# Generative AI in Medicine and Healthcare: Moving Beyond the Peak of Inflated Expectations'
## 3 Applications and Implications of Generative AI and LLMs in Medicine
### Medicine-Specific LLMs
This underscores the potential of integrating wearable device data into AI models for personalized health recommendations and monitoring, although further development and evaluation are necessary for application in safety-critical personal health domains. Around the same time in June 2024, the Personal Health Insights Agent (PHIA) was introduced, also by Google researchers [78]. PHIA is an agent system leveraging LLM capabilities for analyzing and interpreting wearable health data to generate personalized health insights. Utilizing iterative reasoning, code generation, and web search, PHIA addresses personal health queries effectively. This model was evaluated using two benchmark datasets comprising over 4000 health insights questions for objective and open-ended evaluation. Human and expert evaluations demonstrated PHIA's accuracy in addressing factual and open-ended health queries, outperforming standard LLM baselines. It accurately addressed over 84% of factual numerical questions and more than 83% of crowdsourced open-ended questions, providing personalized health insights that can serve as an aid to individuals in interpreting their wearable data and potentially improving their health behaviors. The results highlight the potential for LLM agents to advance behavioral health, making personalized wellness regimens more accessible. In a randomized study assessing the differential diagnosis (DDx) accuracy for 302 NEJM (New England Journal of Medicine) Clinicopathological Conference (CPC) series case reports, a specialized LLM (Google Med-PaLM 2) was compared with twenty physicians (US board-certified internists with a median of 9 years of experience). Each case report was assessed by two clinicians randomized to one of two assistive arms: search engines and standard medical resources, or LLM assistance in addition to these tools. Clinicians in both groups also provided a baseline, unassisted DDx prior to using their assigned tools. The study revealed that Med-PaLM 2 performed better on its own than unassisted clinicians. Additionally, when comparing the two groups of assisted clinicians, those supported by Med-PaLM 2 achieved a higher DDx quality score, highlighting the growing potential of LLMs in supporting complex medical decision-making tasks [79]. While general medicine-specific LLMs like Med-PaLM 2 and Med-Gemini have shown remarkable advancements, there is a growing focus on developing LLMs tailored to specific medical disciplines. These medical-specialty-specific LLMs are designed to address the unique challenges and requirements of distinct specialties, providing a more precise and reliable toolset for healthcare professionals in those domains. A notable recently released model, Harrison.rad.1 by Harrison.ai, is a radiology-specific multimodal LLM designed to address the increasing complexity and workload in radiology [80]. Trained on millions of anonymized DICOM images and radiology reports, Harrison.rad.1 excels at interpreting X-ray images across modalities, generating detailed reports, and incorporating clinical history and patient context. It outperformed other models with 82% accuracy on the VQA-Rad benchmark and achieved an unprecedented 85.67% on the FRCR 2B Rapids exam, exceeding the performance of many radiologists. With its focus on clinical accuracy, factual correctness, and embedded safety measures, Harrison.rad.1 sets a new standard for radiology-focused AI. The Clinical Histopathology Imaging Evaluation Foundation (CHIEF) model is a pathology-specific AI designed to improve cancer diagnosis and prognosis prediction through histopathology image analysis [81]. CHIEF employs two advanced pretraining strategies: unsupervised pretraining to identify features at the tile level and weakly supervised pretraining to recognize patterns across whole-slide images.