Large language models in healthcare: a systematic evaluation on medical Q/A datasets

This study systematically evaluates the performance of state-of-the-art large language models (LLMs) in medical and healthcare applications, focusing on their accuracy in answering domain-specific questions. Using benchmark medical question-answering datasets-PubMedQA, MedQA, and MedMCQA-we assess a diverse set of LLMs, including GPT-4, Med-PaLM-2, OpenBioLLM, BioMistral, MediTron, MedAlpaca, and AlpaCare. Our analysis highlights the varying capabilities of these models across different datasets, emphasizing the impact of model scale, domain-specific fine-tuning, and dataset-specific challenges. Larger models such as OpenBioLLM-70B and Med-PaLM-2, consistently outperformed smaller models, showcasing the benefits of extensive training data and computational resources. However, smaller models, like BioMistral-7B, demonstrated competitive performance on specific datasets, suggesting their potential for resource-constrained environments. Beyond accuracy, we explore the broader implications of LLMs in healthcare, including their applications in medical diagnosis, patient care, clinical decision support, and drug discovery. Despite their promise, LLMs face critical challenges, such as the need for explainability, robust data security, bias mitigation, and hallucination reduction. We conclude that while challenges remain, LLMs hold significant potential to transform healthcare by enhancing efficiency, improving patient outcomes, and facilitating advancements in medical research. Addressing the limitations and promoting responsible innovation will be essential to unlocking their full potential for a patient-centered and equitable healthcare future.