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Enterprise leaders are facing a frustrating reality. Engineering teams are successfully building impressive artificial intelligence proofs of concept in controlled environments. Yet, when the time comes to deploy these tools across the wider organization, progress grinds to a complete halt. You have the budget, the mandate from the board, and the initial working prototype, but translating that pilot into a reliable, production-ready tool feels impossible.

AI's rapid evolution has ignited a transformation across all industries, including the healthcare sector. Large Language Models, such as Claude and GPT-4, have impacted the world with their efficiency in drafting poetry, writing codes and replying to general queries. However, general-purpose models may not work when evaluating an oncology report, predicting the risks of patient readmission, or getting dosage instructions from unorganised clinical notes. General intelligence isn't enough in medicine. Clinical AI demands special skills, privacy, and accuracy.