Two competing paths are emerging for monetizing GenAI in healthcare: one led by tech firms and FDA approvals, the other by clinicians empowering patients directly.
. Most doctors saw it as a novelty, perhaps useful for administrative tasks or as a basic reference tool, but too unreliable for clinical care.
These applications are developed by training AI models to compare two datasets, detect dozens of subtle differences between them and assign a probability factor to each. For reliable results, the training data must be objective, accurate and replicable. That’s why nearly all narrow AI tools are applied in visual specialties like radiology, pathology and ophthalmology — not cognitive fields that depend on subjective entries in the electronic medical record.
While there’s still debate over whether LLMs are ready for independent clinical use by patients, the pace of progress is breathtaking.As generative AI becomes more powerful each year, two distinct approaches are emerging for how it might be monetized in healthcare.Medical care often fails to deliver optimal outcomes at a price patients can afford. This monetization model would seek to close those gaps.
They would then refine these models, training them on radiologist interpretations of thousands of X-rays, transcripts from patient advice centers or anonymized recordings of conversations with patients. The result: specialized generative AI tools designed to address specific gaps in care delivery. They would create low-cost instructional materials for various patient-learning preferences ranging from digital guides and printed pamphlets to YouTube videos and short training courses. These resources would teach patients how to use any of the publicly available large language models safely and effectively.
Unlike startup models that require tens of millions in funding and FDA approval, these educational tools could be developed and deployed quickly by doctors and other clinicians. Because they teach patients how to use existing tools rather than offer direct medical advice, they would avoid many regulatory burdens and face reduced legal liability. And with 40% of physicians already working part-time or in gig roles, there are hundreds, or likely thousands, of experts ready to contribute.
Healthcare Chatgpt Microsoft Antrhopic Fda Approval Openai Narrow Ai Llm Gpt-5
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