Faces of Digital Health

In this episode of The Agentic Patient, a Faces of Digital Health series on how patients are using AI to find answers the healthcare system didn't give them, Tjasa Zajc talks to Elena Ikonomovska, CEO and Co-Founder of Diadia Health. She stopped trusting chatbots with her own health data — after building an AI company on the problem they create.

Elena talks about her two-and-a-half-year journey that followed her mother's death and her own dismissed symptoms — and the causal-reasoning AI she built in response.

Guest: Elena Ikonomovska, CEO, Co-Founder & Chief AI Officer, Diadia Health

What the conversation covers:

- Why Ikonomovska calls generative AI's confident wrong answers "faithful hallucinations"

- Building a causal-reasoning engine instead of using large language models for clinical decisions

- Why lab "normal" ranges differ by genetics — and what that means for your bloodwork

- Her own dismissed thyroid and pre-diabetes symptoms, and what a two-and-a-half-year diagnosis journey actually looks like

- The risk of self-diagnosing from ChatGPT-style tools, and what to ask any AI health platform about your data

- Why she believes AI should strengthen, not replace, the doctor-patient relationship

- Early clinical results: agreement rates with physician judgment and reductions in diagnostic trial-and-error

- The equity risk in AI-driven healthcare — who gets access to validated tools, and who doesn't

Chapters:

00:00 Intro: why The Agentic Patient series exists

02:30 Meet Elena Ikonomovska and the case for causal AI

03:35 From two decades in machine learning to health AI

07:07 What the model needs: blood panels, genetics, and interactions

09:45 Elena's own diagnosis journey — two and a half years to answers

11:32 Why she wouldn't trust chatbots with her health today

12:40 "Faithful hallucinations": the hidden risk in generative AI

15:04 Inside a causal-reasoning engine built without generative AI

17:32 What happens when clinicians outsource reasoning to chatbots

21:06 Redefining "normal": genetics and personalized lab ranges

27:08 Women's health data gaps and the DTC testing boom

28:13 Strengthening, not replacing, the doctor-patient relationship

31:42 Chatbot safety advice: what patients should never share

38:07 Clinical validation, agreement rates, and what's next

Faces of Digital Health:

Website: https://www.facesofdigitalhealth.com

LinkedIn: https://www.linkedin.com/company/faces-of-digital-health

Spotify: https://open.spotify.com/show/4cElKJHrauyP6QJQaCkvdY

Apple Podcasts: https://podcasts.apple.com/gb/podcast/faces-of-digital-health/id1194284040

Newsletter: https://fodh.substack.com

The Agentic Patient series hub: https://www.facesofdigitalhealth.com/agentic-patient-blog

#DigitalHealth #AIinHealthcare #PatientAdvocacy #WomensHealth #HealthTech #ClinicalAI #TheAgenticPatient

The viral robot videos are choreography, not capability. A historian who has lived with a humanoid robot for a year explains what they still can't do.

Emily Kate Genatowski is a historian and AI domestic robotics researcher who bought a humanoid robot, lived with it for over a year, and turned the experience into a TED talk. In this episode of Faces of Digital Health, she separates the demo-reel hype of 2026 from what home robots can actually deliver — and what that means for aging populations, caregiver shortages, and healthcare systems hoping robots will help people stay independent at home.

She explains why companion robots and chore robots are still entirely separate machines, why her robot cannot get into a car, why retirees are often more enthusiastic about robots than younger workers — and why she believes we are living through a "digital Engels pause," where productivity rises faster than the mechanisms that distribute its benefits.

Guest: Emily Kate Genatowski, historian and AI domestic robotics researcher

Her TED talk: https://www.youtube.com/watch?v=rIg-Zt7bFHY

What the conversation covers:

- Humanoid robot hype in 2026 vs real-world capability

- Living with a humanoid robot: logistics, frustration, and transport

- Robots for elderly care and aging in place

- Companion robots vs domestic chore robots — why they're separate devices

- The "digital Engels pause": AI productivity without shared gains

- US, China, and EU approaches to AI and robotics regulation

- Emotional attachment to robots and robot design

- Dual-use risks: humanoid robots and drones in warfare

- "One soul, many bodies": the future architecture of home robots

CHAPTERS

CHAPTERS:

00:00 Intro

02:25 Welcome: a historian who lives with a humanoid robot

03:40 2026 robot hype: why the viral videos are choreography

05:45 Why buy a robot? How the year-long experiment began

07:28 From board games to policy: what the project became

08:57 Robots for aging in place — and who's actually optimistic

09:53 Companion robots vs chore robots: two separate machines

13:55 The digital Engels pause: productivity without shared gains

20:09 US, China, Europe: three regulatory cultures for AI and robotics

25:55 Why the robot travels in a box — and can't get into a car

29:58 Robot guilt: emotional attachment without eyes

33:14 Policy, not technology, as the bottleneck for elderly care robots

35:02 Dual use: drones, warfare, and where robotics could go wrong

37:25 One soul, many bodies: the bifurcated future of home robots

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Website: https://www.facesofdigitalhealth.com

Newsletter: https://fodh.substack.com

LinkedIn: https://www.linkedin.com/company/faces-of-digital-health

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Apple Podcasts: https://podcasts.apple.com/gb/podcast/faces-of-digital-health/id1194284040

#DigitalHealth #Robotics #HumanoidRobots #AgingInPlace #HealthTech #AI

Hallucination is not the biggest risk in clinical AI. Omission is — and it is far harder to detect.

