Insights from the January 31st, 2024 session on Gen AI use cases in the Digital Health sector
In the third session of the Generative AI in Healthcare series, speakers Nikhil Bhojwani (Recon Strategy) and Satish Tadikonda (HBS) provided a thought-provoking overview of the current digital health landscape, followed by an engaging panel discussion led by Alyssa Lefaivre Škopac (Responsible AI Institute). Panel speakers Payal Agarwal Divakaran, Reena Pande, and Andrew Le shared their valuable insights as investors, physicians, and executives at the forefront of AI in digital health.
Recordings of the Generative AI healthcare series- session 1 , session 2 and session 3
Current Landscape
Nikhil Bhojwani kicked off the session with a presentation outlining the intersection of digital technology, artificial intelligence (AI), and healthcare. Bhojwani provided an overview of the pervasive influence of digital components across healthcare domains, illustrating the various ways in which AI was employed, including supporting, augmenting, and substituting human work. Examples were presented, ranging from AI synthesis for electronic health records to AI symptom checkers for diagnosis, prompting dialogue on the implications and ethical considerations of AI integration in healthcare tasks traditionally performed by humans. The conversation emphasized the need for a nuanced understanding of AI’s role in healthcare delivery and management, laying the groundwork for further exploration of its ethical, practical, and regulatory dimensions.
Opportunities in Digital Health
RAI’s Alyssa Lefaivre Škopac then initiated a discussion about the opportunities in digital health, particularly focusing on AI integration. Payal Divakaran elaborated on the different perspectives in adopting AI in consumer, physician, and enterprise-oriented use cases, and the importance of trust in AI adoption. Payal noted the lag in enterprise adoption compared to consumer-facing applications, queuing up Andrew Le to share his perspective on the benefits of consumer-facing AI applications, citing examples of how AI can empower consumers by processing complex healthcare data and providing user-friendly interfaces. Andrew expounded upon the transformative potential of AI in enabling consumers to make sense of healthcare data and navigate the healthcare system more effectively, and the significance of AI in optimizing back-office operations in healthcare was echoed amongst the panel members. Reena Pande also noted the importance of focusing on fundamental problems and considering the provider experience alongside patient outcomes and cost. She highlighted opportunities for AI to streamline administrative tasks, improve diagnostic capabilities, and augment patient treatment, urging for careful consideration of where AI can be responsibly applied. Reena underscored the need for a nuanced approach to AI deployment that serves the interests of all stakeholders.
Risks and Limitations
The panelists expressed a mix of skepticism and optimism regarding the integration of AI as a co-pilot in healthcare, acknowledging the need for trust-building and careful consideration of the nuances in healthcare. They discussed the challenges of ensuring responsible AI deployment, including issues of validity, safety, security, accountability, transparency, explainability, data privacy, and bias. Additionally, they highlighted the importance of addressing these risks to foster confidence and ensure the ethical and effective use of AI in healthcare, including the significance of transparent disclosures and establishing clear accountability frameworks within healthcare institutions. They also responded to keen observations from the audience regarding the need for cultural competency to ensure the unique and varied needs of each user, from patients and physicians to administrators and regulators, are being met by their AI solutions. In order to address these challenges, the panel reiterated the need for ongoing dialogue and collaboration between stakeholders to navigate the complexities of AI integration responsibly.
Accountable AI in Healthcare
The panel discussion then delved into the emerging guidance from regulatory bodies like the FDA on managing the integration of AI in healthcare. Participants engaged in a robust conversation about the accountability, transparency, and interpretability of AI systems in healthcare decision-making processes. They explored the traditional role of clinicians as the ultimate decision-makers and discussed the challenges and opportunities in distributing responsibility among various contributors, including AI systems.
As the session concluded, the moderators and panelists stressed the importance of establishing clear accountability frameworks and guardrails to mitigate risks associated with AI deployment in digital health. They emphasized the need for scalable access to data and partnerships between tech giants and healthcare incumbents to foster trust and manage risks effectively. The discussion underscored the complexity of evaluating AI tools and the necessity of ongoing dialogue and collaboration to responsibly address the evolving landscape of AI integration in healthcare.
The Gen AI in Healthcare series is collaboratively produced by Harvard’s Digital, Data, Design (D^3) Institute and the Responsible AI Institute.
About Responsible AI Institute
Founded in 2016, Responsible AI Institute (RAI Institute) is a global and member-driven non-profit dedicated to enabling successful responsible AI efforts in organizations. RAI Institute’s conformity assessments and certifications for AI systems support practitioners as they navigate the complex landscape of AI products. Members include ATB Financial, Amazon Web Services, Boston Consulting Group, Yum! Brands and many other leading companies and institutions collaborate with RAI Institute to bring responsible AI to all industry sectors. Become a member.