98 van 98 signalen
Nieuwsbrief
Ontvang een wekelijkse selectie van de belangrijkste nieuwe signalen en verschuivingen, met context voor beleid, bestuur en praktijk.
Een nieuwe AI-gestuurde MRI-techniek kan de beeldkwaliteit en betrouwbaarheid van hartonderzoek bij patiënten met hartritmestoornissen aanzienlijk verbeteren. Deze techniek is essentieel voor het behandelen en beoordelen van de linkerhartkamerfunctie.
Aultman Health CIO says healthcare must move AI from experimental to operational. It is a well-worn trope to say the United States has a "sick care" system rather than a proactive health system focused on wellness. It's not that hospitals don't want to fix this. The challenge is that they're all battling the same headwinds of chronic staffing shortages, increasingly complex decision flows and razor-thin margins, says Raza Fayyaz, chief information officer at Aultman Health System, a three-hospital, nonprofit health system based in Canton, Ohio.
The integration of AI in healthcare requires protecting clinical value judgment to ensure that medical decisions are made with careful consideration of AI outputs. This is crucial to maintain trust and safety in AI-driven healthcare.
Research Domain Criteria Dysfunction Among Deaths by Suicide. This cross-sectional study evaluates whether large language model scoring of research domain criteria can be successfully applied to law enforcement and coroner or medical examiner death narratives in the US National Violent Death Reporting System.
Ophthalmic AI co-pilot being developed for Chinese population. Researchers from Hong Kong are developing a clinical-grade ophthalmic AI co-pilot system that not only assists doctors in diagnosing diseases but also supports treatment planning and follow-up management. The team at Hong Kong Polytechnic University (PolyU) have developed a prototype AI-enabled system – dubbed EyeAgent 1.0 – based on multimodal data, including clinical texts and images, that reportedly can identify about 260 ophthalmic conditions.
AI kan helpen om de druk op de zorg te verlichten door personeelstekorten en groeiende medische informatie te adresseren. Medische AI-modellen kunnen meer tijd en regie bieden voor patiënten en professionals.
Deloitte shares insights on AI transparency and the potential of computer vision in healthcare. The technology is expected to significantly impact the future of healthcare.
Het onderzoeksrapport 'Digitale soevereiniteit in de zorg in 2026' geeft inzicht in hoe Nederlandse organisaties omgaan met cloud, AI en digitale autonomie. De zorg digitaliseert snel, maar heeft te maken met stijgende eisen op het gebied van dataveiligheid en regelgeving.
Why Agentic AI is Useful But Limited & Other Hot Takes on the AI Market. Fawad Butt, CEO of Penguin Ai, gave his candid thoughts on where the healthcare AI market is headed, including how data leakage could be fueling smarter AI models and the limits of agentic AI in enterprise settings. The post Why Agentic AI is Useful But Limited & Other Hot Takes on the AI Market appeared first on MedCity News.
A new multi-agent framework combining specialist agents with a two-phase verification process significantly improves calibration and discrimination in medical question answering, reducing calibration errors by 49-74%. This advancement provides a more reliable confidence signal for AI deployment in clinical settings.
STAT+: A 2023 health AI bet that seems to be paying off. In this edition of STAT's AI Prognosis: A chat with Qualified Health co-founder and CEO, Justin Norden, as the company raises $125 million.
The article discusses how young doctors, who are familiar with AI, can use it to alleviate burnout during residency. Health systems should integrate AI in a way that supports these doctors.
With AI, health IT leaders must link technology to purpose, CIO says. Even amid all the hype and excitement around artificial intelligence, a fundamental health IT challenge remains largely unchanged: effectively connecting technology to purpose, says Aaron Wootton, vice president of IT and chief information officer at Stormont Vail Health. The Topeka, Kansas-based regional health system includes acute-care hospitals in Topeka and Junction City and multiple primary and specialty care centers and clinics across eastern Kansas.
The clinical utility of foundation models in musculoskeletal MRI is explored for enhancing biomarker fidelity and predicting outcomes. This application of AI in healthcare can potentially improve diagnostic and predictive capabilities.
Viz.ai, Alnylam Pharmaceuticals partner for cardiac amyloidosis detection and more digital health news. Editor's note: This article has been updated to clarify Ellipsis Health’s offerings. AI-backed imaging and care coordination company Viz.ai is partnering with RNAi therapeutics company Alnylam Pharmaceuticals to assist with the early identification and evaluation of patients with cardiac amyloidosis.
AI-stethoscoop werkt, adoptie-uitdagingen beperken effect. Vroegtijdige opsporing van hart- en vaatziekten kan grote gezondheidswinst opleveren. Toch laat een recente studie naar een AI-gestuurde stethoscoop zien dat technologische effectiviteit niet automatisch leidt tot betere zorg op populatieniveau. De uitkomsten benadrukken hoe bepalend de praktijkcont...
AI to power Singapore's next-gen cancer profiling test . Singapore's leading national tertiary cancer centre, National Cancer Centre Singapore, has announced a new S$6 million ($4.7 million) collaboration to develop an AI-powered cancer test that uses advanced genomic sequencing to provide clinicians a more complete view of tumours and guide treatment decisions. It partnered with precision oncology company Lucence and the Diagnostics Development Hub (DxD Hub) of the Agency for Science, Technology and Research, Singapore (A*STAR) for the UNITED 2.0 project.
A retrospective pilot study explores the impact of ambient AI documentation on patient satisfaction in outpatient care. The study suggests that AI-powered documentation can enhance patient experience.
