Faces of Digital Health

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

AI couldn't cure his mother's stage 4 cancer. It caught three near-fatal errors, found a same-day appointment, and helped her leave on her own terms. When Pratik Desai's mother was diagnosed with stage four duodenal adenocarcinoma — a rare cancer with roughly 3,000 US cases a year — she was nearly discharged without an oncology appointment. Over the next 76 days, Desai used AI at her bedside, from 5am to 10pm, to understand each report, prepare for every appointment, and push a stretched health system to move at the pace her diagnosis demanded. This is a frank account of where AI helped, where it didn't, and the line he refuses to cross. This is a 1:1 interview in The Agentic Patient — a Faces of Digital Health series on how patients and caregivers actually use AI: which tools, which prompts, and which guardrails. GUEST Pratik Desai — New Jersey-based AI practitioner; caregiver and builder of a free, local AI tool for patients HOST Tjaša Zajc — Founder & host, Faces of Digital Health / The Agentic Patient WHAT THE CONVERSATION COVERS - Using AI to interpret a biopsy report and push for a same-day "stat" CT scan - Why AI and the doctors agreed on the care — and clashed on the speed - Finding a same-day oncology appointment through an AI-assisted network search - An error-riddled CT report the AI refused to read — and what it did to trust - Running three Claude "personas" as built-in second and third opinions - A local, open-source AI tool that keeps medical data off the cloud - How to prompt as a patient or caregiver: awareness, knowledge, advocacy — not diagnosis - Where AI failed him: prognosis, and the rule he broke under pressure - Defining quality of life when the outcome is already known CHAPTERS 0:00 How patients use AI — and the guardrails 1:20 Day one: a healthy mother, a diagnosis no one would name 3:34 The first prompt, and pushing for a stat CT scan 7:43 Using AI in the open: agreement on care, friction on speed 9:35 The counterfactual: 76 days with AI at the bedside 12:40 Finding a same-day appointment through a network search 13:40 The CT report the AI refused to read 15:50 When trust erodes: good faith, not competence 18:41 Why switching hospitals wasn't an option 21:54 Defining quality of life: her three goals 28:27 Three Claude personas, and a local private tool 35:12 How to prompt: awareness, knowledge, advocacy — not diagnosis 37:54 Where AI fell short, and the closing asks THE AGENTIC PATIENT SERIES New to the series? Start here → [PASTE PREVIOUS AGENTIC PATIENT EPISODE LINK] All episodes → https://www.facesofdigitalhealth.com/agentic-patient-blog MORE FROM FACES OF DIGITAL HEALTH 🌐 Website: https://www.facesofdigitalhealth.com 📨 Newsletter: https://fodh.substack.com 🎙 Podcast (Apple): https://podcasts.apple.com/gb/podcast/faces-of-digital-health/id1194284040 💼 LinkedIn: https://www.linkedin.com/company/faces-of-digital-health Pratik's tool Regana: https://github.com/RaganaCorp/openhealth-prototype-1 #DigitalHealth #HealthAI #AgenticPatient #PatientAdvocacy #AIinHealthcare #CancerCare #Caregiving #FacesOfDigitalHealth

