The competition for specialized AI medical models is intensifying. A document was issued by Google and DeepMind detailing Med-Gemini, a collection of sophisticated AI models aimed at healthcare applications. According to the authors, Med-Gemini is outperforming rival models like OpenAI’s GPT-4. The latter, however, is keeping up in the medical field and has increased the scope of its partnership with Moderna, a pharmaceutical company.
If confirmed in real-world scenarios, Med-Gemini’s ability to capture context and temporality—a known weakness in current health-related AI models—represents a significant advancement. It’s true that doctors are infamous for using too many acronyms and not documenting everything consistently. However, contextual complexity rather than textual complexity presents the real training problem for medical algorithms.
Med-Gemini appears to have addressed the precise problem by stepping back from the enormous task of creating a comprehensive general medical model. Rather, Google’s developers have taken a vertical-by-vertical approach to creating a “family” of similar models, each of which optimizes a particular medical area or scenario. According to reports, this has led to more accurate and nuanced results, as well as more transparent reasoning and interpretable feedback, like explaining why a given diagnosis is the most likely one.
Google appears to hold Med-Gemini to the same level as physicians, who are supposed to stay updated on the latest research. A substantial extra layer is also included in the new model: a web-based search engine for current information that enables data to be enhanced with outside knowledge and integrates online results into the model.
It’s crucial to keep in mind that even though Med-Gemini has made use of a variety of data sources, including X-rays, pictures of skin lesions, excerpts from medical records, sample questions for medical exams, and more, prospective, real-world validation on actual production-level data is still pending.
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