Republished with full copyright permissions from The San Francisco Press.

Artificial intelligence (AI) took center stage at the recent ViVE conference in Los Angeles, sparking lively conversations and offering a glimpse into its potential in healthcare. As the industry eagerly anticipates the transformative power of AI, it’s crucial to recognize that its impact is yet to fully materialize in addressing critical healthcare challenges.

While the promise of AI in healthcare is undeniable, the industry is grappling with the realization that the expected return on investment from AI implementations remains in an exploratory phase. According to insights garnered from the conference, the healthcare sector’s predominant crises, such as clinical burnout and workforce shortages, are yet to witness significant mitigation from AI solutions.

Despite the current landscape, industry leaders like Mount Sinai CEO Brendan Carr offer a hopeful outlook for the future. Carr’s conceptual framework for organizing healthcare AI into actionable categories sheds light on potential use cases and their impact on patients and providers. By mapping out a 2×2 grid that charts administrative and clinical AI applications and their alignment with patient and provider benefits, Carr provides a strategic approach to navigating the evolving AI terrain in healthcare.

Carr’s innovative thinking has illuminated low-risk administrative AI applications that yield tangible benefits for patients. Highlighting successful initiatives aimed at guiding patient behavior and choices, Carr emphasized the potential of AI to positively influence preventive care, dietary habits, and physical activity.

However, the integration of AI into clinicians’ day-to-day workflows presents a more intricate challenge. Carr’s acknowledgment of the stress-inducing nature of electronic health record (EHR) management for healthcare providers underscores the need for AI to streamline these fundamental operational aspects. Nevertheless, the complex nature of clinical decision-making and diagnoses demands cautious tread, as AI’s involvement in these critical healthcare determinations necessitates human oversight.

An equally cautious approach is warranted for AI’s foray into the purely clinical realm, where the stakes are highest. Carr’s emphasis on the meticulous consideration and measured implementation of AI in clinical decisions, diagnoses, and treatment recommendations underscores the imperative for prudence in these transformative applications.

While the apprehension surrounding AI’s clinical deployment is palpable, Carr’s optimism isn’t misplaced. He predicts a near-term horizon where healthcare systems are poised to realize substantial returns from their ongoing AI initiatives. Notably, he anticipates the field of radiology as an early beneficiary of noticeable AI-driven improvements.