ChatGPT Summarizes Singhal, Nature, July 2023 "LLM and Clinical Knowledge"

 https://www.nature.com/articles/s41586-023-06291-2

Large language models encode clinical knowledge

Karan Singhal, Shekoofeh Azizi, Tao Tu, S. Sara Mahdavi, Jason Wei, Hyung Won Chung, Nathan Scales, Ajay Tanwani, Heather Cole-Lewis, Stephen Pfohl, Perry Payne, Martin Seneviratne, Paul Gamble, Chris Kelly, Abubakr Babiker, Nathanael Schärli, Aakanksha Chowdhery, Philip Mansfield, Dina Demner-Fushman, Blaise Agüera y Arcas, Dale Webster, Greg S. Corrado, Yossi Matias, Katherine Chou, …Vivek Natarajan Show authors

Nature volume 620, pages172–180 (2023)Cite this article

Abstract

Large language models (LLMs) have demonstrated impressive capabilities, but the bar for clinical applications is high. Attempts to assess the clinical knowledge of models typically rely on automated evaluations based on limited benchmarks. Here, to address these limitations, we present MultiMedQA, a benchmark combining six existing medical question answering datasets spanning professional medicine, research and consumer queries and a new dataset of medical questions searched online, HealthSearchQA. We propose a human evaluation framework for model answers along multiple axes including factuality, comprehension, reasoning, possible harm and bias. In addition, we evaluate Pathways Language Model1 (PaLM, a 540-billion parameter LLM) and its instruction-tuned variant, Flan-PaLM2 on MultiMedQA. 

Using a combination of prompting strategies, Flan-PaLM achieves state-of-the-art accuracy on every MultiMedQA multiple-choice dataset (MedQA3, MedMCQA4, PubMedQA5 and Measuring Massive Multitask Language Understanding (MMLU) clinical topics6), including 67.6% accuracy on MedQA (US Medical Licensing Exam-style questions), surpassing the prior state of the art by more than 17%. However, human evaluation reveals key gaps. To resolve this, we introduce instruction prompt tuning, a parameter-efficient approach for aligning LLMs to new domains using a few exemplars. 

The resulting model, Med-PaLM, performs encouragingly, but remains inferior to clinicians. We show that comprehension, knowledge recall and reasoning improve with model scale and instruction prompt tuning, suggesting the potential utility of LLMs in medicine. Our human evaluations reveal limitations of today’s models, reinforcing the importance of both evaluation frameworks and method development in creating safe, helpful LLMs for clinical applications.

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CHATGPT SUMMARY

MINI SUMMARY

The study explores the application of large language models (LLMs) to medical question-answering. Scaling Flan-PaLM from 8B to 540B significantly improves its performance, and instruction prompt tuning enhances alignment with medical expertise. However, LLMs introduce biases and potential harms. Lay user assessments show improvements, but the model falls short of clinician-generated responses. Ethical implications emphasize fairness, equity, and rigorous evaluation for responsible integration into healthcare.

LONG SUMMARY

The article discusses the potential and challenges of utilizing large language models (LLMs) for medical question-answering, highlighting the complex landscape of applying these models to the healthcare domain. The study focuses on a variant of the PaLM model, named Flan-PaLM, and presents extensive research on its performance in answering medical questions. The authors explore various aspects of model performance, human evaluations, and ethical considerations.

Key Points:

Performance Improvement with Scaling and Instruction Prompt Tuning: The study demonstrates that scaling up the Flan-PaLM model from 8B to 540B significantly improves its performance in answering medical questions, as evidenced by an approximate 2× increase in accuracy. The results also emphasize the importance of instruction prompt tuning, showing that Flan-PaLM's performance surpasses the base PaLM model across multiple-choice datasets.

Effective Knowledge Encoding: Flan-PaLM's strong performance on multiple-choice medical question-answering tasks suggests an emergent ability to encode medical knowledge, even without dataset-specific fine-tuning. Despite improvements with scale, the study asserts that scaling alone does not explain the model's success.

