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Evaluating medical LLMs with multiple choice questions does not reflect real clinical practice. Satvik Tripathi explains what better evaluation of large language models in healthcare should look like. Satvik Tripathi tells Jason Nagels that clinicians rarely face four tidy options, so multiple choice question answering is a weak way to benchmark medical LLMs. He highlights data leakage: when models are trained on internet data, public benchmarks can end up in training data and inflate performance. His team is building human-annotated internal benchmark datasets and has tested upwards of 20 models, finding that fine-tuned medical models are not always the best performers. Satvik argues for human evaluation of real clinical questions to learn what models do well, noting they excel at creating decision trees but struggle with certain classification tasks. He also raises the open question of how to share private benchmarks with others while protecting them. Want to go deeper on imaging informatics, enterprise imaging and healthcare AI? Check out Nagels Consulting at https://www.nagelsconsulting.com for consulting, training and certification prep (including CIIP). If you found this clip useful, give it a like, subscribe for more conversations with healthcare and medical imaging leaders, and tell us in the comments: how do you think medical AI models should be benchmarked? #MedicalLLM #HealthcareAI #LLM #AIEvaluation #GenerativeAI #ClinicalAI #Radiology #ImagingInformatics #HealthIT #NagelsConsulting TAGS: medical LLM evaluation, evaluating LLMs healthcare, LLM benchmarks, MedQA, data leakage AI, AI benchmarking, human evaluation AI, clinical AI validation, fine-tuned medical models, large language models healthcare, generative AI healthcare, decision trees LLM, healthcare AI, clinical AI, imaging informatics, health IT, Satvik Tripathi, Nagels Consulting

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