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This research explores the application of generative AI techniques in healthcare to address three significant areas: enhancing electronic health records (EHRs) through automated conversation summarization, simplifying complex medical language into patient-friendly summaries, and providing personalized care recommendations using data from smartwatches and wearables. In the first part, we propose a technical framework for utilizing generative AI to listen to conversations during healthcare appointments and generate concise summaries for inclusion in EHRs. The process involves speech recognition, natural language processing (NLP), named entity recognition (NER), contextual understanding, text summarization, and seamless integration with EHR systems. The implementation of such a system requires rigorous evaluation, training data, and adherence to healthcare regulations. The second part focuses on simplifying complex medical language into summaries that patients can understand. We present a technical sequence flow that involves data collection, preprocessing, training data preparation, model selection, architecture, training, evaluation, fine-tuning, deployment, user interaction, summary generation, output presentation, and feedback iteration. By employing generative AI models trained on medical documents, patients can access simplified and understandable summaries, improving patient education and communication in healthcare settings. Lastly, we explore the utilization of generative AI for personalized care recommendations using data from smartwatches and wearables. Its technical sequence flow encompasses data …