{"id":4069,"date":"2024-04-24T07:29:04","date_gmt":"2024-04-24T07:29:04","guid":{"rendered":"https:\/\/favtutor.com\/articles\/?p=4069"},"modified":"2024-04-24T07:29:06","modified_gmt":"2024-04-24T07:29:06","slug":"open-medical-llm-leaderboard","status":"publish","type":"post","link":"https:\/\/favtutor.com\/articles\/open-medical-llm-leaderboard\/","title":{"rendered":"Open Medical-LLM Leaderboard Will Rank LLMs for Healthcare"},"content":{"rendered":"\n<p>LLMs have been rapidly advancing in the past 2 years. They also have amazing potential for the healthcare industry, but to evaluate them correctly, something was needed, and now we have it: Open Medical-LLM Leaderboard.<\/p>\n\n\n\n<p><strong>Highlights:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Researchers at the Open Life Science AI have released a new leaderboard for evaluation of Medical LLMs<\/li>\n\n\n\n<li>The leaderboard checks and ranks each Medical LLM, based on its knowledge and question-answering capabilities.<\/li>\n\n\n\n<li>It a collection of all major medical evaluation parameters like the MedQA and the MedMCQA datasets.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>New Open Medical-LLM Leaderboard<\/strong><\/h2>\n\n\n\n<p>LLMs  like GPT-3, GPT-4, and Med-PaLM 2, along with the growth in electronic health records, have made a significant progress for use of AI in the healthcare sector.<\/p>\n\n\n\n<p>One of the most promising applications of LLMs lies in medical question-answering (QA) systems. By leveraging the vast knowledge encoded within these models, healthcare professionals can quickly obtain accurate and relevant information, streamlining decision-making processes and improving diagnostic accuracy. <\/p>\n\n\n\n<p>However, many challenges need to be overcome before the medical community can widely adopt LLMs. A  tiny error could put lives in jeopardy. The accuracy of information provided by LLMs has always been in question due to the well-publicised hallucination issues. <\/p>\n\n\n\n<p>So, a benchmark model was something that the industry needed to make such LLMs a practical tool for real-life tasks. <\/p>\n\n\n\n<p><strong>Researchers at the Open Life Science AI non-profit have introduced a new series of benchmarks to evaluate models on parameters specific to the healthcare industry, where they are ranked on the Open Medical-LLM Leaderboard.<\/strong><\/p>\n\n\n\n<p>The ranking are based on of each model&#8217;s medical knowledge and question-answering capabilities. Here&#8217;s how they explained in their <a href=\"https:\/\/huggingface.co\/blog\/leaderboard-medicalllm\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">official announcement<\/a>:<\/p>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p>&#8220;The Open Medical LLM Leaderboard aims to track, rank and evaluate the performance of large language models (LLMs) on medical question answering tasks. It evaluates LLMs across a diverse array of medical datasets, including MedQA (USMLE), PubMedQA, MedMCQA, and subsets of MMLU related to medicine and biology. The leaderboard offers a comprehensive assessment of each model&#8217;s medical knowledge and question answering capabilities.&#8221;<\/p>\n<\/blockquote>\n\n\n\n<p>The datasets cover various aspects of medicine such as general medical knowledge, clinical knowledge, anatomy, genetics, and more. They contain multiple-choice and open-ended questions that require medical reasoning and understanding.\u201d The researchers clarified in their post on huggingface.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Evaluation Parameters in the Leaderboard<\/strong><\/h2>\n\n\n\n<p>The medical LLM leaderboard is not an entirely brand new benchmark, but rather a collection of all major medical evaluation parameters like the MedQA and the MedMCQA datasets.<\/p>\n\n\n\n<p>Here is the full list of datasets on which the accuracy of LLMs is being evaluated:<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>MedQA<\/strong><\/h3>\n\n\n\n<p>The MedQA dataset consists of multiple-choice questions from the United States Medical Licensing Examination (USMLE). It covers general medical knowledge and includes 11,450 questions in the development set and 1,273 questions in the test set. <\/p>\n\n\n\n<p>Each question has 4 or 5 answer choices, and the dataset is designed to assess the medical knowledge and reasoning skills required for medical licensure in the United States.