Last week, OpenAI launched its newest model, GPT 5. The company announced that this latest iteration is its most advanced model, including significantly improved deep reasoning and understanding capabilities. Furthermore, the new model is meant to be faster while having the ability to provide more personalized and curated responses for users.
One of the most notable aspects that the company focused on with the announcement is the significant improvement in the new model’s ability to navigate healthcare related topics and questions.
In fact, the model has apparently surpassed all previous iterations in performance as graded by OpenAI’s own HealthBench paradigm, the company’s healthcare rubric that it developed to track model progress in the field of healthcare. According to the company, GPT 5 is capable of “proactively flagging potential concerns and asking questions to give more helpful answers,” transitioning itself from being purely a query based system to more of a conversational thought partner: “think of it as a partner to help you understand results, ask the right questions in the time you have with providers, and weigh options as you make decisions.”
Why is this important?
As artificial intelligence systems are becoming more mainstream, users are increasingly relying on them for more personalized and accurate answers. Healthcare remains one of the most important and prevalent subjects that users make queries about on the internet. AI systems such as GPT serve this purpose well, especially as incremental improvements in models significantly further the conversational experience. According to the company, “GPT‑5 responds empathetically, organizes and explains information clearly for a non-expert, and proactively flags important factors that would help provide a more detailed follow-up answer.”
This is overwhelmingly the trend among AI companies, given that technology giants have become privy to the fact that the healthcare experience for the average consumer has become significantly strained; thus, they also see it as a sizable opportunity for AI driven chat agents to fill consumer experiential gaps.
Google’s MedGemma collection of models has done a significant amount of work in being able to accurately comprehend large amounts of medical text and image data. This is particularly important for the healthcare field, especially for enterprises use-cases, as healthcare data is largely unstructured and frequently found in a variety of different modalities. Organizations can use these models to better augment their capabilities and glean insights from large datasets, in addition to collaborating on routine tasks such as clinical decision support, triaging, basic diagnostics and even determining treatment capabilities.
The growth in the accuracy and efficacy of these models raises an important question, however: will the traditional healthcare workflow soon become a tiered system, where these AI systems will be the frontline providers for non-emergent queries, while trained, human physicians will be reserved for more emergent, acute and complex diagnostics?
With how overburdened the healthcare system currently is and will continue to be given the impending physician shortage, this is certainly not out of the realm of possibilities. Especially as Moore’s law is increasingly playing out (the cost of compute will continue to become cheaper over time as technology and chips become more advanced), there is significant opportunity for artificial intelligence systems to severely disrupt the traditional economics of how the healthcare system runs today.
The important question, however, will be how far society values the sophistication and ease of using these models as opposed to the traditional physician visit.
