As artificial intelligence becomes more deeply integrated into healthcare, experts are urging organisations to strengthen AI governance through better collaboration, stronger data governance and practical risk management. During a healthcare AI forum, industry leaders said fragmented regulations, legal challenges and poor-quality data remain major barriers to safe and trusted AI adoption.
Panelists stressed that AI governance should involve clinical, operational and IT leaders from the beginning. Dr. Leeda Rashid from the U.S. Food and Drug Administration’s Digital Health Center of Excellence said, “We need a multi-stakeholder approach.”
Experts highlighted the growing complexity of healthcare regulations, especially as organisations navigate different federal, state and local requirements. Dr. Deepti Pandita, Chief Medical Information Officer and VP of Clinical Informatics at the University of California Irvine Health, raised concerns about patients choosing to opt out of AI-assisted care. She said, “What happens then?” and added, “We are in this very, very nebulous zone of what does using AI in healthcare really mean from a compliance and regulatory perspective.”
Erika Kim, Program Manager at the Advanced Research Projects Agency for Health (ARPA-H), described AI as a “very fast-moving space” and called for stronger collaboration between healthcare providers, researchers and regulators. She said, “We need the community who are battling this in the real world to help us to get the right requirements and right understanding of where the bottlenecks are and what things need to be really put in place for people to have trust.”
The panel recommended creating formal data governance councils with clinicians, researchers, operational teams, IT professionals, cybersecurity experts, compliance officers and legal teams to oversee AI throughout its entire lifecycle, from procurement to retirement.
Speakers also stressed that AI governance cannot succeed without strong data governance. Dr. Pandita warned, “Bad data will lead to bad AI.” Experts said healthcare organisations must improve data quality, standardisation and integration before expanding AI adoption.
The discussion also emphasised that governance frameworks should reflect an organisation’s workflow and culture rather than following fixed templates. Dr. Pandita said, “When you do structure your AI governance, you have to look at it from not just a governance standpoint. It is a marriage of governance with workflow and culture.”
Experts concluded that AI should be used to improve data quality by helping clean, standardise and organise healthcare information, making AI systems more reliable and trustworthy.
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