Welcome to Pulse: Amplify, where we sit down with the leaders and changemakers shaping the future of health.

Guy Tsafnat joins Pulse to unpack why healthcare is “data rich but evidence poor,” and what it really takes to turn messy clinical data into something usable at scale.

He shares hard-earned lessons from building multiple startups, including why sales is harder than technology, how founders should think about co-founders, and why asking for help matters more than perfect pitch decks.

The conversation explores why healthcare data is fundamentally different to other industries, why most data projects stall before delivering value, and what needs to change to make evidence-based care actually work in practice.

Guy also gives a pragmatic take on AI in healthcare—why “rubbish in, rubbish out” still applies, where ambient AI is showing real promise, and why simply layering AI onto poor data won’t change clinical practice.

Finally, he reflects on the realities of building a global health startup from Australia, including the challenges of selling innovation locally and the advantages of lower development costs and strong R&D support.

Connect with Guy Tsafnat: LinkedIn

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Production by Octopod Productions | Ivan Juric

Welcome to Pulse: Amplify, where we sit down with the leaders and changemakers shaping the future of health.

A recent Nature Medicine study went viral after reporting that ChatGPT Health under-triaged more than half of emergency cases when tested using clinician-written scenarios. The finding raised serious concerns about whether consumer AI tools are safe for medical triage.

But researchers from Macquarie University’s Australian Institute of Health Innovation took a closer look at the study design and suspected the results might reflect the evaluation format rather than the AI’s clinical capability.

In this episode of Pulse Amplify, Louise and George speak with David Fraile Navarro about their follow-up study testing five frontier AI models across more than a thousand trials. Their research suggests that when AI systems are evaluated using more natural, patient-style interactions rather than exam-style prompts, triage performance improves significantly.

The discussion explores why prompt structure, forced answer formats, and restrictions on clarifying questions can dramatically alter model behaviour, and why designing realistic evaluation methods is essential as millions of people begin using AI for health advice.

The conversation also examines broader questions:
How should AI triage tools be evaluated?
What role should clinicians play in AI-mediated care?
And what do patients need to know before trusting AI with health decisions?

References

Ramaswamy A. et al. (2026). ChatGPT Health performance in a structured test of triage recommendations. Nature Medicine.
https://www.nature.com/articles/s41591-026-04297-7

Fraile Navarro D, Magrabi F, Coiera E. (2026). Evaluation format, not model capability, drives triage failure in the assessment of consumer Health AI. Zenodo.
https://doi.org/10.5281/zenodo.18975048

Connect with David Fraile Navarro: LinkedIn

Visit Pulse+IT.news to subscribe to breaking digital news, weekly newsletters and a rich treasure trove of archival material. People in the know, get their news from Pulse+IT – Your leading voice in digital health news.

Follow us on LinkedIn Louise | George | Pulse+IT

Follow us on BlueSky Louise | George | Pulse+IT

Send us your questions pulsepod@pulseit.news

Production by Octopod Productions | Ivan Juric