Summary of Gemini Goes to Med School: Exploring the Capabilities Of Multimodal Large Language Models on Medical Challenge Problems & Hallucinations, by Ankit Pal et al.
Gemini Goes to Med School: Exploring the Capabilities of Multimodal Large Language Models on Medical Challenge Problems & Hallucinations
by Ankit Pal, Malaikannan Sankarasubbu
First submitted to arxiv on: 10 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper comprehensively evaluates the safety and effectiveness of large language models (LLMs) in healthcare. The study compares open-source LLMs with Google’s new multimodal LLM, Gemini, across various medical tasks such as medical reasoning, hallucination detection, and Medical Visual Question Answering. While Gemini shows competence, it lags behind state-of-the-art models like MedPaLM 2 and GPT-4 in diagnostic accuracy. The analysis reveals that Gemini is highly susceptible to hallucinations, overconfidence, and knowledge gaps, indicating potential risks if deployed uncritically. Gemini’s performance varies across medical subjects and test types, with a significant accuracy gap compared to GPT-4V on the medical VQA dataset. To mitigate these risks, the study proposes prompting strategies that improve performance. Additionally, it releases a Python module for medical LLM evaluation and establishes a dedicated leaderboard on Hugging Face for medical domain LLMs. This comprehensive evaluation and analysis provide actionable feedback for developers and clinicians, highlighting the importance of rigorous testing before deploying LLMs in healthcare. The research aims to facilitate future development by releasing a Python module for medical LLM evaluation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models have the potential to help doctors and researchers make better decisions. But it’s crucial to test these models thoroughly to make sure they’re safe and accurate. This study did just that, comparing Google’s new multimodal LLM, Gemini, with other open-source models. While Gemini showed some promise, it wasn’t as good as some of the state-of-the-art models in certain tasks. The analysis also found that Gemini had a tendency to make mistakes, especially when dealing with complex medical questions. To improve these models, researchers proposed special instructions or “prompting” strategies that can help them perform better. The study’s findings have important implications for how we use large language models in healthcare. It highlights the need for careful testing and evaluation before deploying these models in real-world applications. By releasing a special Python module and establishing a leaderboard for medical domain LLMs, this research aims to facilitate future development and improve the accuracy of medical decision-making. |
Keywords
* Artificial intelligence * Gemini * Gpt * Hallucination * Prompting * Question answering
