This study evaluates the performance of OpenAI's o1, a large language model with internalized chain-of-thought, in the medical field. Using 37 medical datasets, including new question-answering tasks based on professional medical quizzes from NEJM and The Lancet, the study assesses o1's understanding, reasoning, and multilingual capabilities. The research aims to address limitations in existing medical LLM benchmarks and provide a comprehensive evaluation of o1's potential in clinical applications.
Description: Presents accuracy and F1 results for o1 and other LLMs across various medical tasks, demonstrating o1's superior performance in Knowledge QA and Medical Calculation.
Relevance: Provides key quantitative evidence supporting o1's enhanced clinical understanding and reasoning.
Description: Compares o1 and GPT-4's responses on a LancetQA diagnostic question, visually showcasing o1's accurate and concise reasoning compared to GPT-4's hallucination and incorrect answer.
Relevance: Illustrates o1's superior reasoning ability in a real-world clinical scenario.
This study demonstrates the potential of OpenAI's o1 model as a step towards an "AI doctor." The evaluation across multiple medical datasets reveals o1's enhanced understanding and reasoning capabilities, showing promise for bridging the gap between AI and human doctors. However, further research is needed to address limitations such as hallucination and to develop more robust evaluation metrics for advanced LLMs in the medical domain. Future work should explore areas like safety, retrieval augmented generation, and the broader implications of integrating such models into healthcare practices.
This abstract introduces a study evaluating OpenAI's o1, a large language model with internalized chain-of-thought, in the medical field. It focuses on o1's understanding, reasoning, and multilingual capabilities across 37 medical datasets, including new question-answering tasks based on professional medical quizzes. The study finds that o1 shows promise in medical applications, outperforming GPT-4, but also identifies weaknesses like hallucination and inconsistent multilingual abilities.
The abstract clearly states the purpose of the research, which is to evaluate the performance of o1 in the medical domain.
The use of datasets based on professional medical quizzes (NEJM and The Lancet) adds clinical relevance and real-world applicability to the study.
While the abstract mentions that o1 surpasses GPT-4, it would be stronger to quantify this improvement with specific percentages or metrics.
Rationale: Quantifying the improvement would provide a more concrete measure of o1's performance gain and strengthen the impact of the findings.
Implementation: Include specific performance metrics, such as accuracy percentages or F1 scores, to demonstrate the extent of o1's improvement over GPT-4.
The abstract briefly mentions weaknesses like hallucination and inconsistent multilingual ability. Providing more detail on these limitations would strengthen the analysis.
Rationale: A more detailed discussion of the weaknesses would provide a more balanced perspective on o1's performance and highlight areas for future research.
Implementation: Briefly describe the types of hallucinations observed and the specific contexts where multilingual ability is inconsistent.
Figure 1 is a radar chart comparing the performance of five large language models (LLMs) on twelve medical datasets. The LLMs compared are o1, GPT-4, GPT-3.5, Meditron-70B, and Llama3-8B. The datasets include PubMedQA, MedQA, NEJMQA, MedMCQA, LancetQA, Medbullets, PUBHEALTH Ver., MedNLI-Dis, MedCalc-Bench, MIMIC4ED, PICO, and MedBench. The chart suggests o1 generally outperforms the other models on most datasets.
Text: "Figure 1: Overall results of o1 and other 4 strong LLMs. We show performance on 12 medical datasets spanning diverse domains. o1 demonstrates a clear performance advantage over close- and open-source models."
Context: This is the first sentence of the abstract and introduces the first figure summarizing the overall performance comparison of different LLMs.
Relevance: This figure is highly relevant to the abstract as it visually summarizes the main finding of the study: o1's superior performance on a range of medical datasets, supporting the claim that it advances the development of an 'AI doctor'.
Figure 2 is a bar chart showing the average accuracy of the same five LLMs (o1, GPT-4, GPT-3.5, Meditron-70B, and Llama3-8B) across 19 medical datasets. o1 achieves the highest average accuracy of 74.3%.