AI ethicist Jess Morley: these chatbots are giving medical advice — so regulate them as medical devices. Part of The Agentic Patient, a Faces of Digital Health series on how patients actually use AI — which tools, which prompts, which safeguards. In this episode, host Tjaša Zajc sits down with Dr Jess Morley, Associate Research Scientist at the Yale Digital Ethics Center and a former AI subject-matter expert at the UK Department of Health and Social Care, for a clear-eyed account of where health AI is going wrong — and how to use it well anyway. Morley argues we systematically overestimate what these tools can do and underestimate the harm. She makes the case for "skeptical optimism," explains why bioethics principles built for one-to-one care break down against many-to-many AI harms, and reframes ambient scribes as inference engines rather than transcription services — with real consequences for coding, billing and patient records. Then she gets practical: the guardrails, prompts and habits patients (and clinicians) can use today. Guest: Dr Jessica Morley — Associate Research Scientist, Yale Digital Ethics Center; formerly UK Department of Health and Social Care and the Bennett Institute, University of Oxford. What the conversation covers: - Why "skeptically optimistic" is the honest position on health AI - AI adoption as "a hammer looking for nails" — and what needs-led design would look like instead - OpenEvidence, EU rules and the question of regulatory capture - The DeepMind–Royal Free case and why law alone isn't enough - Beneficence, non-maleficence, autonomy, justice — and where they fail for AI - Ambient AI scribes, miscoding, billing inflation and phantom tests - Paid vs free models and the widening access gap - The "ask why" rule and knowing when to walk away from a chatbot - Red-teaming your own assumptions and playing models off each other - Building a personal "harness" with skills so AI works from your history - The last-mile problem and the case for regulating LLMs as medical devices - Whether AI is narrowing how clinicians think Chapters: 02:50 — Intro: The Agentic Patient and the case for skeptical optimism 05:52 — "A hammer looking for nails": adoption pressure without a plan 07:25 — OpenEvidence, EU rules and regulatory capture 09:42 — The DeepMind–Royal Free lesson: why law needs ethics 13:29 — The bioethics principles and what they were built to do 19:40 — Autonomy, consent and the ambient-scribe problem 21:49 — Scribes as inference engines: miscoding, fraud and phantom tests 29:06 — Paid vs free models and the access gap 33:25 — Using AI safely: the "ask why" rule 37:38 — Knowing when to walk away: engagement design and degradation 44:58 — Red-teaming and playing models off each other 49:00 — Harnesses and skills: making the model work for you 51:38 — The last-mile problem and regulating AI as a medical device 58:00 — Does AI narrow the clinician's mind? The Agentic Patient series: https://www.facesofdigitalhealth.com/agentic-patient-blog Website: https://www.facesofdigitalhealth.com Newsletter: https://fodh.substack.com LinkedIn: https://www.linkedin.com/company/faces-of-digital-health

98% of patients welcome AI in their care — and still want a human in charge. That tension ran through the OECD and Spanish Ministry of Health conference on scaling AI in health (Madrid, late May 2026), and it frames this episode of Faces of Digital Health. Out of 38 OECD countries, only seven have a formal AI strategy and just over a tenth run workforce upskilling programmes — the ambition is outrunning the institutions meant to govern it. Host Tjaša Zajc brings together voices from across the conference to ask what actually has to change: regulation, trust, who gets a seat at the table, and the parts of the agenda nobody is funding. Featuring: - Eric Sutherland — Senior Economist, OECD - Aferdita Bytyqi — Executive Director & Founding Partner, Digital Transformations for Health Lab (DTH-Lab) - Erza Selmani — Research Fellow, DTH-Lab - Valentina Strammiello — Executive Director, European Patients Forum (EPF) - Dr Ricardo Baptista Leite — CEO, HealthAI (the Global Agency for Responsible AI in Health) - Dr Persephone Doupi — Senior Medical Officer, Finnish Institute for Health and Welfare; President, European Federation for Medical Informatics (EFMI) What the conversation covers: - Why trust — not capability — is the binding constraint on health AI adoption - The OECD readiness gap: AI strategies, HTA frameworks and workforce upskilling - How patients really feel about AI: consent forms, transparency, and keeping clinicians central - Why youth health and wellbeing keep getting left out of AI governance frameworks - Five recommendations to make the EU AI Act work for health and competitiveness - Coordinating the EU AI Act, MDR/IVDR and the European Health Data Space - Health technology assessment and reimbursement as the real barriers to scale - AI literacy and prevention: the most underweighted lever in the room Chapters: 0:10 — Welcome: AI in Health & the 2026 OECD Conference in Madrid 0:25 — Key Stats: Only 7 of 38 OECD Countries Have a Formal AI Strategy 2:10 — Eric Sutherland (OECD): We're Not Using Data as Effectively as We Could 3:11 — Afrodita & Erza (DTH Lab): Youth Health Is Missing from AI Governance Frameworks 5:12 — Valentina Stramello (EPF): 98% of Patients Are Positive About AI, But Trust Requires Transparency 7:14 — Dr. Ricardo Baptista Leite (Health AI): 5 Recommendations to Fix EU AI Policy for Health 10:53 — Persephone Doupi (EFMI): We Must Prioritize AI Literacy and Shift Healthcare Toward Prevention — 🎧 Listen: https://www.facesofdigitalhealth.com 📩 Newsletter (incl. written OECD conference summary): https://fodh.substack.com 💼 LinkedIn:https://www.linkedin.com/company/12594967/ 🌐 Site: https://www.facesofdigitalhealth.com #DigitalHealth #HealthAI #AIinHealthcare #HealthPolicy #EUAIAct #EHDS #ResponsibleAI #PatientVoice #HealthTechAssessment #HealthTech

