served as a beneficial 'time out' for better consideration of patient context. This demonstrates a thoughtful consideration of the cognitive processes involved.
The section acknowledges the limitations of the study, including the use of clinical vignettes rather than real patient cases and the lack of external validity evidence for the scoring rubrics. This demonstrates a critical and balanced assessment of the study's limitations.
The Discussion section raises important considerations for the real-world implementation of LLMs in clinical settings, such as the potential impact of hallucinations and misinformation on patient care. This highlights the ethical and practical challenges of deploying LLMs in healthcare.
The section connects the findings to broader implications for clinical practice, suggesting that decision support represents a promising application of LLMs. This highlights the potential of LLMs to enhance patient care beyond clerical workflows.
The Discussion section effectively uses an illustrative example (lung nodule management) to highlight the complexity of management reasoning and the importance of considering patient-specific factors. This helps to clarify the distinction between diagnostic and management reasoning.
This medium-impact improvement would enhance the Discussion section by providing a more balanced discussion of the potential benefits and risks of LLM use. While the section acknowledges the risk of hallucinations and misinformation, it could more explicitly discuss the potential for bias, over-reliance on the LLM, and the impact on physician-patient interaction. This is crucial for the Discussion section, which is responsible for providing a comprehensive and nuanced interpretation of the study's findings and their implications.
Implementation: Add a paragraph discussing potential risks and downsides of LLM use, including: (1) Potential for bias in LLM output. (2) Risk of over-reliance on the LLM by physicians, potentially leading to deskilling or reduced critical thinking. (3) Potential negative impact on the physician-patient relationship if LLMs are perceived as impersonal or replacing human interaction. (4) Challenges in ensuring equitable access to LLM technology.
This medium-impact improvement would strengthen the Discussion section by explicitly connecting the findings back to the theoretical framework of management scripts introduced in the Introduction. The Discussion should explain how the study's results support or challenge the existing understanding of management scripts and their use in clinical decision-making. This is important for linking the study's empirical findings to the theoretical underpinnings of the research, a key function of the Discussion section.
Implementation: Add a paragraph explicitly discussing the implications of the findings for the understanding of management scripts. For example: 'The study's findings suggest that LLMs may be able to augment or improve physician management scripts by providing alternative perspectives and prompting consideration of a wider range of factors. This challenges the traditional view of management scripts as solely individual cognitive heuristics and suggests a potential role for AI in shaping and refining these scripts.'
This low-impact improvement would improve the clarity of the Discussion section. While the section mentions the potential for LLMs to influence physicians to be more empathetic, it could benefit from a more precise definition of "empathy" in this context. Different interpretations of empathy exist, and clarifying the specific aspect observed in the study would strengthen the claim. This aligns with the Discussion section's role in providing a clear and precise interpretation of the study's findings.
Implementation: Replace "apparent empathy" with a more specific description of the observed behavior. For example: '...we observed that physicians using the LLM more frequently expressed consideration for the perspectives and feelings of other providers and patients in difficult situations.' Also, consider adding a brief definition or operationalization of empathy in this context.
This low-impact improvement would enhance the Discussion section by providing a more nuanced discussion of the study's limitations related to the clinical vignettes. While the section acknowledges that the cases are not real patient cases, it could further elaborate on the potential impact of this limitation on the study's external validity and generalizability. The Discussion section is the appropriate place to address such limitations in detail.
Implementation: Expand the discussion of the limitations related to clinical vignettes. For example: 'While the clinical vignettes were designed to be realistic and based on real patient encounters, they necessarily represent a simplification of the complexities of real-world clinical practice. Factors such as time pressure, interruptions, and emotional responses to patients, which can influence decision-making, are difficult to fully replicate in a simulated setting. This may limit the generalizability of the findings to real-world clinical scenarios.'
The Methods section clearly describes the participant recruitment process, including the target population (practicing physicians and residents in general medical specialties), recruitment sources (email lists from three institutions), and the requirement for written informed consent. This provides transparency about the study population.
The section specifies the study's exemption from institutional review board oversight, naming the relevant IRBs. This indicates adherence to ethical research practices.
The Methods section details the construction of the clinical case vignettes, including their origin (Core IM podcast), the involvement of a panel of experts in their development, and the intentional selection of cases to explore a breadth of management decision-making. This provides context for the study's stimulus materials.