Text: "Figure 2: Average accuracy of o1 and other 4 strong LLMs. o1 achieves the highest average accuracy of 74.3% across 19 medical datasets."
Context: This sentence, appearing immediately after the mention of Figure 1, introduces the second figure focusing on average accuracy across a wider range of datasets.
Relevance: This figure complements Figure 1 by providing a summary statistic (average accuracy) across a larger set of datasets, further strengthening the claim of o1's superior performance and its relevance to the abstract's main point.
This introduction sets the stage for a study evaluating the OpenAI o1 model's performance in medicine. It discusses the concept of general intelligence, the progress of LLMs, and the limitations of current medical LLM benchmarks. The paper aims to address this gap by evaluating o1's understanding, reasoning, and multilingual capabilities in medicine using existing and newly created datasets.
The introduction effectively establishes the context by discussing the broader field of AI and the evolution of LLMs, leading to the rationale for evaluating o1 in medicine.
The introduction clearly articulates the limitations of current medical LLM benchmarks and the need for a more comprehensive evaluation of advanced models like o1.
While the introduction mentions o1's internalized CoT, it could further elaborate on what makes o1 unique compared to previous models and how this might impact its performance in medicine.
Rationale: Highlighting o1's specific advancements would strengthen the motivation for the study and clarify its contribution.
Implementation: Add a sentence or two explaining the novel aspects of o1's architecture or training process that differentiate it from previous LLMs, particularly in the context of medical applications.
The introduction mentions three key aspects (understanding, reasoning, and multilinguality), but could more clearly define what these entail in the medical context and how they will be operationalized in the evaluation.
Rationale: Providing more concrete definitions would improve the clarity and rigor of the evaluation framework.
Implementation: Briefly define each aspect (understanding, reasoning, and multilinguality) within the specific context of medical applications. For example, explain what "understanding" means for a medical LLM and how it differs from "reasoning" in this domain.
This section discusses prior work on large language models (LLMs) with enhanced reasoning abilities, particularly in the medical domain. It highlights the development of LLMs with chain-of-thought (CoT) reasoning and reinforcement learning, and their application to medical tasks. The section also emphasizes the need for comprehensive evaluation of LLMs in medicine, considering understanding, reasoning, and multilinguality.
The section effectively establishes the context of LLMs and their growing importance in various fields, including medicine. It clearly motivates the need for evaluating o1's capabilities in this domain.
The section provides a concise yet relevant overview of prior work on LLMs with enhanced reasoning and their application to medical tasks. It cites key studies that have contributed to this area.
While multilinguality is mentioned as an important aspect, the section could benefit from a more in-depth discussion of the challenges and opportunities of multilingual medical LLMs.
Rationale: Given the global nature of healthcare, addressing language barriers is crucial for the widespread adoption of medical LLMs. A deeper discussion would highlight the importance of this aspect.
Implementation: Include a brief discussion of the specific challenges in developing multilingual medical LLMs, such as data scarcity in certain languages and the need for cross-lingual evaluation. Mention any existing work on multilingual medical NLP and how o1 relates to it.
The section mentions the need for comprehensive evaluation but doesn't delve into the specific metrics that will be used. Providing more detail on the chosen metrics would strengthen the methodological foundation.
Rationale: Clearly defining the evaluation metrics upfront would enhance the transparency and rigor of the study. It would also allow readers to better understand the criteria used to assess o1's performance.
Implementation: Briefly mention the types of metrics that will be used to evaluate understanding, reasoning, and multilinguality. For example, mention accuracy, F1-score, BLEU, or other relevant metrics. Refer to Table 1 if it provides more details on the metrics.
This section details the evaluation pipeline used in the study, outlining the taxonomy of evaluations, the aspects and tasks involved, and the metrics employed. The pipeline assesses language models across understanding, reasoning, and multilinguality using a variety of medical datasets and prompting strategies. The section emphasizes the comprehensive nature of the evaluation, aiming to provide a holistic view of LLM performance in the medical domain.
