# 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  
The development of medicine-specific LLMs has significantly advanced the field of medical AI, offering specialized capabilities for clinical and personal health applications. This subsection reviews several notable models, their development, performance, and potential implications for healthcare. Med-PaLM [74], introduced by Google Research in December 2022, laid the groundwork for specialized medical LLMs. Following this, Med-PaLM 2 [59] was also developed by Google Research, with the model published in March 2023. Building on its predecessor, Med-PaLM 2 incorporated medical domain-specific fine-tuning and a novel ensemble refinement prompting strategy to enhance its medical reasoning capabilities. It achieved state-of-the-art results on several medical question-answering benchmarks, including a significant improvement over its predecessor. It scored 86.5% on the MedQA dataset (a multiple-choice question answering based on the United States Medical Licensing Examination (USMLE)), outperforming previous models by a notable margin. Human evaluations indicated that Med-PaLM 2's answers were preferred over physician-generated answers on multiple axes relevant to clinical utility, such as factuality, reasoning, and low likelihood of harm. The model also showed substantial improvements in answering complex medical questions and demonstrated better performance and lower risk of harm compared to its predecessor when tested on adversarial datasets. Despite these findings, the study cautioned that further validation and safety studies are necessary before applying the model to real-world settings [59]. Published in November 2023, MEDITRON-70B [75] is an open-source suite of LLMs tailored for the medical domain, available in two versions with 7 billion and 70 billion parameters. Built on Meta's Llama-2, these models are pretrained on a comprehensive medical corpus, including PubMed articles and medical guidelines. MEDITRON-70B outperforms several state-of-the-art baselines, including GPT-3.5 and Med-PaLM, showing significant gains in medical reasoning tasks. However, despite its strong performance, its creators, [PERSON] et al. [75], advised against using MEDITRON in clinical applications without further testing due to safety concerns. In April 2024, Google DeepMind introduced the Med-Gemini family of models [76], offering highly capable multimodal models specialized in medicine. Med-Gemini models achieve state-of-the-art performance on 10 out of 14 medical benchmarks, including a 91.1% accuracy on the MedQA (USMLE) benchmark, surpassing previous models like Med-PaLM 2. These models excel in handling complex medical tasks involving text, images, and long-context data, outperforming GPT-4V in several benchmarks. Med-Gemini demonstrates practical applications in generating medical summaries, referral letters, and engaging in multimodal medical dialogues. The models have shown potential to assist in clinical decision-making, medical education, and research, although further rigorous evaluation is needed before deployment in real-world clinical settings. Introduced in June 2024, Google's Personal Health Large Language Model (PHLLM) [77] is a version of Gemini fine-tuned for text understanding and reasoning over numerical time-series personal health data, particularly focusing on sleep and fitness applications. This model integrates mobile and wearable device data, often neglected in clinical settings, for continuous and longitudinal personal health monitoring. PH-LLM was evaluated on three benchmark datasets for personalized insights and recommendations, expert domain knowledge, and prediction of self-reported sleep quality outcomes. Working with domain experts, its creators developed 857 case studies in sleep and fitness to demonstrate model's capabilities. The model performed comparably to human experts in fitness and showed significant improvements in sleep insights after fine-tuning, achieving 79% on sleep medicine exams and 88% on fitness exams, surpassing human expert average scores.