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.