A Responsible AI Perspective to implementing Generative AI in Personalized Healthcare: Implications, Challenges and Future Directions

Fagbola, Temitayo Matthew; Dhiman, Aarzoo; Mboli, Julius; Mishra, Bhupesh

Abstract

Generative AI (GenAI) is transforming personalized healthcare by enabling customized treatment plans, advancing drug discovery, and offering targeted diagnostic support. While these advancements offer significant potential, they also present complex ethical and practical challenges. This paper explores the ethical implications and practical challenges associated with integrating GenAI into personalized healthcare, with a focus on the need for comprehensive Responsible AI frameworks. We critically assess existing frameworks, highlighting their limitations in the context of personalized healthcare. Key ethical concerns include algorithmic bias, threats to patient privacy, diminished patient autonomy, and the lack of accountability for AI-driven errors. On a practical level, challenges such as the integration of GenAI with current healthcare systems, the need for high-quality and diverse training data, and issues related to trust, transparency and explainability are examined. Our approach involves a systematic review of recent literature on personalized healthcare, AI ethics, healthcare GenAI applications, and international AI regulatory and governance standards. Our findings indicate that while GenAI holds great promise for improving personalized healthcare outcomes, current frameworks often fail to adequately address healthcare-specific challenges. These gaps include insufficient measures to mitigate bias, inadequate regulation of data privacy, and a lack of clear universally acceptable requirements for explainability in medical AI applications. This review contributes to the ongoing discussion by offering specific recommendations to enhance Responsible AI frameworks. These include fostering interdisciplinary collaboration, improving data governance strategies, and implementing stricter transparency standards as GenAI advancement continues to evolve. We call for continued research and policy development to ensure that GenAI integration in personalized healthcare remains ethical, equitable, and focused on promoting patient welfare without compromising ethical standards.

Citation

Fagbola, T. M., Dhiman, A., Mboli, J., & Mishra, B. (2024, October). A Responsible AI Perspective to implementing Generative AI in Personalized Healthcare: Implications, Challenges and Future Directions. Paper presented at 1st International Workshop on Responsible AI (RAI) for Healthcare and Net Zero, IIT Madras, Chennai, India

• Presentation Conference Type: Conference Paper (unpublished)
• Conference Name: 1st International Workshop on Responsible AI (RAI) for Healthcare and Net Zero
• Start Date: Oct 16, 2024
• End Date: Oct 17, 2024
• Acceptance Date: Sep 25, 2024
• Deposit Date: Sep 30, 2024
• Publicly Available Date: May 28, 2025
• Peer Reviewed: Peer Reviewed
• Keywords: Responsible AI; Generative AI; Conversational AI; Personalized medicine; Healthcare AI; Explainable healthcare; Inclusive Chatbot; Sustainable healthcare; AI Framework
• Public URL: https://hull-repository.worktribe.com/output/4836578
• External URL: https://www.responsibleaihull.com/events/responsibleai-chennai