Considering the Value of Generative AI in Healthcare

By | September 14, 2023

Generative AI is sparking intriguing discussions in healthcare, particularly in its role within chatbots for diagnostic and decision-making purposes. The potential benefits are vast, but it’s essential to recognize and address the accompanying risks and complexities.

Diagnostic accuracy and adherence to evidence-based medicine have been central issues in recent healthcare conversations. As we strive to enhance outcomes and affordability, any tool capable of contributing to these goals deserves thorough examination and testing. And generative AI offers a unique opportunity to improve precision and bolster evidence-based diagnostic and treatment choices.

However, hurdles loom on the path to integrating generative AI into healthcare. One primary concern revolves around the “black box” nature of algorithm creation and data utilization. Meaning, the users can’t see the inner workings of the algorithm. So, understanding the basis of AI recommendations is crucial to building trust. Yet, it’s worth noting that much of clinical decision-making today relies on the experiences and training of clinicians, which often exist as their own “black boxes.”

Regulation is another pressing issue. The Food and Drug Administration (FDA) oversees diagnostics, drugs, and procedures in the United States, but AI does not currently fall under a medical device exception. Regulatory oversight is vital to assure the public that AI tools have been rigorously evaluated and hold value. Trusting lives to a wave of enthusiasm without proper scrutiny poses significant risks.

Moreover, the role of clinicians in utilizing generative AI is paramount. AI should not serve as a substitute but rather as a complement to the existing clinical capabilities within the healthcare ecosystem. As we know, the pace of knowledge and technological advancement is staggering, making it impossible for clinicians to keep up without the aid of these tools. Additionally, clinicians are human and susceptible to biases, behavioral economic dynamics, and inherent error rates. Generative AI holds promise in mitigating these issues, provided we understand its training and capabilities, as well as its own “knowledge drift.”

Another aspect of generative AI we need to understand are the legal ramifications and where we place accountability. Who is responsible if something goes wrong? Is it the clinician, the product manufacturer, or the AI itself, which is continuously learning and evolving. From a clinical liability perspective, the standard of care evolves with medical advancements. If generative AI becomes the new standard of care without addressing the before mentioned concerns, it could be seen as the norm without a solid understanding of its full value. This situation parallels the present state of clinical treatments, where standards of care may sometimes reflect something other than proven methodologies.

Regardless of where one stands on the spectrum of excitement or apprehension, we should embrace the challenge. The potential for improvement in healthcare is vast, and while concerns exist, systematically addressing each problem can lead to significant advancements. Let’s approach the integration of generative AI into healthcare with a balanced perspective, recognizing the need for rigorous evaluation, transparency, and thoughtful implementation to ultimately achieve a greater good for all.