John Laursen, SVP at IMO Health, has spent his career on the layer of healthcare AI that gets the least attention: clinical terminology and the semantic data infrastructure underneath every model deployed in a hospital. IMO Health's terminology has been built and curated since 1994 and now sits behind roughly 12 billion terminology search transactions a year across US provider organisations and every major EHR.

In this interview with Tjaša Zajc, Laursen makes the case that structured data was necessary but is no longer sufficient. AI reasoning across a thirty-year patient chart needs semantic continuity — an understanding that clinical language recorded in the 1990s and language recorded today can mean the same thing. Without it, health systems are investing in models that cannot reliably interpret their own records.

The conversation also covers what happens when ambient AI scribes get it wrong, why accumulated clinical data has become a computational cost rather than an asset, and why clinician trust is the constraint that determines how fast clinical AI can move.

Guest:

John Laursen — Senior Vice President, IMO Health (Chicago, US)

Host:

Tjaša Zajc — Faces of Digital Health

What the conversation covers:

- Why omissions, not hallucinations, are the underrated risk in clinical AI

- What a semantic layer does that structured data alone cannot

- How clinical terminology maps to SNOMED CT and ICD-10 — and why those code sets were built for different purposes

- Ambient AI scribes: what happens when a model mishears or over-infers a diagnosis

- The billing and clinical consequences of an error entering the patient record

- Why problem lists hundreds of entries long now cost money in token burn

- Patient-generated and AI-generated content entering the EHR, and why health systems resist it

- Translating lay language into clinical terminology without losing specificity

- Ambient documentation, billing intensity and friction with payers

- How data quality expectations differ between the US, the NHS, the Gulf states and Singapore

- Who governs clinical data as coding complexity increases

- Why AI performance breaks down on rare disease and the difficult 20% of cases

- Knowledge graphs as a grounding source for clinical AI models

- What health systems should require from AI vendors before clinical deployment

Chapters:

02:20 Why the data layer decides what clinical AI can do

03:27 Inside IMO Health: 12 billion terminology searches a year

05:36 Keeping terminology current: SNOMED, ICD-10 and clinical governance

07:35 The semantic bridge: why structured data alone is not enough

10:17 Patient language versus clinical language in the record

12:23 When an ambient scribe mishears: clinical and billing consequences

14:53 Omissions, bloated problem lists and unnecessary token burn

19:12 Outside the US: the NHS, the Gulf, Singapore and coding complexity

20:46 Who governs clinical data as complexity increases

23:35 Patient-side AI recorders and resistance to external data

26:08 Ambient documentation, billing intensity and payer friction

29:21 The last 20%: rare disease, model limits and AI governance

33:38 Grounding, clinician trust and the cost of misfiring

Faces of Digital Health:

Website: https://www.facesofdigitalhealth.com

Newsletter: https://fodh.substack.com

Spotify: https://open.spotify.com/show/4cElKJHrauyP6QJQaCkvdY

Apple Podcasts: https://podcasts.apple.com/gb/podcast/faces-of-digital-health/id1194284040

LinkedIn: https://www.linkedin.com/company/faces-of-digital-health

#digitalhealth #healthcareAI #clinicalinformatics #EHR #ambientAI #interoperability #healthdata #SNOMED #healthIT #medicalcoding

OpenEvidence didn't leave Europe because of regulation alone — its ad-and-data business model never fit the European market.

When OpenEvidence, the $12B clinical AI search platform used daily by over 40% of US physicians, withdrew from the EU and UK in April 2026 citing the EU AI Act, European clinicians lost a tool many had quietly adopted. In this episode, Philippe Habets — physician-scientist and CEO of Amsterdam-based EvidenceHunt — argues the story is as much about business models as regulation: ad-funded clinical search and selling clinician search behaviour to pharma don't transfer to Europe, where clinicians distrust anything that looks commercial.

We also examine what AI evidence search is measurably doing inside hospitals: more uniform knowledge across teams, fewer junior-to-senior consultations, faster decisions — and the open question of whether that convergence improves care or narrows clinical thinking.

