Govern AI at the Source
The tools your teams use to make decisions are only as reliable as the judgment they bring to them. Health AI delivers science-credentialed training programs that build the one capability no governance software can provide: researchers and clinicians who know when not to trust a model.
High-stakes domains. One standard.
These programs are built for environments where the cost of acting on a flawed AI output is asymmetric — where being wrong has real consequences downstream.
Biotech & Drug Discovery Teams
Research scientists and computational biologists using AI in lead identification, protein modeling, or clinical data analysis. The validation gap here connects directly to regulatory exposure.
Health Professions Educators
Nursing, pharmacy, PA, and allied health programs preparing graduates for clinical environments already deploying AI decision support. Accreditation bodies are beginning to flag AI competency as a formal requirement.
Life Sciences Research Institutions
University research centers, academic medical centers, and biomedical programs where AI tools are entering lab workflows faster than governance frameworks can catch up.
Pharmaceutical & Clinical Research
CROs, pharma R&D teams, and clinical scientists who need to evaluate AI-assisted outputs — from literature synthesis to trial design — with a documented, defensible methodology.
Biomedical & Health Engineering
Engineers building AI-adjacent health devices, diagnostics, or clinical tools for whom AI validation is a design requirement — not an afterthought.
Enterprise Science Teams
Organizations deploying AI in regulated or high-consequence environments — automotive safety systems, industrial quality control, financial risk modeling — where scientific validation discipline transfers directly.
Built on the RIGOR™ Framework
These programs are not generic AI literacy courses. Every session is grounded in the same methodology Health AI uses for deployment-grade AI validation across healthcare, automotive, and enterprise environments. Participants learn to apply a field-tested, structurally disciplined framework — not a checklist adapted from a vendor whitepaper.
Four Delivery Formats.
Each program can be delivered as-designed or customized to the specific tools, research domain, and risk context of your team. Custom engagements are available for all formats.
When to Trust the Model
AI Validation Essentials for Life Sciences Researchers
What It Addresses
Most researchers using AI tools have no structured method for evaluating when the output is trustworthy — or how to document that judgment for a manuscript, collaborator, or regulatory submission. This program closes that gap using the RIGOR™ framework as a practical evaluation tool.
Hands-on throughout. Participants work on outputs from their own research domain, not generic case studies.
Participants Leave With
- The RIGOR™ one-page validation reference card
- A working draft AI use protocol for their lab or team
- A defensible validation statement template for manuscripts and submissions
- Structured vocabulary for communicating AI limitations to collaborators and reviewers
AI-Ready
How to Talk About Your AI to Investors and Regulators
What It Addresses
Sophisticated investors and regulatory reviewers are now asking pointed questions about AI validation methodology in diligence and submissions. Most founders and senior scientists have no structured response. This session provides the language, the framework, and the documentation approach.
Covers what FDA expects when AI touches a drug development workflow, what "validation" means in an IND context, and how to frame AI-assisted research in investor materials without overclaiming.
Participants Leave With
- A working vocabulary for AI validation defensible in regulatory and investor contexts
- A one-paragraph AI methodology statement adaptable for IND, publication, or pitch deck
- Clarity on where current FDA AI/ML guidance creates risk in their specific pipeline
- A framework for ongoing documentation as their AI use evolves
AlphaFold in Practice
Validating Structural Predictions for Research and Publication
What It Addresses
AlphaFold and structural prediction tools have transformed computational biology — but the confidence and validation questions every user faces are rarely addressed with scientific rigor. When is a predicted structure publication-ready? How do you communicate prediction confidence to collaborators and peer reviewers? What documentation do journals and regulatory bodies now expect?
Led by an active researcher with direct experience in computational protein modeling and structural validation in drug discovery contexts.
Participants Leave With
- A structured validation checklist for AlphaFold and structural prediction outputs
- Language for communicating prediction confidence in manuscripts and grant applications
- Criteria for when independent experimental validation is required vs. optional
- Documentation framework aligned with emerging journal and regulatory expectations
Building Your Team's AI Protocol
A Facilitated Working Session for a Single Team
What It Addresses
A bespoke working session for one team. Unlike the group programs, this is not a workshop — it is a structured facilitation that produces a real deliverable: a draft AI use protocol built around the team's specific tools, research domain, risk tolerance, and regulatory context.
The protocol is not a template filled out during the session. It is a document built with the team, reviewed against RIGOR™ criteria, and designed to hold up under institutional or regulatory scrutiny.
The Team Leaves With
- A completed draft AI use protocol tailored to their specific context
- RIGOR™ pillar assessment for each AI tool currently in use by the team
- A prioritized list of validation gaps and recommended remediation steps
- A document ready for submission to institutional review, compliance, or an IRB
"The benchmark-only standard is no longer defensible. Validation is a lifecycle discipline — and it begins with the humans making decisions, not the software monitoring them."— Olga Lavinda, PhD, CEO, Health AI
From first conversation to delivered program.
Scoping Call
A 20-minute conversation to understand your team's current AI tools, research domain, regulatory context, and the specific gap you need to close. No commitment required.
Program Selection
We recommend the right format and identify any customization needed. Most teams can be served by an existing program; some require a tailored session design, quoted separately.
Delivery
In-person or hybrid delivery at your facility or a designated venue. Sessions are led by Dr. Lavinda directly — not delegated to a training associate.
Deliverables & Follow-On
Every participant leaves with a concrete output. Organizations interested in ongoing governance support or protocol development can continue to an advisory engagement.
Schedule a Conversation
Ready to build a validated team?
Start with a 20-minute scoping call. We will identify which program fits your team's context and whether any customization is needed.
Book a Discovery Call View the RIGOR™ FrameworkHealth AI Training & Workforce Programs | RIGOR™ Framework | Insights
Olga Lavinda, PhD | CEO, Health AI | © 2026 Health AI. RIGOR™ is a trademark of Health AI.