Generative AI is rapidly advancing in the field of healthcare, with two competing visions emerging on how it will be monetized. The progress of Generative AI has been remarkable, especially with the release of ChatGPT by OpenAI in late 2022. Initially seen as a novelty, it is now outperforming most physicians on medical exams and challenges. Companies like Google, Microsoft, and Nvidia are developing GenAI tools that are expected to double in capabilities annually, with OpenAI’s GPT-5 model set to launch soon.
The pressing question now is how Generative AI will be monetized in healthcare. Two main approaches are being considered. The first involves tech companies developing FDA-approved tools for diagnosis and treatment. These tools would be specifically designed for clinical use, with a focus on improving patient outcomes. The second approach is clinician-led, allowing patients to access and use large language models for managing chronic diseases and assessing symptoms.
To understand the advantages of each approach, it is important to consider the differences between Generative AI and traditional AI tools used in medicine. Traditional AI tools, known as narrow AI, are trained on specific datasets to solve well-defined problems, primarily in visual specialties like radiology. These tools outperform clinicians but are limited to the tasks they were designed for.
On the other hand, Generative AI, powered by large language models like ChatGPT, Claude, and Gemini, is trained on a vast amount of internet-accessible content, enabling them to respond to a wide range of medical questions. These tools offer opportunities across various modalities and have the potential to transform healthcare delivery.
As Generative AI becomes more powerful, two distinct monetization models are emerging. The entrepreneurial tech model involves developing specialized GenAI tools through venture funding and FDA approval. These tools would address specific gaps in care delivery and could command high prices. The clinician-led GenAI education model focuses on empowering patients to use existing large language models for self-management. This approach is less expensive, easier to develop, and more transformative in improving patient outcomes.
Both models have their trade-offs, but they are not mutually exclusive. While technology companies may lead the way in developing GenAI tools, clinicians and educators also have the opportunity to drive the process and improve healthcare delivery. With the potential to revolutionize patient care and accessibility, Generative AI is set to play a significant role in the future of medicine.
