# Generative AI in Medicine and Healthcare: Moving Beyond the Peak of Inflated Expectations'
## 4 Discussion  
The development of medicine-specific Large Language Models (LLMs) such as Med-PaLM, MEDITRON-70B, Med-Gemini, PH-LLM, and PHIA showcases the rapid advancements and potential of these models to enhance healthcare delivery and personalized health monitoring. These models demonstrate significant improvements in medical reasoning, diagnostic accuracy, and practical applications, offering new possibilities for clinical decision support, patient communication, and educational tools. The integration of Retrieval-Augmented Generation (RAG) and prompt engineering in medical applications, as demonstrated by tools like Aeyeconsult [69] and the custom dermatology AI chatbot built using NVIDIA's 'Chat with RTX' [70], further enhances the capabilities of AI models by combining generative AI with domain-specific knowledge bases. These advancements improve diagnostic accuracy, educational utility, and user experience by providing precise, contextually relevant answers and interactive learning tools. The Surgical Robot Transformer (SRT) is another example of how AI is being used to transform healthcare. Developed by Johns Hopkins University and Stanford University, SRT is a deep learning model that employs transformer architecture to automate surgical tasks through imitation learning. The model learns surgical skills by analyzing visual observations of expert human surgeons performing various tasks, enabling it to mimic their actions and movements with precision. This innovative application of AI has the potential to significantly enhance the field of robotic surgery, improving surgical accuracy and reducing human error [83]. OpenAI's latest LLM model, o1, represents a notable advancement in language model capabilities, incorporating an internalized chain-of-thought technique powered by rein forcement learning strategies. While initially evaluated on general language tasks, a recent study [84] assessed its performance in medical scenarios, including understanding, reasoning, and multilinguality. Using data from 37 medical datasets and two newly constructed question-answering tasks based on professional medical quizzes, o1 demonstrated superior performance, surpassing GPT-4 by 6.2% and 6.6% in accuracy across various tasks. These results suggest that o1's enhanced reasoning abilities significantly improve its capacity to understand medical instructions and address complex clinical scenarios. Nonetheless, the study highlighted areas requiring further refinement, such as hallucinations, inconsistent multilingual capabilities, and the need for more robust evaluation protocols. These findings underscore the importance of iterative development and rigorous testing to optimize LLMs for specialized applications like medicine [84]. Hallucinations remain a significant challenge for LLMs in healthcare, as they can result in the generation of plausible-sounding but incorrect or even harmful information. In this context, the Differential Transformer (Diff Transformer) offers a promising solution to address this issue. Diff Transformer mitigates the tendency of traditional transformer architectures to over-allocate attention to irrelevant context by amplifying relevant information and canceling out noise, thereby fostering the emergence of sparse attention patterns. Experimental results demonstrate its enhanced performance in long-context modeling, key information retrieval, hallucination mitigation, and in-context learning. In healthcare applications, Diff Transformer's potential to reduce hallucinations and improve accuracy positions it as a transformative innovation for advancing LLMs in clinical decision-making, question answering, and summarization tasks. Moreover, its robustness to order permutation strengthens its reliability in real-world medical scenarios, paving the way for more efficient and trustworthy medical AI systems [85]. Despite these promising developments, significant challenges remain.