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AI in information management
How professionals structure expert knowledge, evaluate model outputs, handle uncertainty, and translate AI into responsible operational workflows.
Teaching // Digital Health // AI
Teaching digital health and AI from the builder side.
Available for adjunct, part-time, modular, guest, workshop, and project-based teaching across digital health, health AI, applied AI, and information management.
I bring the classroom a rare overlap: medical training, health-tech founder execution, AI systems work, and practical experience translating clinical and information workflows into products people can evaluate and use.
Adjunct / part-time
Digital health
Health AI
Information management
Teaching Fit
For programs where medicine, AI, and information work meet.
The strongest fit is applied teaching for students and professionals who need to understand AI as a workflow capability, not just a model demo.
Adjunct, part-time, modular, guest, workshop, and project-based teaching formats
Digital health, healthcare AI, Smart Healthcare in Asia, and applied AI in information systems
Graduate, executive education, innovation, information management, health informatics, and product-studio contexts
Courses where students need to connect AI capability with workflow, evidence, trust, adoption, and implementation
Modules
Course and workshop areas.
These can be shaped as a full module, short course, guest lecture, executive session, or student project studio.
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Smart healthcare and digital health
Patient engagement, continuous care, clinical workflow mapping, adoption constraints, and the practical economics of health technology.
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Responsible health AI
Guardrails, human review, escalation, evaluation loops, safety boundaries, and the difference between useful assistance and overclaiming.
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Health-data workflows
Patient-generated data, records, interoperability concepts, privacy expectations, and the information architecture behind trusted health products.
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Product studio for health AI
From problem discovery and stakeholder interviews to requirements, prototype scope, implementation planning, and demo-ready product thinking.
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Founder/operator practicum
A builder-side view of pitching, stakeholder communication, delivery tradeoffs, and turning ambiguous AI ideas into executable projects.
Classroom Outcomes
What students should leave able to do.
Evaluate AI outputs without treating them as authority
Map clinical and operational workflows before proposing technology
Design human review, guardrails, escalation paths, and feedback loops
Translate health and information problems into product requirements
Explain AI tradeoffs clearly to clinicians, operators, founders, and technical teams
Build or critique practical prototypes with realistic adoption constraints
Formats
Ways to engage.
Designed for flexible academic formats: Singapore hybrid, remote, modular, or project-based work where the applied perspective is the point.
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Adjunct or part-time lecturer
Semester modules, applied courses, or repeatable teaching blocks around digital health, AI, and information management.
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Short course or executive education
Compact programs for working professionals, innovation teams, and healthcare or information-systems leaders.
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Guest lecture or workshop
Focused sessions on health AI, applied AI workflows, patient engagement, or responsible product boundaries.
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Project-based teaching
Student mentorship, capstone critique, product studios, and applied project review for real healthcare and information-systems problems.
Evidence
Why this is credible in a classroom.
Medical foundation
Doctor of Medicine, University of Zagreb School of Medicine, with anatomy teaching assistant experience in lab and faculty settings.
Builder credibility
Founder/operator across AI-native health systems, mental health software, hypertension management, health chatbots, and medical-device concepts.
Applied AI systems
Hands-on work with LLMs, RAG, agents, prompt systems, automations, human review, evaluation loops, and workflow integration.
Teaching communication
Experience creating frameworks, workshops, stakeholder-ready documentation, pitch narratives, and cross-disciplinary product explanations.
Teaching Lens
AI should be taught as judgment under constraints.
In health and information management, AI is not just a technical tool. It changes how people structure knowledge, interpret evidence, manage uncertainty, and decide what deserves human review.
My teaching focus is practical: students should learn to ask better workflow questions, define safer AI boundaries, communicate across disciplines, and judge whether an AI system is useful enough to enter a real organization.
Open a channel
Looking for applied teaching in digital health, AI, or information management?
Send the course, audience, format, semester or timing, and the capability you want students to leave with. I can share a short course outline, teaching materials, CV, or references.