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>MedMCQA<\/strong><\/h3>\n\n\n\n<p>MedMCQA is a large-scale multiple-choice QA dataset derived from Indian medical entrance examinations (AIIMS\/NEET). It covers 2.4k healthcare topics and 21 medical subjects, with over 187,000 questions in the development set and 6,100 questions in the test set.<\/p>\n\n\n\n<p>Each question has 4 answer choices and is accompanied by an explanation. MedMCQA evaluates a model&#8217;s general medical knowledge and reasoning capabilities.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>PubMedQA<\/strong><\/h3>\n\n\n\n<p>PubMedQA is a closed-domain QA dataset, In which each question can be answered by looking at an associated context (PubMed abstract). It consists of 1,000 expert-labeled question-answer pairs. Each question is accompanied by a PubMed abstract as context, and the task is to provide a yes\/no\/maybe answer based on the information in the abstract.<\/p>\n\n\n\n<p>The dataset is split into 500 questions for development and 500 for testing. PubMedQA assesses a model&#8217;s ability to comprehend and reason over scientific biomedical literature.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>MMLU Subsets (Medicine and Biology)<\/strong><\/h3>\n\n\n\n<p>The MMLU benchmark (Measuring Massive Multitask Language Understanding) includes multiple-choice questions from various domains. For the Open Medical-LLM Leaderboard, we focus on the subsets most relevant to medical knowledge:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Clinical Knowledge:<\/strong> 265 questions assessing clinical knowledge and decision-making skills.<\/li>\n\n\n\n<li><strong>Medical Genetics:<\/strong> 100 questions covering topics related to medical genetics.<\/li>\n\n\n\n<li><strong>Anatomy:<\/strong> 135 questions evaluating the knowledge of human anatomy.<\/li>\n\n\n\n<li><strong>Professional Medicine:<\/strong> 272 questions assessing knowledge required for medical professionals.<\/li>\n\n\n\n<li><strong>College Biology:<\/strong> 144 questions covering college-level biology concepts.<\/li>\n\n\n\n<li><strong>College Medicine:<\/strong> 173 questions assessing college-level medical knowledge.<\/li>\n<\/ul>\n\n\n\n<p>Each MMLU subset consists of multiple-choice questions with 4 answer options and is designed to evaluate a model&#8217;s understanding of specific medical and biological domains.<\/p>\n\n\n\n<p>Also, read about the <a href=\"https:\/\/favtutor.com\/articles\/gpt-4v-rag-clinical-trial-screening\/\">Study shows that GPT-4V with RAG<\/a> offers numerous benefits in Clinical Trial Screening.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Conclusion<\/strong><\/h2>\n\n\n\n<p>This Open Medical-LLM Leaderboard is a valiant effort to evaluate models, medical professionals across the world have declared the wide disparity between textbook or dataset situations and actual clinical cases.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Let&#8217;s discuss the Open Medical-LLM leaderboard for evaluating Medical LLMs,  based on their knowledge and question-answering capabilities.<\/p>\n","protected":false},"author":20,"featured_media":4070,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jnews-multi-image_gallery":[],"jnews_single_post":null,"jnews_primary_category":{"id":"","hide":""},"footnotes":""},"categories":[57],"tags":[56,72],"class_list":["post-4069","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai","tag-ai","tag-llm"],"_links":{"self":[{"href":"https:\/\/favtutor.com\/articles\/wp-json\/wp\/v2\/posts\/4069","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/favtutor.com\/articles\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/favtutor.com\/articles\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/favtutor.com\/articles\/wp-json\/wp\/v2\/users\/20"}],"replies":[{"embeddable":true,"href":"https:\/\/favtutor.com\/articles\/wp-json\/wp\/v2\/comments?post=4069"}],"version-history":[{"count":2,"href":"https:\/\/favtutor.com\/articles\/wp-json\/wp\/v2\/posts\/4069\/revisions"}],"predecessor-version":[{"id":4073,"href":"https:\/\/favtutor.com\/articles\/wp-json\/wp\/v2\/posts\/4069\/revisions\/4073"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/favtutor.com\/articles\/wp-json\/wp\/v2\/media\/4070"}],"wp:attachment":[{"href":"https:\/\/favtutor.com\/articles\/wp-json\/wp\/v2\/media?parent=4069"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/favtutor.com\/articles\/wp-json\/wp\/v2\/categories?post=4069"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/favtutor.com\/articles\/wp-json\/wp\/v2\/tags?post=4069"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}