The section is well-organized, presenting the evaluation pipeline in a logical and easy-to-follow manner. The clear structure makes it easy to understand the different components of the evaluation.
The evaluation encompasses a wide range of tasks, datasets, and metrics, covering key aspects of medical language understanding and reasoning. This comprehensive approach strengthens the validity and generalizability of the findings.
While the section mentions three prompting strategies (direct, chain-of-thought, and few-shot), it could provide more specific examples of how these prompts are constructed and implemented for different tasks.
Rationale: Providing more concrete examples of the prompts would enhance the reproducibility of the study and allow for better comparison with future work.
Implementation: Include example prompts for at least one task under each aspect (understanding, reasoning, and multilinguality). This could be done by adding a supplementary table or appendix with example prompts.
The section lists the metrics used but could provide more justification for why these specific metrics were chosen and how they align with the goals of the evaluation.
Rationale: Providing a rationale for the metric selection would strengthen the methodological rigor of the study and demonstrate that the chosen metrics are appropriate for evaluating the specific tasks and aspects.
Implementation: For each metric (accuracy, F1-score, BLEU, ROUGE, AlignScore, and Mauve), briefly explain why it is suitable for evaluating the corresponding tasks. For example, explain why accuracy is appropriate for multiple-choice questions and why BLEU/ROUGE are used for free-form text generation.
Figure 3 illustrates the evaluation pipeline used in the study. It outlines the aspects of model capabilities (understanding, reasoning, and multilinguality), the prompt strategies (direct prompting, chain-of-thought, few-shot prompting), the language models used (o1, GPT 3.5, GPT 4, Meditron, Llama3), and the evaluation metrics (Prediction Accuracy, Hallucination, Tendency, and Free-form Text Generation Capability). The diagram shows a flow from aspects and tasks to prompt strategies, then to language models, and finally to evaluation.
Text: "First, we present the taxonomy of our evaluation, along with an overview of the evaluation pipeline as shown in Figure 3."
Context: This sentence appears in the 'Overall Taxonomy of Evaluations' subsection, introducing the figure that visually represents the evaluation pipeline.
Relevance: Figure 3 is crucial for understanding the methodology of the study. It visually outlines the entire evaluation process, connecting the aspects being evaluated, the prompting strategies, the models used, and the metrics employed. This provides a clear framework for the experiments and results presented later in the paper.
Table 1 provides a detailed list of the 37 datasets used in the evaluation, categorized by three fundamental aspects: Understanding (Concept Recognition, Text Summary), Reasoning (Knowledge QA, Clinical Decision Support, Agent, Medical Calculation), and Multilinguality (Knowledge QA, Agent). For each dataset, the table lists the task, a brief description, and the metrics used for evaluation. Asterisks mark newly constructed datasets.
Text: "In Table 1, our evaluation efforts are structured into three main parts: aspect, task, and dataset."
Context: This sentence, located in the 'Aspects and Tasks' subsection, introduces Table 1, which details the datasets used in the evaluation, organized by aspect and task.
Relevance: Table 1 is essential for understanding the scope and methodology of the study. It provides a comprehensive overview of the datasets used, linking them to the three aspects being evaluated (understanding, reasoning, and multilinguality). This allows readers to assess the breadth and relevance of the evaluation.
This section details the experimental setup, including prompting strategies, models used, and the main result highlighting o1's improved performance in clinical understanding and reasoning. It emphasizes o1's superior performance compared to other models, particularly in knowledge understanding and diagnostic reasoning scenarios, suggesting a step closer to an "AI doctor". The section also sets the stage for further analysis by mentioning other observations and potential limitations.
The section clearly outlines the prompting strategies and models used in the evaluation, providing sufficient detail for reproducibility.
The section provides compelling evidence for o1's superior performance in various medical tasks, supporting the claim of its enhanced capabilities in clinical understanding and reasoning.
