Most AI in healthcare is overhyped.
Here's where it actually works.
I am not anti-AI. I am anti-hype. The most valuable AI applications start with a clear problem, not a shiny algorithm.
The Evidence-First AI Maturity Model
Predictive Modeling
ML models in development, some validated. Risk scores, patient stratification, disease progression — but models live in notebooks, not production.
Next step: Build deployment infrastructure and establish model validation frameworks.
Healthcare AI Case Patterns
Healthcare organizations struggle to identify high-risk patients before costly interventions are needed.
Machine learning models trained on longitudinal claims and EHR data stratify patient populations by risk, so care managers can intervene early.
Kaiser Permanente cut hospitalizations 12% in pilot programs. Humana reduced ER visits 8% in high-risk cohorts using similar models.
Systematic reviews and meta-analyses take months of manual screening and classification.
ML classifiers cut screening time by 60-80% while maintaining sensitivity. Generative AI builds health economic models in hours, not weeks.
A pharma client completed a systematic review in 6 weeks instead of 5 months. That's the difference between informing a decision and being too late.
Up to 80% of clinical data lives in unstructured physician notes, discharge summaries, and pathology reports.
Domain-optimized NLP extracts structured data from clinical text with 94% accuracy in phenotyping, nearly double manual chart review (49%).
One rare disease study found 3x more eligible patients by adding NLP-extracted data to claims-only criteria. The patients were there all along.
The most impactful AI in healthcare doesn't start with the algorithm. It starts with the question. Organizations that begin with a clear decision they need to make, then work backward to the evidence and tools required, consistently outperform those chasing the latest model architecture.
Whether it's predictive risk modeling, automated evidence synthesis, or extracting insights from unstructured clinical text, the focus should always be on practical, responsible implementation that delivers measurable outcomes.
References
- Digital Bricks AI. 7 AI trends to watch in 2026. Published 2026. Accessed February 2026. digitalbricks.ai
- Merative. Real-world data trends 2026: the shift to quality and AI precision. Published 2026. Accessed February 2026. merative.com
- Elicit Research. AI-powered evidence synthesis for biopharma. Published 2025. Accessed February 2026. elicit.com
Let's Discuss AI Strategy
Interested in where AI can actually accelerate your evidence generation? Let's talk.