The section describes the development of the scoring rubrics using a modified Delphi process involving an expert group. This indicates a rigorous approach to creating assessment tools for the complex task of management reasoning.
The Methods section clearly outlines the study design as a prospective, randomized, single-blind study, specifying the randomization process (to either GPT-4 or conventional resources) and the blinding of graders to group assignment. This provides a concise overview of the study's methodology.
The section describes the prompt design for the LLM-only arm, mentioning the use of established principles of prompt design and the iterative development of a zero-shot prompt. This provides insight into the methodology used for the LLM comparison group.
The Methods section details the rubric validation process, including the use of preliminary datasets, independent grading by multiple graders, and consensus meetings to resolve disagreements. This enhances the credibility of the scoring process.
The section clearly defines the primary and secondary study outcomes, including the mean score for each group and scores in predefined domains of the rubrics. This provides a clear understanding of the study's key measures.
The Methods section describes the statistical methods used, including the target sample size, the use of generalized mixed-effects models to account for clustering, and the statistical significance level. This provides a concise overview of the statistical analysis approach.
This medium-impact improvement would enhance the study's methodological rigor and transparency. The Methods section should explicitly state *how* participants were randomized to the two study arms. While the section mentions randomization, it doesn't specify the method used (e.g., simple randomization, block randomization, stratified randomization). This is important for assessing the potential for bias and ensuring the comparability of the two groups. The Methods section is the appropriate place for this level of detail.
Implementation: Add a sentence specifying the randomization method used. For example: 'Participants were randomized to the two study arms using block randomization with a block size of 4, ensuring balanced group sizes throughout the enrollment period.' or 'Simple randomization was performed using a computer-generated random number sequence.'
This high-impact improvement would significantly enhance the study's reproducibility and transparency. The Methods section should provide more detail about the "basic instruction on system access and use" given to participants in the GPT-4 arm. Simply stating that such instruction was provided is insufficient. The specific instructions, including any examples or guidance on how to interact with GPT-4, should be included, ideally in a supplementary file. This is crucial for allowing other researchers to replicate the study and understand the specific conditions under which GPT-4 was used. The Methods section is the definitive location for this information.
Implementation: Include a detailed description of the training provided to participants in the GPT-4 arm. This should include: (1) The specific instructions given on accessing and using the ChatGPT Plus interface. (2) Any examples or demonstrations provided to illustrate how to interact with GPT-4. (3) The content of any written or verbal instructions given. (4) The duration of the training. Ideally, provide the full training materials as a supplementary file.
This medium-impact improvement would enhance the clarity and completeness of the Methods section. While the section mentions that participants were proctored, it doesn't explicitly state whether the proctors were blinded to the study arm assignments. This is important for assessing the potential for bias in the administration of the study. The Methods section is where this information should be clearly stated.
Implementation: Add a sentence explicitly stating whether the proctors were blinded to the study arm assignments. For example: 'Proctors were blinded to the participants' group assignments to minimize potential bias.' or 'Due to the nature of the intervention, proctors were not blinded to group assignments, but they were instructed to provide only technical support and avoid influencing participants' responses.'
This low-impact improvement would add clarity to the Methods section. While the section mentions a modified Delphi process, it does not specify the *number of rounds* used in this process. This is a standard detail to report for Delphi studies, and it helps readers understand the extent of iteration and consensus-building. This detail belongs in the Methods section as part of the description of the rubric development.
Implementation: Add the number of rounds used in the modified Delphi process. For example: 'Through an iterative modified Delphi process, involving three rounds of expert feedback and refinement, we developed management rubrics...'
This medium-impact improvement would enhance the study's transparency and allow for a more thorough assessment of the scoring rubrics. The Methods section should provide more detail about the *criteria* used to determine whether answers were "reasonable" and therefore awarded points. While the section mentions that points were awarded for all answers deemed reasonable by the expert panel, it doesn't explain the criteria or guidelines used to make these judgments. This is important for understanding the scoring process and assessing its potential for bias. This information is crucial for the Methods section, which is where the scoring methodology is described.
Implementation: Provide a more detailed explanation of the criteria used to determine the "reasonableness" of answers. This could include: (1) Referencing established clinical guidelines or best practices. (2) Describing the process used by the expert panel to reach consensus on what constituted a reasonable answer. (3) Providing examples of answers that were considered reasonable and unreasonable. (4) Including the full scoring rubrics in a supplementary file.