The next problem in healthcare AI may have little to do with AI itself.
A sophisticated model can analyze enormous amounts of medical data, but that capability does not help much if the underlying data has already lost the clinical distinctions researchers need.
That is the argument emerging from life sciences analytics: the bottleneck may be the semantic layer beneath the model, where clinical meaning is translated into standardized terminology.
For pharmaceutical companies, that can determine whether an AI system identifies the right patient cohort, recognizes meaningful disease differences or produces evidence researchers can actually use.
When Clinical Meaning Gets Flattened
Healthcare data relies heavily on standardized systems such as ICD-10, SNOMED CT and LOINC. They provide consistency across billing, documentation and interoperability, but they were not necessarily designed for the increasingly specific questions being asked by modern drug development.
Rare diseases are one example.
The source article points to research published in Orphanet Journal of Rare Diseases that found only 34% of 454 rare diseases could be specifically coded in ICD-10-GM. That leaves researchers trying to identify patient populations through terminology that may not precisely describe the condition they are looking for.
The problem also appears in disease severity.
A standardized code might identify psoriasis, while the clinical record contains much more information about severity, comorbidities and the patient's specific presentation. Once those distinctions disappear into a broader administrative category, an AI model cannot reliably reconstruct what was never preserved.
The same issue affects phenotyping and subtyping. Molecular characteristics and clinical modifiers often live in free-text notes rather than structured fields, creating a gap between what clinicians document and what downstream analytics systems can easily interpret.
The Data Problem Comes Before The Model
This creates an uncomfortable challenge for the AI industry.
Healthcare organizations can spend heavily on increasingly capable models while leaving the underlying data architecture largely unchanged.
That is unlikely to solve the core problem.
If the cohort itself is defined by what was billable rather than what was clinically true, a more powerful model may simply process the wrong cohort more efficiently.
Research cited in the source article illustrates the difference. Natural language processing applied to unstructured patient records increased identification of vaccine administrations by 16.8% compared with structured data alone.
The lesson is broader than vaccination records.
Clinical information exists across EHR notes, claims, registries, literature and other sources. The challenge is preserving the meaning as that information moves between systems.
Why Terminology Is Becoming AI Infrastructure
The answer is not simply adding more codes.
A useful terminology layer needs clinical validation, provenance and the ability to connect concepts across different data sources.
That means physicians, terminologists and subject matter experts still have an important role in defining what the data actually means.
It also means researchers need to know where a concept came from and why a particular patient was included in a cohort.
The more sophisticated approach is a connected knowledge graph that can maintain relationships between clinical concepts rather than treating terminology as a static lookup table.
That infrastructure becomes particularly important as AI moves from summarizing information toward making recommendations, selecting cohorts and supporting research decisions.
The better model may not be the one with the most parameters. It may be the one working with the most clinically faithful data.
For pharmaceutical and life sciences companies, that changes the investment question. Before upgrading a model, organizations may need to examine how much clinical specificity survives from the original patient record through to the AI system.
What This Means For Miami
Miami has a particularly relevant reason to pay attention to this shift.
The region's healthcare ecosystem combines major health systems, academic research, clinical trials and an expanding technology sector. That creates an opportunity for Miami to compete not only in healthcare AI applications, but in the infrastructure that makes those applications clinically useful.
The University of Miami and other local institutions are already exploring AI across clinical research and healthcare. As these systems become more sophisticated, the quality and structure of the underlying clinical data will become increasingly important.
For Miami startups, that could create opportunities in clinical data normalization, terminology management, knowledge graphs, real-world evidence and AI systems designed around high-quality healthcare data rather than simply larger models.
For investors, it also raises a useful question when evaluating healthcare AI companies: what happens if the model is not the main moat?
The companies that can preserve clinical meaning across EHRs, claims, research databases and other sources may ultimately provide some of the infrastructure on which Miami's next generation of healthcare AI is built.
The broader lesson is straightforward. Miami does not need to build every foundational AI model to become an important healthcare AI market. Building the data infrastructure that allows those models to work reliably could be just as valuable.
Reporting Source: This article builds upon reporting from MedCity News and adds analysis of what the development means for Miami and South Florida.