Doctors are using ChatGPT in clinic right now — and some of them don't care about privacy. Three operators on what that means for healthcare AI. Recorded live at health.tech in Basel, this panel from Faces of Digital Health unpacks the convergence reshaping clinical software: ambient AI scribes, agentic AI in healthcare, on-device LLMs, and the regulatory drag (MDR, EU AI Act, EHDS) that is widening the gap between what clinicians actually use and what hospitals are allowed to buy. Host Tjaša Zajc is joined by: Jonathan Bringas — CEO & Founder, Lapsi Health (Kaiku: FDA-cleared AI stethoscope, ambient scribe and clinical assistant in one device) Blaž Triglav — CEO, Mediately (drug information platform, 1M+ HCPs across Europe) Amanda Herbrand — Clinical data modelling consultant, formerly University Hospital Basel What the conversation covers: — Why EHR data fragmentation is the precondition AI hasn't solved — Shadow AI: why clinicians trust ChatGPT more than enterprise tools (and the agency hypothesis behind it) — The convergence of stethoscopes, scribes, drug information and decision support into one workflow layer — ROI in healthcare AI: financial, time, clinical accuracy — and Herbrand's fourth dimension, user satisfaction — "Doctors were the original vibe coders": the 2,000 Excel spreadsheets running European hospitals — Why FDA-cleared beats MDR in 2026 sales cycles, and what Chile's regulatory minimalism tells us — The asymmetry that will break European medtech: applicants using AI to build, regulators forbidden from using AI to assess — On-device AI, ambient computing, AGI in clinical workflows — and the de-skilling risk no one wants to discuss ⏱ Chapters 00:00 — Opening: AI agents, vibe coding, and what doctors actually want 01:30 — Data fragmentation: the precondition AI hasn't solved (Amanda Herbrand) 02:30 — Keiku: collapsing stethoscope, scribe and assistant into one device 05:15 — The convergence reshaping healthcare AI — and the shadow AI in clinic 07:30 — Why doctors trust ChatGPT more than enterprise tools: the agency hypothesis 10:30 — ROI: financial, time, clinical accuracy — and Herbrand's fourth dimension 15:30 — Choosing solutions: modular requirements and FDA-cleared moats 19:30 — EHDS and the missing connector in European standardisation 21:00 — "Doctors were the original vibe coders": the 2,000 spreadsheet problem 24:30 — The two-speed world: regulated medicine vs the Wild West 28:00 — Why Chile's regulatory minimalism beats Europe's MDR 30:30 — Agentic AI vs regulators: the asymmetry that will break European medtech 33:30 — On-device AI, AGI, and the deskilling no one wants to discuss 🎧 View the video podcast: https://www.youtube.com/watch?v=fciFwMmIfRc&t=174s 📩 Newsletter: https://fodh.substack.com 🔗 LinkedIn:   / dashboard   🌐 facesofdigitalhealth.com #HealthcareAI #DigitalHealth #AmbientAI #AgenticAI #ClinicalAI #EHR #EHDS #MedTech #HealthTech

Most conversations about agentic AI in healthcare get stuck on capability. This one is about the gap between capability and deployment — and what closes it. Aashima Gupta, Global Director of Healthcare Strategy and Solutions at Google Cloud, argues that healthcare's bottleneck isn't vision; it's courage. The processes are documented poorly or not at all, AI fluency programs reach a fraction of employees who want them, and most enterprises are running agents without the harnesses — grounding, evaluation, red-teaming — that production deployment actually requires. Meanwhile patients navigate three different "clock speeds" (annual insurance cycles, shifting provider rosters, Medicare pricing) that bear no relation to the timeline of their own health. We cover the European vs US deployment posture, the difference between agents-with-agency and rule-based AI, why Highmark's library of one million internal prompts matters, Google Cloud's full-stack efficiency play (TPU Ironwood, Gemini, the 40% data-centre electricity reduction DeepMind delivered years ago), and the multi-agent "harnesses" — including the red/blue/green team architecture — that are starting to make production-grade healthcare AI plausible. Video: https://youtu.be/rLtaxQLgCg0?si=JDP6kK97_tYsFoSb Newsletter: https://fodh.substack.com/ Agentic Patient Series: https://www.facesofdigitalhealth.com/agentic-patient-blog

