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 Resonance 360 brings to AI partnerships.
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. Resonance 360 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. Resonance 360 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. Resonance 360 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
Dr Tham writes regularly on LinkedIn about healthcare AI, governance, and the evolving role of the radiologist. A selection of recent pieces below.
Singapore's updated framework defines three distinct stakeholder groups across the AI lifecycle: Developers, Deployers, and Users. Each carries specific obligations — from transparency and testing, to governance and risk assessment, to professional judgment and human oversight.
"Most deployment friction comes from exactly this: unclear ownership."Read on LinkedIn
Budget 2026 signalled Singapore's intent to formalise AI governance at a national level. The question for healthcare AI is whether regulation will move fast enough to enable innovation, or slow enough to protect it. A look at what inclusive governance actually requires from the ground up — including the role of RADII.
Read on LinkedInAfter 16 years in private radiology, three models emerge: restructured hospitals, small independent centres, and large private groups. Scale enables governance, technology investment, and systems thinking. But it also raises the stakes for the people inside it.
"The more we automate, the more crucial the driver in the seat becomes."Read on LinkedIn
Dr 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.