| The problem
For decades, healthcare has optimized for data collection, storage and exchange. We have become remarkably good at capturing observations, but far less effective at preserving the relationships that give those observations meaning. A patient’s health journey is continuous, yet the data describing that journey is often captured as fragmented snapshots rather than a complete clinical narrative. A clinical trial database captures one chapter of the journey. An electronic health record captures another. A radiology archive stores images. A pathology system records diagnoses. A genomics platform identifies mutations. A claims database documents utilization and reimbursement. Each system faithfully records its own perspective. The challenge lies in stitching those perspectives together into a complete patient journey. Evidence networks and AI
Instead of thinking about training healthcare AI on collections of datasets, we should start thinking about building evidence networks. An evidence network is a connected representation of a patient’s health journey, where every observation retains its meaning, context and relationship to every other relevant observation. This matters because AI does not reason over isolated data points. It reasons over evidence. And evidence is created only when observations remain connected. Connections are key
AI models are increasingly expected to identify biomarkers, optimize clinical trial design, generate external comparators, discover safety signals and support regulatory decision-making. For these outputs to carry regulatory weight, the underlying evidence must be traceable, clinically interpretable and reproducible across datasets and settings. Regulatory agencies are also exploring AI-enabled clinical trial processes to shorten development timelines. Larger models and greater computing power will undoubtedly improve these capabilities. But no amount of computational sophistication can reliably recreate information that was lost during the data lifecycle. The next breakthrough in AI-powered drug development is therefore unlikely to come solely from a new algorithm or a larger foundation model. It will come from specialized, multilayered AI solutions that connect clinical observations through semantic harmonization, clinical context, temporal relationships and multi-modal links. — By MedCity Influencer Narasimha Kumar |
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