Guest: Philippe Habets, MD PhD, CEO & co-founder, EvidenceHunt (Amsterdam)

What the conversation covers:

- Why OpenEvidence left Europe: EU AI Act vs the ad-based and data-selling business model

- What hospitals did after OpenEvidence's exit — governance, procurement, shadow AI use

- How AI literature search changes clinical decision making and medical hierarchies

- Automation bias, tunnel vision and who is liable when AI is wrong

- Guardrails in practice: PII stripping, refusing clinical advice, reformulating case questions into research questions

- Why LLM answers differ between tools — and the omission problem in complex patients

- Living guidelines: automating systematic literature reviews and guideline updates (some protocols are 17 years old)

- Should patients have access to the same evidence tools as clinicians?

- EvidenceHunt vs OpenEvidence: data sources, GDPR, medical device regulation

- What won't change in healthcare AI in the next three years

Previous episode with Philippe Habets (2023): https://www.youtube.com/watch?v=F8tC0B4NvpM

CHAPTERS

00:00 Introduction

03:11 OpenEvidence leaves Europe: what it meant for a European competitor

04:31 No user spike — but hospitals started asking questions

06:54 What EvidenceHunt is: from PubMed frustration to systematic reviews

11:35 How clinicians actually adopt AI evidence tools

13:46 Uniform knowledge, fewer senior consultations: measured effects on clinical thinking

16:24 Tunnel vision, automation bias and the liability question

18:04 Guardrails in practice: PII stripping and refusing clinical advice

20:31 The omission problem: evidence is group statistics, patients are N of 1

23:26 The real reason OpenEvidence left: ads, data-selling and European distrust

29:03 Should patients have the same evidence tools as clinicians?

31:51 Why disclaimers aren't enough — safeguards must be enforced in the product

33:43 Living guidelines: automating updates for protocols up to 17 years old

40:00 The biggest product challenge: too many features, one clean interface

41:40 What won't change in healthcare AI in the next three years

FACES OF DIGITAL HEALTH

Website: https://www.facesofdigitalhealth.com

Newsletter: https://fodh.substack.com

LinkedIn: https://www.linkedin.com/company/faces-of-digital-health

Spotify: https://open.spotify.com/show/4cElKJHrauyP6QJQaCkvdY

Apple Podcasts: https://podcasts.apple.com/gb/podcast/faces-of-digital-health/id1194284040

#digitalhealth #healthcareAI #OpenEvidence #EUAIAct #clinicaldecisionsupport #evidencebasedmedicine #healthtech

AI models are "eager to please" — and in healthcare, that's a liability. So what should LLMs never be allowed to do in clinical software?

Three years after GPT-3 reached the public, frontier models have largely converged in capability. In this episode, Robert Tovornik, Innovation Lead at Better — the healthcare IT company building on openEHR — explains why the real differentiator in healthcare AI is no longer the model but the data layer underneath it. He makes the case for keeping clinical coding and terminology in deterministic systems, confining LLMs to retrieval and orchestration, and validating AI the way you'd come to trust a colleague: through experience, not certification.

Guest: Robert Tovornik, Innovation Lead, Better

What the conversation covers:

- Why frontier LLMs are converging — and why context now matters more than model capability

- What AI should never do in clinical software: inference vs retrieval

- Why ICD-10 and SNOMED coding should stay in deterministic systems, not ChatGPT

- How to validate non-deterministic AI systems when unit tests no longer work

- Automation bias: what happens when users stop checking AI outputs

- Conversational EHRs — solving the "missing button" problem in clinical interfaces

- Vibe coding vs regulated clinical software: why one iterates in hours and the other in years

- An ambient AI scribe built in two weeks — deployed in India, stalled in Europe

- EU AI Act, data residency laws, and the cost of compliance

- Digital twins, ambient AI, and what hospitals should invest in before deploying AI

Faces of Digital Health explores how healthcare systems around the world adopt digital technologies and AI.

🔗 Website: https://www.facesofdigitalhealth.com

🎧 Spotify: https://open.spotify.com/show/4cElKJHrauyP6QJQaCkvdY

🎧 Apple Podcasts: https://podcasts.apple.com/gb/podcast/faces-of-digital-health/id1194284040

📰 Newsletter: https://fodh.substack.com

💼 LinkedIn: https://www.linkedin.com/company/faces-of-digital-health

#healthcareAI #openEHR #digitalhealth #healthIT #EHR #clinicalAI #healthcareinnovation

02:20 Three years of GPT: from model capability back to data and context

04:26 How AI changed software development inside an openEHR vendor

06:13 Clients now arrive with AI-informed (and misinformed) requirements

09:05 Why clinical coding belongs in deterministic systems, not ChatGPT

11:27 "Eager to please": why LLMs shouldn't be trusted with inference

14:03 Validating non-deterministic AI when unit tests no longer work

16:50 How do you trust AI? The same way you trust a colleague

18:03 Regulation, compliance costs, and the automation bias problem

20:09 Conversational EHRs and the missing-button problem

23:02 Vibe coding vs iterating regulated clinical software

25:23 Users are building AI experience faster than health systems

27:50 An ambient scribe built in two weeks — adopted in India, stalled in Europe

29:16 Data quality as the differentiator between good and bad AI systems

31:10 Ambient AI, operation prep, and the digital twin horizon