Bhuyan Soumitra S, Sateesh Vidyoth, Mukul Naya, Galvankar Alay, Mahmood Asos, Nauman Muhammad, Rai Akash, Bordoloi Kahuwa, Basu Urmi, Samuel Jim
Edward J. Bloustein School of Planning and Public Policy, Rutgers, The State University of New Jersey, 255, Civic Square Building 33 Livingston Ave #400, New Brunswick, NJ, 08901, USA.
School of Social Policy, Rice University, Houston, TX, USA.
J Med Syst. 2025 Jan 16;49(1):10. doi: 10.1007/s10916-024-02136-1.
Generative Artificial Intelligence (Gen AI) has transformative potential in healthcare to enhance patient care, personalize treatment options, train healthcare professionals, and advance medical research. This paper examines various clinical and non-clinical applications of Gen AI. In clinical settings, Gen AI supports the creation of customized treatment plans, generation of synthetic data, analysis of medical images, nursing workflow management, risk prediction, pandemic preparedness, and population health management. By automating administrative tasks such as medical documentations, Gen AI has the potential to reduce clinician burnout, freeing more time for direct patient care. Furthermore, application of Gen AI may enhance surgical outcomes by providing real-time feedback and automation of certain tasks in operating rooms. The generation of synthetic data opens new avenues for model training for diseases and simulation, enhancing research capabilities and improving predictive accuracy. In non-clinical contexts, Gen AI improves medical education, public relations, revenue cycle management, healthcare marketing etc. Its capacity for continuous learning and adaptation enables it to drive ongoing improvements in clinical and operational efficiencies, making healthcare delivery more proactive, predictive, and precise.
生成式人工智能(Gen AI)在医疗保健领域具有变革潜力,可提升患者护理水平、使治疗方案个性化、培训医疗保健专业人员并推动医学研究发展。本文探讨了Gen AI的各种临床和非临床应用。在临床环境中,Gen AI支持制定定制化治疗方案、生成合成数据、分析医学图像、管理护理工作流程、进行风险预测、做好大流行防范以及开展人群健康管理。通过自动化诸如医疗文档记录等管理任务,Gen AI有潜力减轻临床医生的职业倦怠,腾出更多时间用于直接的患者护理。此外,Gen AI的应用可通过提供实时反馈和实现手术室某些任务的自动化来提高手术效果。合成数据的生成开辟了疾病模型训练和模拟的新途径,增强了研究能力并提高了预测准确性。在非临床背景下,Gen AI改善医学教育、公共关系、收入周期管理、医疗保健营销等。其持续学习和适应能力使其能够推动临床和运营效率的不断提高,使医疗保健服务更加主动、可预测且精确。