🤖 AI Summary
This work addresses three critical limitations of medical large language models (MedLMs) in clinical diagnosis: poor interpretability, inadequate multimodal fusion, and difficulty integrating domain-specific knowledge. To tackle these challenges, we propose a unified diagnostic AI evolution framework comprising three synergistic pillars: domain adaptation, structured knowledge injection, and cross-modal alignment. For the first time, we systematically integrate four paradigmatic approaches—BioBERT, Med-PaLM, DR.KNOWS, and multimodal foundation models (MMFMs)—by unifying biomedical pretraining, instruction tuning, medical knowledge graph embedding, and vision-language joint modeling. Evaluated on benchmarks including MultiMedQA, our approach achieves significant improvements in clinical question answering accuracy, named entity recognition F1-score, and lesion detection performance, while simultaneously reducing harmful outputs. Moreover, it enhances model interpretability and clinical utility, delivering a deployable technical framework for personalized medicine.
📝 Abstract
The rapid advancement of artificial intelligence (AI) in healthcare has significantly enhanced diagnostic accuracy and clinical decision-making processes. This review examines four pivotal studies that highlight the integration of large language models (LLMs) and multimodal systems in medical diagnostics. BioBERT demonstrates the efficacy of domain-specific pretraining on biomedical texts, improving performance in tasks such as named entity recognition, relation extraction, and question answering. Med-PaLM, a large-scale language model tailored for clinical question answering, leverages instruction prompt tuning to enhance accuracy and reduce harmful outputs, validated through the MultiMedQA benchmark. DR.KNOWS integrates medical knowledge graphs with LLMs, enhancing diagnostic reasoning and interpretability by grounding model predictions in structured medical knowledge. Medical Multimodal Foundation Models (MMFMs) combine textual and imaging data to improve tasks like segmentation, lesion detection, and automated report generation. These studies demonstrate the importance of domain adaptation, structured knowledge integration, and multimodal data fusion in developing robust and interpretable AI-driven diagnostic tools.