While the section mentions investigating advanced promptings, it lacks details on their specific implementation and impact on o1's performance. Providing more information would enhance the analysis.
Rationale: A more detailed analysis of advanced prompting techniques would provide insights into their effectiveness and potential for further improving o1's performance in medical tasks.
Implementation: Include specific examples of the advanced prompts used and quantify their impact on o1's performance across different tasks. Discuss any observed trends or limitations of these techniques.
The section could benefit from a more explicit comparison of o1's performance with previous state-of-the-art models in medical NLP tasks. This would better contextualize the findings and highlight the significance of o1's advancements.
Rationale: Positioning o1's performance within the existing literature would strengthen the paper's contribution and demonstrate the novelty of the findings.
Implementation: Include a brief discussion comparing o1's results with those reported in previous studies using similar datasets or tasks. Quantify the improvements achieved by o1 and discuss the potential reasons for its superior performance.
Table 2 presents the Accuracy (Acc.) and F1 results for several models (o1, GPT-4, GPT-3.5, Meditron-70B, Llama3-8B) across four tasks related to two aspects: Understanding (Concept Recognition) and Reasoning (Clinical Decision Support, Knowledge QA, Medical Calculation). The table highlights o1's results with a gray background and includes average scores for each metric. o1 generally outperforms other models, especially in Knowledge QA and Medical Calculation tasks. For example, o1 achieves 72.6% average F1 score in Concept Recognition, 74.9% average accuracy in Clinical Decision Support, and 84.8% average accuracy in Knowledge QA.
Text: "Results presented in Table 2 demonstrate that o1 outperforms other models on the understanding aspect in most clinical tasks."
Context: This sentence, located in the 'Main Result' subsection, introduces Table 2 to support the claim of o1's superior performance in understanding and reasoning tasks.
Relevance: Table 2 directly supports the main claim of the section by providing quantitative evidence of o1's performance in various medical tasks. It highlights o1's strengths in knowledge understanding and reasoning, contributing to the overall argument that the model is a step closer to an 'AI doctor'.
Table 3 presents the BLEU-1 (B-1) and ROUGE-1 (R-1) results for the same models as in Table 2, focusing on three tasks related to two aspects: Understanding (Text Summary, Concept Recognition) and Reasoning (Clinical Decision Support). o1's results are highlighted. o1 shows competitive performance in text summarization and concept recognition, achieving average ROUGE-1 scores of 31.4% and 32.5%, respectively. For example, in Text Summary, o1 achieves an average ROUGE-1 score of 31.4%, compared to 29.0% for GPT-4 and 27.7% for GPT-3.5.
Text: "Additionally, on the summarization task in Table 3, o1 achieves a 2.4% and 3.7% increase in ROUGE-1 score over GPT-4 and GPT-3.5 (i.e., 31.4% vs. 29.0% vs. 27.7%), demonstrating its enhanced capacity for real-world clinical understanding."
Context: This sentence follows the discussion of Table 2 and introduces Table 3 to further demonstrate o1's enhanced capacity for clinical understanding in summarization tasks.
Relevance: Table 3 complements Table 2 by providing additional evidence of o1's performance using different metrics (BLEU-1 and ROUGE-1) commonly used for evaluating text generation tasks. This further supports the section's argument about o1's enhanced clinical understanding and its potential as an 'AI doctor'.
Table 4 presents the AlignScore and Mauve results for the same models across three tasks and two aspects, similar to Table 3. Higher AlignScore indicates better factual consistency, while higher Mauve indicates better distribution match with human-written text. o1 shows some improvements in Mauve compared to other models, but its AlignScore is generally lower than GPT-4's, suggesting potential issues with hallucination. For example, in Text Summary, o1's average AlignScore is 20.3, while GPT-4's is 21.6.
Text: "We use AlignScore (Zha et al., 2023) to evaluate hallucination in LLMs. In Table 4, the o1 model demonstrates a 1.3% decrease in AlignScore compared to GPT-4 across five text summarization datasets."
