# Generative AI in Medicine and Healthcare: Promises, Opportunities and Challenges
## 3 Current Efforts of Applying Generative AI and LLMs in Medicine and Healthcare
### Professional Education  
A UNESCO quick start guide published in April 2023 describes a number of potential use cases of generative AI, such as ChatGPT, in higher education, including application examples in teaching and learning and academic research [41]. Although focused on higher education in general with no specific mention of medical education, many of these applications can be adapted to the specific settings of medical education at undergraduate, postgraduate and continuing medical education (CME) levels (and even to patient education and health education of the general public).  
The second author of this article was recently guest editing a theme issue for _JMIR Medical Education_ on the topic of 'ChatGPT and Generative Language Models in Medical Education'. The issue attracted dozens of submissions in a few months, with more than a dozen of these already published online as of 31 July 2023, covering a range of generative AI topics in medical and professional education [42].  
Unlearn.AI [43] utilizes generative AI to create \"digital twins\" of individual patients, offering a comprehensive model of potential health outcomes under different scenarios. Healthcare professionals can personalize treatment plans, monitor patient progress, and make informed decisions for improved patient outcomes. The digital twins simulate the effects of various treatments on a patient's biology using real-world data, aiding in personalized treatment plans and clinical research. In hematology, for instance, clinicians can use digital twins to simulate disease progression under different therapies, guiding treatment choices. Additionally, digital twins optimize clinical trials, providing insights without large control groups. Unlearn. AI's approach streamlines processes, supporting clinician education and enhancing patient follow-up. The study by [PERSON] et al. also demonstrated accuracy in capturing disease progression, making digital twins a valuable tool in advancing healthcare practices [44].  
As patients increasingly record their doctor's visits, research has been underway to use these transcripts to gain insights for patients and extract structured data for enriching EHRs. Abridge [45] is a digital tool that utilizes generative AI to document medical dialogues, saving physicians from manual note-taking. Abridge plays a vital role in patient education by sending after-visit summaries to patients through its consumer app, aiming to increase their engagement and adherence to care. In cases like polycythemia, patients may struggle to remember all the details discussed during their appointments. Abridge tackles this problem by providing a comprehensive transcript of the conversation for patients to review later. The platform also highlights essential information from the dialogue and simplifies complex medical jargon into more understandable language. This ensures that patients have a clear comprehension of their diagnosis, treatment choices, and future actions, ultimately promoting better patient adherence and positive health outcomes.  
Additionally, [PERSON] et al. presented end-to-end methods for generating long and semi-structured clinical summaries called SOAP notes from clinical conversations in collaboration with Abridge [46]. Their approach leveraged a unique corpus of transcripts and associated SOAP notes. The methods focus on decomposing summarization tasks into extractive and abstractive subtasks, shifting the workload progressively from the abstractive to the extractive component.