Documenting the Path to AI Adoption
I am publishing my findings as I build. Explore the frameworks, templates, and prototypes currently in development for the diagnostic sector.
Latest Insights & Articles
Actionable advice and deep dives into the topics that matter most to modern diagnostic labs.
Ready-to-Implement Code Solutions
Explore my library of pre-tested AI code, validated demos, and deployment frameworks built to bypass prototyping and rapidly integrate intelligence into your healthcare lab.
AI Lab Report Analyzer
Transform unstructured lab reports into actionable insights instantly. AI-powered analysis that extracts, interprets, and flags critical values from any lab report format.
AI Readiness & Strategy Assessment
A comprehensive audit of your data, systems, and goals, culminating in a custom AI implementation roadmap.
AI Use Cases for Diagnostic Labs
Explore common AI applications and how they solve specific challenges in diagnostic laboratory operations.
Predictive Quality Control
Predict QC failures 6-8 hours before they occur, preventing costly instrument downtime and sample re-runs.
Turnaround Time Forecasting
Predict TAT 12-24 hours in advance to optimize staffing, manage expectations, and prevent bottlenecks.
Lab Data Anomaly Detection
Automatically detect unusual patterns in lab results, instrument performance, or operations that may indicate problems.
📥 Frameworks in Development
I am currently building and testing these planning tools. Request beta access to use them in your lab in exchange for feedback.
AI Readiness Self-Assessment
A structured questionnaire to evaluate your lab's data infrastructure, team capabilities, and compliance readiness.
Request Beta Access →Vendor Evaluation Matrix
Key questions to ask AI solution vendors to separate marketing claims from real technical capabilities.
Request Beta Access →ROI Calculator Model
A spreadsheet framework for building financial projections to secure executive budget for AI.
Request Beta Access →❓ Common Questions
Why focus on "Readiness" first?
Research shows that 70% of healthcare AI projects fail due to poor infrastructure planning, not poor technology. My research focuses on identifying these gaps before a lab spends budget on software.
How do you validate your prototypes?
I build working models using publicly available datasets (such as MIMIC-III or synthetic LIS data). This proves that the logic, code, and statistical approach are sound before applying them to private patient data.
Can I contribute to this research?
Yes. I am actively interviewing Lab Directors and Operations Managers to ensure these frameworks address real-world pain points. If you are open to a 15-minute interview, please connect.
📊 The Value of Strategic Planning
Typical ROI scenarios where proper AI readiness assessment prevents costly failures.
Scenario A: The Premature Purchase
The Risk: A lab buys a $200k predictive analytics platform but discovers their LIS data is too unstructured to use it.
The Strategic Fix: A pre-purchase Data Governance Audit would identify the cleaning required *before* the license clock starts ticking.
Scenario B: The "Black Box" Rejection
The Risk: Pathologists refuse to use an AI tool because they don't understand how it reaches conclusions.
The Strategic Fix: Implementing an "Explainable AI" validation framework ensures clinical staff trust the tool before deployment.
Join the Research Cohort
Get notified when new frameworks, prototypes, and white papers are released. No spam, just technical insights for lab leaders.
Subscribe to Updates