My Seven Health Tech Observations From HLTH Europe 2026 Event
AI is moving from writing clinical notes to checking them. "Human-in-the-loop" is becoming a product, not a footnote. Nearly everyone is racing to own the data underneath. And everything in between...
(This article is originally published at BiopharmaTrend).
I spent last week in Amsterdam, reporting for BiopharmaTrend at HLTH Europe 2026, arguably Europe’s flagship healthcare innovation event, hosting more than 5,000 attendees.
It is my second HLTH experience, and two additions to this year’s program stood out: last year’s Pharma & Life Sciences Spotlight grew into a full two-day Global Pharma Summit aimed at C-suite from 30-plus pharma and biotech organizations across 20-plus countries, and the floor gained a dedicated AI @ HLTH zone with its own stage dedicated completely to the progress of artificial intelligence in healthcare and clinical research.
The organizers framed the agenda around whether healthcare AI has broken the Gartner hype cycle, skipping the trough of disillusionment and landing straight into clinical workflows, patient portals, and everyday tools, or not…?
The reality is, as always, nuanced and ambiguous, but whatever it is, I have gathered several observations from various interviews and discussions, the HLTH Europe show floor, and the announcements timed to the event, that might be shaping this area in the second half of 2026 and beyond.
The Rise of “Verification layer”
There is an increasing number of companies that are positioning healthcare AI tools/services not as a producer of clinical content/material, but as a means to verify it.
For instance, Berlin-based aiomics has put into production a verification layer that sits on top of hospital IT systems, turning the unstructured documents a hospital receives (e.g. faxes, referrals, scans, dictation) into a structured, sourced patient record. Rather than generating clinical text and hoping it is correct, the system audits every statement against the original source document through a multi-agent protocol — the company’s answer to what it calls the central risk of generative AI in medicine: fluent, plausible output built on bad data. It is live across more than 30 hospital sites in Germany, certified to ISO 27001, and is being independently evaluated at the Charité in Berlin.
The same positioning showed up elsewhere. Guideways AI is launching EU MDR Reviewer and QMS Reviewer, putting AI on the reviewer’s side of a full CE-certification submission to catch issues before they cause delays. AMBOSS pointed to a benchmark result: its clinical AI search agent, LiSA, ranked first overall and in the top safety tier of the NOHARM study from Stanford, Harvard and collaborators, which assessed 31 AI systems across 100 real clinical scenarios using 12,747 expert annotations — a result the company credits to drawing only from curated clinical sources. And FiveBrane’s Datametior turns the lens on the data itself, scoring how AI-ready a dataset is before anyone spends money training on it.
“Human-in-the-loop” is probably a product category, not a caveat
The phrase recurred often enough that it read less like a reassurance and more like a product. Kimberly Noel, Roche’s Global Lead of AI Advocacy and Digital Health, pointed to Human-In-the-Loop GmbH, a company built entirely around the idea. Its stated sole mission is promoting AI transformation while staying compliant with the EU AI Act and ISO/IEC 42001, and it argues that “human in the loop” is not a metaphor but the operating model.
The same language showed up at the primary-care practice Hausärzte am Spritzenhaus, which runs AI-based task steering under what it calls a strict human-in-the-loop architecture, and at Longevity AI, whose Florence 2.0 is built to keep the doctor at the center of every patient interaction.
EU digital sovereignty as a selling point
Among the European companies, regulatory readiness and data sovereignty kept appearing as selling points rather than mere “disclaimers”. Datum Agent positioned itself as the first vertically integrated, EU-sovereign AI platform purpose-built for healthcare — GPU infrastructure hosted in the EU, patient data processed within EU jurisdiction, and, in its words, “without dependency on US hyperscalers.”
Already mentioned earlier aiomics made a point of running entirely within the EU and holding ISO 27001 certification. iCure said its Cardinal v2 is prepared for NIS-2, the EU AI Act and the European Health Data Space… and so on.
Triage and navigation
Some of the more detailed numbers came from tools that route patients to the right level of care.
Infermedica, with Healthdirect Australia, published peer-reviewed research in Mayo Clinic Proceedings: Digital Health analyzing more than 1.55 million real-world virtual triage interactions, reporting emergency-department intent down from 36.7% to 24.6%, engagement with lower-acuity care more than doubled, and patient uncertainty about where to seek care down 99.6%.
A study published in Nature Health of Ada Health’s integration into South Africa’s MomConnect platform reported that, among 968 participants, the share seeking care more than doubled from 17% to 43%, with recommendations rated safe by an independent physician panel in 98% of cases.
And Tucuvi reported that its clinical voice agent, LOLA, was associated with a 43.7% reduction in urgent COPD admissions and a 40% drop in hospital stays at Hospital Ribera Povisa.
The longevity is becoming a mainstream term
Longevity was hard to miss. For instance, Longevity AI announced launching an AI platform for preventive-care practices, built on more than 1.6 million longitudinal health records and used by systems including Maccabi and Clalit.
Reya.ai, a 2026 Health 2.0 Award winner and NVIDIA Inception member, announced Reya Essentials to lower the barrier to entry for new longevity clinics.
Beyond the crowded field of GLP-1 companion apps, Lumen made the case for metabolic intelligence as the next step after GLP-1’s effect on obesity treatment — tracking how the body responds to the drugs over time to support engagement and long-term outcomes, drawing on more than 100 million measurements from over 350,000 users.
Unfiltered released its Longevity 100 power list as part of an investment report, and Kearney and Microsoft put out a report on technology and longevity alongside a panel asking, pointedly, whether longevity is just prevention rebranded.
Adherence and persistence
Medication non-adherence kept surfacing as a problem people had put numbers to.
BrightInsight, with Sanofi, tracked more than 6,000 specialty patients and presented real-world evidence on persistence at 12 months, opening on the figure that 71% of specialty patients abandon therapy within a year.
PACE Clinical, which is integrating the BEAMER project’s B-COMPASS model, cited EU figures of 200,000 premature deaths a year and over €125 billion in avoidable healthcare costs tied to non-adherence.
Observia’s SPUR behavioral diagnostic, which the company says is validated by seven publications, is being built into an explainable-AI adherence agent. And Redcare Pharmacy’s smartpatient was mentioned to be working with UCB to support patients with Hidradenitis Suppurativa across the care journey.
Data as a healthcare and pharma asset was the biggest topic
The throughline beneath the AI was data itself. Lots of companies presented solutions in the data infrastructure and data aggregation areas. For instance, Lumen pointed to more than 100 million real-world metabolic measurements from over 350,000 users; PAICON’s PaiX Navigator offered access to disease datasets spanning 60-plus countries, pitched explicitly at closing the representation gap in medical AI; and Leumit opened 23 years of clinical EMR community-care data to European innovators.
Others focused on making existing data usable. Like, Data4Life’s Data2Evidence platform went live at Mount Sinai, opening secure access to more than 12.4 million de-identified patient records, while Briya launched a no-code environment for running real-world evidence and epidemiological studies on clinical data.
A panel on data as pharma’s biggest asset, featuring speakers from AstraZeneca, Roche, Memorial Sloan Kettering, and Germany’s Health Data Lab, broadly agreed on why so much of it stays locked up: the obstacles are less technical than about governance and trust, and bolting AI onto broken interoperability mostly just surfaces the breakage faster.
One speaker noted that the patient is still the one stitching their records together, re-explaining their history to a new healthcare provider roughly 40% of the time. Others pointed to what connected data makes possible — using a combination of large language models to read entire patient charts ahead of a visit and flag likely trial eligibility. One panelist cited a national claims dataset of some 75 million people, opened to research after a change in German law.