Context: This sentence, appearing in the 'Further Analysis' subsection, introduces Table 4 to discuss the issue of hallucination in o1, using AlignScore as a metric.
Relevance: Table 4 is relevant because it addresses the important issue of hallucination, a known weakness of LLMs. By presenting AlignScore and Mauve results, it provides insights into the factual consistency and distribution match of the generated text, adding a crucial dimension to the evaluation of o1's performance.
Figure 4 compares the answers and reasoning provided by o1 and GPT-4 on a question from the LancetQA dataset. The question involves diagnosing a rash in a two-month-old infant. o1 correctly identifies Congenital Syphilis, while GPT-4 incorrectly chooses Congenital Candidiasis. o1's reasoning is shorter and more concise, while GPT-4's explanation includes hallucinated information.
Text: "In addition to delivering higher accuracy, o1 provides more concise and straightforward answers. In the example illustrated in Figure 4, o1 generates shorter interpretations while offering the correct answer."
Context: This sentence, following the discussion of o1's strong reasoning abilities, introduces Figure 4 to provide a specific example comparing o1 and GPT-4's responses on a diagnostic question.
Relevance: Figure 4 strengthens the section's argument about o1's superior reasoning abilities by providing a qualitative example. It visually demonstrates o1's concise and accurate reasoning compared to GPT-4's incorrect answer and hallucinated explanation, further supporting the claim that o1 is getting closer to an 'AI doctor'.
Figure 4 compares the answers and reasoning provided by o1 and GPT-4 on a medical question from the LancetQA dataset. The question involves diagnosing a rash in a two-month-old infant. o1 correctly identifies Congenital Syphilis, providing a concise explanation. GPT-4 incorrectly diagnoses Congenital Candidiasis, offering a longer and less accurate reasoning process.
Text: "In addition to delivering higher accuracy, o1 provides more concise and straightforward answers. In the example illustrated in Figure 4, o1 generates shorter interpretations while offering the correct answer. In contrast, GPT-4 tends to generate hallucinated explanations alongside incorrect answers."
Context: This paragraph discusses o1's improved accuracy and conciseness in comparison to GPT-4, using Figure 4 as a supporting example.
Relevance: Figure 4 supports the claim that o1 not only achieves higher accuracy but also provides more concise and clinically sound reasoning compared to GPT-4. This reinforces the paper's argument for o1's potential as a reliable clinical tool.
Table 5 compares the accuracy of o1, GPT-4, and GPT-3.5 on two agentic benchmarks: AI Hospital and AgentClinic. AI Hospital tasks include Symptom identification, Medical Examination, Diagnostic Results, Diagnostic Rationales, and Treatment Plan. AgentClinic includes MedQA and NEJM subsets. o1 generally performs well, but GPT-4 outperforms it on certain AI Hospital tasks.
Text: "In more complex reasoning scenarios that involve multi-turn conversations and environmental simulations, o1 outperforms both GPT-4 and GPT-3.5 on the AgentClinic benchmark, achieving accuracy gains of at least 15.5% and 10% with scores of 45.5% and 20.0% on its MedQA and NEJM subsets, respectively."
Context: This paragraph discusses o1's performance in complex reasoning scenarios using agentic benchmarks, referencing Table 5 for detailed results.
Relevance: Table 5 provides quantitative evidence of o1's performance in complex, multi-turn medical scenarios, which are crucial for real-world clinical applications. It directly relates to the section's focus on evaluating o1's reasoning abilities.
Table 6 presents the accuracy of o1 and GPT-4, with and without Chain-of-Thought (CoT) prompting, on five knowledge QA datasets: PubMedQA, MedQA, MedMCQA, LancetQA, and NEJMQA. The results show that CoT prompting improves the performance of both models, even though o1 is trained with CoT data.
Text: "o1 was released using chain-of-thought (CoT) data embedding in the training process; however, we found that applying the CoT prompting still enhances o1’s performance on knowledge QA tasks in medicine, as shown in Table 6."