The Agentic Patient is here — and most healthcare systems don't have a plan for it. In this special reverse-role episode of Faces of Digital Health, Eric Sutherland interviews host Tjaša Zajc about what a year of using AI through her own chronic illness has actually taught her about patients, doctors, and the future of healthcare AI. 200 million people will ask ChatGPT a health question this week. The question is no longer whether patients will use AI to navigate their care — it's how to help them do it well, without harm, and in productive partnership with their clinicians. In this episode: - Why "patients know best" breaks down for chronic patients - The three archetypes AI is creating: minimizers, cyberchondriacs, and informed collaborators - What happens when doctors dismiss patients who use AI - A two-model verification method for cross-checking medical AI advice - Why "digital literacy" is the wrong name for the most important skill in modern healthcare - Two prompts that genuinely change what AI gives you back - What health ministries should actually do — and why we shouldn't offload patient AI education to doctors ⏱ CHAPTERS 00:00 Intro & reverse-role experiment 01:00 Eric Sutherland: "a data guy with personality" 01:36 A year as a chronic patient using AI 02:50 Same prompt, different LLMs — the trust problem 04:30 How The Agentic Patient series was born 06:00 Three patient archetypes 09:00 When doctors dismiss AI, patients start hiding 12:30 Dale Atkinson, HIMSS Europe, and data outside the clinic 13:30 200M weekly ChatGPT health queries — who's accountable? 15:30 The two-model cross-verification method 17:00 Making 7-minute appointments work with AI 19:30 Finland's Elements of AI — a model for healthcare 22:00 Why chronic patients may not know best 24:30 Five minutes with a health minister 27:00 Two prompts that change AI outputs 30:00 The agentic patient is a survivor, not a tech enthusiast 🎙 ABOUT THE AGENTIC PATIENT The Agentic Patient is a series under Faces of Digital Health exploring how patients and clinicians are actually using AI in healthcare — the wins, the harms, and the best practices emerging across cancer care, chronic disease, and primary care. 🔗 LINKS Newsletter: https://fodh.substack.com/p/the-agentic-patients-are-here More episodes: https://www.facesofdigitalhealth.com/agentic-patient-blog Tjaša Zajc on LinkedIn: https://www.linkedin.com/in/tjasazajc/ Eric Sutherland on LinkedIn: https://www.linkedin.com/in/esutherland272/?skipRedirect=true #AgenticPatient #AIinHealthcare #DigitalHealth #FacesOfDigitalHealth #HealthcareAI #ChatGPT #PatientEmpowerment #ChronicIllness #AIliteracy #MedicalAI #PatientAdvocacy #DigitalTransformation

When Demetri Giannikopoulos was diagnosed with multiple sclerosis, his community neurologist handed him a sheet with fifty medication options and told him to pick one. That was a long time ago. Today he's the Chief Innovation Officer at RadAI, overseeing how artificial intelligence gets deployed in radiology across US health systems — and he's spent two decades learning how to navigate a healthcare apparatus that, in his words, "is not designed for sick patients." In this conversation Demetri explains why the most valuable thing AI has done for him as a patient isn't clinical — it's the 50 pages of insurance underwriting documents he fed into ChatGPT to save several thousand dollars on a plan that looked, on paper, worse. He walks through his "red team" prompting technique, the error he caught in a radiology report where legacy speech-recognition software had dropped the word "no," and why he thinks the regulatory debate around AI in healthcare should look less like drug approval and more like how we regulate nuclear power. If you want a ground-level view of what AI can and cannot do inside the American medical system, this is where to start. Additional resource with prompt tips: https://aipatients.org/ Additional resource: Scanxiety toolkit: https://edge.sitecorecloud.io/americancoldf5f-acrorgf92a-productioncb02-3650/media/ACR/Files/Clinical/Patient-Family-Centered-Care/PFCC-Scanxiety-Toolkit-Brochure-Digital-Version.pdf Full Agentic Patient series: https://www.facesofdigitalhealth.com/agentic-patient-blog Detailed summary and tips from Demetri: https://www.facesofdigitalhealth.com/agentic-patient-blog/red-teaming-your-health-plan-demetri-giannikopoulos-on-responsible-ai-the-cures-act-and-what-patients-should-actually-do 6 tips on AI use for patients: https://fodh.substack.com/p/the-agentic-patients-are-here

