From Scans to Structured Data: Converting Medical Reports to JSON with Pydantic

On May 6th, a developer published a tutorial on Dev.to that put a name to a problem most healthcare data teams handle through gritted teeth: medical scan reports arriving as dense, unstructured prose — radiologist notes, pathology summaries, discharge letters — that sit essentially inert because nothing downstream can parse them at scale. The proposed solution is methodologically clean. Feed the raw report into a large language model with careful instructions to extract specific fields: patient identifiers, clinical findings, measurements, diagnostic conclusions. Then run the output through Pydantic, the Python validation library, which enforces a defined schema and rejects anything that doesn't conform. The LLM handles the ambiguity of natural language. Pydantic handles the rigidity that production systems demand. Between them, you get structured JSON that an electronic health record, a billing system, or a research database can actually use. The tutorial is a single voice so far — one developer's working approach, scored modestly at 6.9 on Dev.to — but the problem it addresses is not modest at all.

If confirmed as a repeatable pattern, here is what this means. The combination of LLM extraction and Pydantic validation is essentially a translation layer between how clinicians communicate — in prose, in inference, in qualified uncertainty — and how software systems operate. That layer, if it becomes standardised practice, changes the economics of medical data integration considerably. Right now, converting legacy scan reports into structured records requires either expensive human review, brittle regex pipelines, or both. An LLM-plus-schema approach could compress that cost dramatically and open historical archives of clinical data to analysis that was previously impractical. The second-order effect is more consequential: structured clinical data at scale accelerates everything from drug discovery pipelines to population health modelling. The organisations that build reliable extraction workflows early will hold a meaningful head start over those still wrestling with PDF parsers. For developers in health-adjacent spaces, the practical implication is immediate — this is a pattern worth testing in your own context, even if the tutorial itself is a proof of concept rather than a production blueprint.

Watch for other developers publishing implementations, performance benchmarks, or failure cases — particularly around LLM hallucination in clinical contexts, which is where this approach either earns genuine trust or falls apart quietly.

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