Human Evaluation on Medical Expertise: Human evaluation involving clinicians indicates that while Flan-PaLM performs well, it falls short of human expertise in various medical aspects. The Med-PaLM variant, tuned with medical instruction prompts, narrows the performance gap with clinicians, demonstrating the potential of instruction fine-tuning for medical alignment.

Bias, Harm, and Equity: The study acknowledges the potential harms and biases that LLMs can introduce to medical responses. Flan-PaLM was found to contain biased information and had potential to cause harm in certain cases. Instruction prompt tuning with Med-PaLM reduced these risks, indicating the model's improved alignment with medical consensus and ethical considerations.

Lay User Assessment: Lay user evaluations showed that Med-PaLM's answers were perceived as more helpful and relevant to user intent compared to Flan-PaLM, but still lagged behind clinician-generated responses.

Ethical and Future Considerations: The authors highlight the ethical implications of deploying LLMs in healthcare and emphasize the need for rigorous quality assessment, evaluation of biases, and mitigation of potential harms. They also stress the importance of interdisciplinary collaboration to ensure fairness and equity in healthcare AI applications.

Methods and Results:

The study employs the Flan-PaLM model, an LLM variant, to evaluate its performance in medical question-answering tasks. The authors observe significant performance improvements when scaling up the model's size from 8B to 540B, especially in medical-related tasks. Additionally, they highlight the impact of instruction prompt tuning, which enhances the alignment of model responses with medical expertise.

Human evaluations involving clinicians and lay users uncover the model's limitations and strengths. Flan-PaLM's output contains biased and potentially harmful information, but these issues are mitigated with Med-PaLM's instruction prompt tuning. The study emphasizes the need to comprehensively evaluate bias and fairness, considering the complexities of healthcare disparities and social contexts.

Conclusions:

The study underscores the transformative potential of LLMs in medical question-answering while cautioning about the complexities of deploying such technologies in the healthcare domain. While scaling and instruction prompt tuning can enhance performance, addressing biases, ensuring safety, and improving alignment with medical expertise are crucial. The authors urge interdisciplinary collaboration and ethical considerations to responsibly advance the translation of these research findings into practical healthcare improvements. This research serves as a catalyst for discussions among AI researchers, clinicians, policymakers, and other stakeholders, with the shared goal of harnessing LLMs for medical applications while minimizing potential risks.

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I asked about any areas where AI might excel (hot spots).  

In the comprehensive evaluation of AI model performance and clinical human expertise, the AI model exhibited strengths, but it did not consistently surpass clinical experts. The AI model's advantages included its ability to process and generate responses rapidly and its potential to offer insights from a wide array of medical sources. However, the clinical experts outperformed the AI model in terms of alignment with scientific and clinical consensus, absence of incorrect or biased content, and overall safety in providing medical information. Despite its capabilities, the AI model's performance remained below that of human clinicians in several critical aspects, highlighting the complexities of applying AI to the medical domain.

AI's worst areas?

Yes, the AI model exhibited certain limitations and shortcomings in its capabilities. Notably, the AI model struggled in providing responses that were consistently aligned with current scientific and clinical consensus. It sometimes generated answers that deviated from established medical knowledge, which is a crucial aspect for accurate medical information dissemination. Additionally, the AI model had challenges in detecting and addressing potential biases, both in terms of demographic information and broader medical contexts. This underscores the importance of ensuring fairness and equity in AI-generated medical information. Furthermore, while the AI model demonstrated improvements in its responses through instruction prompt tuning, it still fell short of clinical experts in aspects such as generating responses that were devoid of incorrect or biased content, and ensuring patient safety by minimizing potential harms in the information it provided. These limitations emphasize the need for continuous refinement and careful assessment when deploying AI models in the critical healthcare domain.