Context: This paragraph discusses the impact of CoT prompting on o1 and GPT-4's performance, referring to Table 6 for detailed results.
Relevance: Table 6 directly addresses the question of whether CoT prompting benefits models already trained with CoT data. This is relevant to the section's focus on analyzing o1's performance and the impact of different prompting strategies.
This discussion section analyzes the implications of o1's performance in the medical domain, highlighting both its potential benefits and drawbacks. It raises concerns about o1's adverse impacts, such as increased decoding time and inconsistent performance across tasks. The section also emphasizes the need for rethinking evaluation metrics for stronger LLMs, advocating for more robust measures beyond traditional metrics like BLEU and ROUGE. Finally, it calls for more reliable prompting techniques adapted to the evolving internal prompting strategies of future LLMs.
The section goes beyond simply praising o1's performance and critically examines its limitations and potential drawbacks, such as increased decoding time and inconsistent performance across tasks.
The section raises important questions about the limitations of current evaluation metrics for advanced LLMs and advocates for the development of more robust measures.
While the section mentions o1's inconsistent performance, it would be stronger with more specific examples and analysis of when and why o1 underperforms.
Rationale: Providing specific examples would make the analysis more concrete and persuasive, allowing readers to better understand the limitations of o1.
Implementation: Select a few representative examples of datasets or tasks where o1 underperforms and provide a detailed analysis of the possible reasons for this underperformance. Consider factors such as task complexity, data characteristics, or limitations of the model's architecture or training data.
The section critiques traditional metrics but could benefit from exploring and suggesting specific alternative metrics that might be more suitable for evaluating advanced LLMs.
Rationale: Suggesting concrete alternative metrics would provide more actionable guidance for future research and contribute to the development of better evaluation practices.
Implementation: Discuss specific alternative metrics that have been proposed in the literature, such as metrics based on semantic similarity, factual consistency, or human evaluation. Analyze the strengths and weaknesses of these alternatives and discuss their potential applicability to evaluating medical LLMs like o1.
The section briefly mentions the need for reliable prompting techniques but could expand on this by discussing specific strategies and their potential benefits for future LLMs.
Rationale: A more detailed discussion of prompting techniques would provide valuable insights for future research and development of LLMs.
Implementation: Discuss specific prompting strategies that could be adapted to future LLMs with internal prompts. Consider techniques like prompt engineering, meta-learning for prompt generation, or incorporating external knowledge into prompts. Analyze the potential benefits and limitations of these strategies in the context of medical applications.
This conclusion summarizes the study's findings on OpenAI's o1 model in the medical domain, emphasizing its progress towards an "AI doctor." It evaluated o1 across three key aspects (understanding, reasoning, and multilingual capabilities) using 37 medical datasets, including two novel ones. The study concludes that o1 shows promise in bridging the gap between AI and human doctors.
The conclusion effectively summarizes the key findings of the study, highlighting the main contributions and implications.
The conclusion emphasizes the potential impact of o1 in the medical field, highlighting its progress towards the goal of an AI doctor.
While the conclusion mentions limitations and future work, it could be more specific about the research questions and methodologies that should be explored in future studies.
Rationale: Providing more concrete suggestions for future research would enhance the impact and value of the study.
Implementation: Instead of just mentioning safety and RAG, elaborate on specific research questions related to safety, such as bias detection, fairness, and explainability. For RAG, discuss how it could be integrated into o1 and what specific benefits it might offer in the medical domain.
The conclusion could expand on the broader implications of the findings, considering the potential impact of o1 on healthcare practices, medical education, and patient care.
Rationale: Discussing the broader implications would enrich the conclusion and provide a more comprehensive perspective on the potential impact of o1 in the medical field.
Implementation: Discuss the potential benefits and challenges of integrating o1 into clinical workflows, such as its role in assisting with diagnosis, treatment planning, or patient education. Consider the ethical implications of using AI in healthcare and how these can be addressed.