Diana Ferro works at a major pediatric hospital in Italy, working on AI infrastructure, rare diseases, and — importantly — the International Alliance of Pediatric Centers on AI. Unlike the patient voices earlier in the Agentic Patient series, she sits on the other side of the consulting-room door. Her concerns are sharper, more specific, and more uncomfortable. She is not against patient AI use. She is watching what happens when desperate parents, teenagers in crisis, and sycophantic chatbots meet in a pediatric setting and she is trying to build the guardrails in real time. Diana frames AI in pediatric medicine as a two-front problem. On one front, Italian hospitals are racing to build the data infrastructure — EU-funded — to share research across institutions and turn billing data into diagnostic and predictive tools. On the other front, patients and families are already ahead of the system, using consumer LLMs in ways that clinicians are not trained to respond to. She describes three specific, observed harms she's seeing in pediatric practice: parents using AI to deny rare-disease diagnoses, adolescents using AI as a pro-eating-disorder coach by telling it they want to "lose weight to be healthy," young people with weak support systems finding AI easier to talk to than a clinician — including, she notes, in contexts tied to self-harm. The Agentic Patient Series: https://www.facesofdigitalhealth.com/agentic-patient-blog Agentic Patient 6 tips: https://fodh.substack.com/p/the-agentic-patients-are-here

Russ was diagnosed with bowel cancer in late 2021 and simultaneously with smoldering myeloma, aged 40. The smoldering myeloma has been inactive; the bowel cancer has progressed through multiple surgeries (bowel, liver, lung) and is now stage 4, on active chemotherapy. He runs AI for the business he works for, so his day job is adjacent to the technology. He blogs publicly about his disease at fcancerwith.ai and on LinkedIn. He is British; cared for by the NHS with some private care around the edges. He is articulate, technically fluent, and willing to pay roughly £200 a month for AI subscriptions.

Fidelia Cascini is Digital Healh expert engaged by the Italian Ministry of Health on European and national initiative. She recently wrote a book on secondary use of data in healthcare, read it here: Secondary Use of Electronic Health Data Public Health Perspectives, Use Cases and Challenges https://link.springer.com/book/10.100... This conversation covers the categorization of electronic health data, the importance of distinguishing different data types, and the potential applications and challenges in digital health. We also explore healthcare systems' digitalization, patient data privacy, the European Health Data Space (EHDS), and future public health perspectives. Learn about patient-generated data, clinical trials, and the significant role of digital health advancements in shaping the future of healthcare. www.facesofdigitalhealth.com Newsletter: https://fodh.substack.com/ Youtube: https://www.youtube.com/watch?v=tbSCXUhIDZ8 Show notes: 01:08 Diving into Different Categories of Health Data 01:35 Discussion on Secondary Use of Data in Europe 02:16 Call for Listener Feedback 03:07 Starting the Interview with Felia Kini 03:36 Importance of Distinguishing Data Types 04:38 Challenges in Using Different Data Sources 06:38 Real-World vs. Clinical Trial Data 07:19 Patient Preferences and Medication Adherence 09:31 Most Suitable Data for Secondary Purposes 10:32 Patient Generated Health Data 11:53 Discussions Among Health Ministries 13:09 Trustworthiness and Integration of Digital Tools 17:45 Patient Reported Outcomes in EHRs 19:35 European Health Data Space (EHDS) 29:55 Patient Control Over Data 33:47 Barriers to EHDS Success 37:13 Public Health Perspectives and Global Changes 40:14 Conclusion and Future Perspectives 42:20 Closing Remarks and Upcoming Events