Bridging deep radiology expertise with the healthcare AI ecosystem. For AI companies and healthcare systems that need a credible clinical partner — someone who understands both sides of the room.
Most AI tools in healthcare are built by people who understand machine learning. Fewer are shaped by people who understand what a radiologist actually does — how they read, how they decide, where the friction lives, and what the technology has to survive to be useful. That clinical grounding is what Dr Tham brings to every AI partnership.
Areas of engagement:
01
For AI companies and vendors seeking a credible clinical partner to shape, validate, or advocate for their product within healthcare systems. Dr Tham provides frank, subspecialty-grounded clinical input — not endorsement, but genuine engagement with what the technology does, where it works, and where it falls short in real clinical conditions.
This includes
Clinical evaluation of AI tools against real-world radiology practice
Advisory on product-market fit within clinical workflows
Regulatory and governance positioning
Speaking engagements and panel participation
Clinician-facing communication strategy
02
Most AI implementations fail not because the technology is wrong, but because the workflow around it hasn't changed. Dr Tham offers clinical workflow analysis for radiology departments and healthcare systems seeking to integrate AI and automation — identifying where AI meaningfully reduces friction, where it introduces new risks, and how to build governance and training structures for sustainable adoption.
This includes
End-to-end radiology workflow analysis
AI integration and deployment strategy
Accountability and ownership framework design
Clinician training and change management
Post-deployment monitoring and performance review
03
Radiology reporting is one of the highest-leverage applications of AI in healthcare — and one of the most poorly executed. Dr Tham advises on structured reporting pipeline design, AI-assisted report generation, and the development of patient-friendly report outputs. Grounded in real reporting practice, this work is about quality, clarity, and accountability — not just speed.
This includes
Structured report template design for MSK and beyond
AI-assisted dictation and report generation pipeline review
Patient-friendly report simplification
Quality assurance and output review frameworks
Integration with RIS and clinical documentation systems
A mix of clinical AI work, technical side projects, and recent pieces on radiology, governance, and the role of the subspecialist.
A position paper arguing that AI's real value is not information access but interactive challenge — sparring with intellectually fluent systems that were once a privilege of elite institutions. Introduces a K-shaped divergence model and five philosophical techniques for using AI as a deliberate-practice partner. SSRN preprint, April 2026.
Singapore's updated framework defines three distinct stakeholder groups across the AI lifecycle: Developers, Deployers, and Users. Each carries specific obligations.
"Most deployment friction comes from exactly this: unclear ownership."Read the piece
Budget 2026 signalled Singapore's intent to formalise AI governance at a national level. A look at what inclusive governance actually requires from the ground up — including the role of RADII.
Read the pieceAfter 16 years in private radiology, three models emerge: restructured hospitals, small independent centres, and large private groups.
"The more we automate, the more crucial the driver in the seat becomes."Read the piece
A satirical radiology hedging engine. Paste a normal report, pick an intensity tier, watch clinical certainty become defensible uncertainty. A weekend build mirroring how radiologists communicate — LLM proxy, rate limiting, and bot protection on a serverless edge stack.
View projectAn interactive web guide teaching orbital mechanics through NASA's Artemis II mission. Four chapters covering orbits, trans-lunar injection, gravity assists, and reentry — a creative side project demonstrating technical storytelling outside the medical domain.
View projectDr Tham writes regularly on AI in healthcare, radiology governance, and the evolving role of the subspecialist. Follow on LinkedIn for the latest pieces.
Follow on LinkedInIf you are building for radiology or healthcare and need a clinical partner who has been in the room — get in touch. Engagements are considered individually.