Generative Artificial Intelligence Use in Healthcare: Opportunities for Clinical Excellence and Administrative Efficiency.

Journal: Journal of medical systems
PMID: 39820845

Abstract

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.

Authors

  • Soumitra S Bhuyan
    School of Planning and Public Policy, Rutgers University-New Brunswick, New York, New York, USA.
  • Vidyoth Sateesh
    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.
  • Naya Mukul
    School of Social Policy, Rice University, Houston, TX, USA.
  • Alay Galvankar
    Biotechnology High School, Freehold, NJ, USA.
  • Asos Mahmood
    Center for Health System Improvement, College of Medicine, The University of Tennessee Health Science Center, Memphis, TN, USA.
  • Muhammad Nauman
    Department of Computer Science, University of Okara, Okara, Pakistan.
  • Akash Rai
    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.
  • Kahuwa Bordoloi
    Department of Psychology and Counselling, St. Joseph's University, Bangalore, India.
  • Urmi Basu
    Insight Biopharma, Princeton, NJ, USA.
  • Jim Samuel
    Rutgers, The State University of New Jersey, New Brunswick, NJ, United States.

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