Corresponding Manager: Sara Jahanmir (sara.jahanmir@neoma-bs.fr)
Track Manager(s): Sara Jahanmir, Anna Bastone, Annaluce Mandiello
Description
Artificial Intelligence (AI) is transforming healthcare with innovations such as predictive analytics, clinical decision support, and automated processes. Yet despite significant potential, ethical, technical, personal and organizational challenges continue to hinder widespread adoption of AI tools in healthcare. This track examines AI’s journey from development to clinical impact, emphasizing real-world integration and stakeholder collaboration. We welcome theoretical and empirical studies that explore the adoption of AI in healthcare across (but not limited to) the following six interconnected themes.
(1) trustworthy AI development, emphasizing transparency, validation, and bias mitigation; (2) clinicians’ adoption and integration of AI into workflows, clinical reasoning, and decision-making; (3) the critical role of technicians in infrastructure, maintenance, scalability, and cybersecurity; (4) the influence of hospital managers and leaders in fostering innovation cultures and managing organizational change; (5) patients’ trust, expectations, digital literacy, and participation in AI-influenced care; and (6) insurers’ impact on diffusion, reimbursement, and regulatory frameworks that affect the adoption of AI in healthcare.
Cross-cutting issues such as ethics, governance, policy, equity, and global disparities also shape the adoption processes. Together, these perspectives trace the journey of AI from development (Theme 1), through implementation and organizational alignment (Themes 2–4), to its ultimate impact on patients (Theme 5), the broader healthcare system, and transformative industry-wide effects (Theme 6).
Keywords
AI in Healthcare, Technology Diffusion, Digital Transformation, Ethics of AI, Human-Centered Design
Key References
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Davenport, T., & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future Healthcare Journal, 6(2), 94-98.
He, J., Baxter, S. L., Xu, J., Xu, J., Zhou, X., & Zhang, K. (2019). The practical implementation of artificial intelligence technologies in medicine. Nature Medicine, 25(1), 30–36.
Kelly, C. J., Karthikesalingam, A., Suleyman, M., Corrado, G., & King, D. (2019). Key challenges for delivering clinical impact with artificial intelligence. BMC Medicine, 17(1), 195.
Longoni, C., Bonezzi, A., & Morewedge, C. K. (2019). Resistance to medical artificial intelligence. Journal of Consumer Research, 46(4), 629-650.
Morley, J., Machado, C. C., Burr, C., Cowls, J., Joshi, I., Taddeo, M., & Floridi, L. (2020). The ethics of AI in health care: a mapping review. Social Science & Medicine, 260, 113172.
Reddy, S., Allan, S., Coghlan, S., & Cooper, P. (2020). A governance model for the application of AI in health care. Journal of the American Medical Informatics Association, 27(3), 491-497.
Shortliffe, E. H., & Sepúlveda, M. J. (2018). Clinical decision support in the era of artificial intelligence. Jama, 320(21), 2199-2200.
Topol, E. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56.
Wiens, J., Saria, S., Sendak, M., Ghassemi, M., Liu, V. X., Doshi-Velez, F., … & Goldenberg, A. (2019). Do no harm: a roadmap for responsible machine learning for health care. Nature Medicine, 25(9), 1337-1340.