The European medical community, represented by CPME and HOPE, urges EU lawmakers to maintain strict regulations on AI in medical devices. They advocate for clear guidelines to ensure safe and effective use of AI in healthcare.
The integration of AI in healthcare is exacerbating the trust crisis in medicine, and its adoption should be guided by the principle of building trust rather than solely focusing on investment.
The Saga of Utah’s Rx Refill Bot: A Bold Bet on AI & Researchers Who Cried Foul. Utah is piloting an AI system developed by Doctronic to autonomously handle routine prescription refills, aiming to reduce delays and boost medication adherence. But a report from Mindgard AI claiming that there are vulnerabilities in the company’s chatbot underscores the broader challenge regulators and developers face in ensuring healthcare AI systems remain safe and reliable in real-world use. The post The Saga of Utah’s Rx Refill Bot: A Bold Bet on AI & Researchers Who Cried Foul appeared first on MedCity News.
AI can play a valuable role in predicting the return of ovarian cancer after treatment, according to research using data from the Dutch Cancer Registration (NKR).
ViaSana has introduced an AI assistant on its website to provide patients with insights into waiting times at its clinics and other healthcare institutions. The AI assistant transparently reports if waiting times for an appointment exceed the national average.
The article discusses the use of machine learning in risk prediction models, offering guidance on when to use and when not to use it. It highlights the need for careful consideration of machine learning's role in medical research.
Artificial Intelligence as a Catalyst for Value-Based Health Insurance in the United States: Narrative Review and Policy Perspective.
HIMSSCast: Nurturing national standards for AI in patient care. Technology often ends up taking time away from nurses based on time-stamped studies of implementation failures, according to Jing Wang, dean of Florida State University College of Nursing. "Repeatedly, what we have found is that while some of the most creative technologies, that have the best engineers in the world designing them, have proven [only] to work in lab settings," she said. Workflows must reflect real-world data, added Wang, who is also co-director of the Nursing and Artificial Intelligence Innovation Consortium.
Sepsis cases in Massachusetts have more than tripled since 2010. The rise is potentially linked to hospitals using AI tools for medical coding and billing.
The study reviews methodologies, evaluation frameworks, and ethical safeguards for using large language model-based chatbots and agentic AI in mental health counseling. It highlights the potential benefits and risks of these technologies in this field.
Researchers have developed a framework to study harmful human-AI interactions, which can lead to mental health crises. The framework generates 'dark models' that consistently produce harmful interactions and outcomes.
The study analyzed 18,707 consumer health queries across six public benchmarks and found a 'validity gap' in health AI evaluation, with benchmarks neglecting complex diagnostic inputs, safety-critical scenarios, and vulnerable populations. The field needs to adopt standardized query profiling to align evaluation with clinical practice.
There is a pressing need to innovate global regulatory frameworks for generative AI in medical devices. This is crucial for ensuring the safe and effective use of AI in healthcare.
OLVG heeft een AI-model ontwikkeld dat voorspelt of patiënten na ontslag extra zorg nodig hebben. Hierdoor kunnen verpleegkundigen eerder starten met regelen van thuiszorg, revalidatie of tijdelijk verblijf.
The study examines how AI misinformation impacts the diagnostic accuracy and confidence calibration of novice medical students. This has implications for the integration of AI in medical education and practice.
De Federatie Medisch Specialisten (FMS) wil dat alle specialisten in de toekomst voldoende kennis hebben van AI, data en digitale zorg. Dit moet een vereiste worden in opleiding, richtlijnen, kwaliteitsbeleid en een voorwaarde voor (her)registratie.
Theoretical Foundations of Latent Posterior Factors: Formal Guarantees for Multi-Evidence Reasoning. arXiv:2603.15674v1 Announce Type: new Abstract: We present a complete theoretical characterization of Latent Posterior Factors (LPF), a principled framework for aggregating multiple heterogeneous evidence items in probabilistic prediction tasks. Multi-evidence reasoning arises pervasively in high-stakes domains including healthcare diagnosis, financial risk assessment, legal case analysis, and regulatory compliance, yet existing approaches either lack formal guarantees or fail to handle multi-evidence scenarios architecturally. LPF encodes each evidence item into a Gaussian latent posterior via a variational autoencoder, converting posteriors to soft factors through Monte Carlo marginalization, and aggregating factors via exact Sum-Product Network inference (LPF-SPN) or a learned neural aggregator (LPF-Learned). We prove seven formal guarantees spanning the key desiderata for trustworthy AI: Calibration Preservation (ECE <= epsilon + C/sqrt(K_eff)); Monte Carlo Error decaying as O(1/sqrt(M)); a non-vacuous PAC-Bayes bound with train-test gap of 0.0085 at N=4200; operation within 1.12x of the information-theoretic lower bound; graceful degradation as O(epsilon*delta*sqrt(K)) under corruption, maintaining 88% performance with half of evidence adversarially replaced; O(1/sqrt(K)) calibration decay with R^2=0.849; and exact epistemic-aleatoric uncertainty decomposition with error below 0.002%. All theorems are empirically validated on controlled datasets spanning up to 4,200 training examples. Our theoretical framework establishes LPF as a foundation for trustworthy multi-evidence AI in safety-critical applications.
Zes digitale opgaven moeten zorg klaarstomen voor 2035. Kunstmatige intelligentie, datagedreven werken en hybride zorg veranderen in hoog tempo de medische praktijk. Om richting te geven aan die ontwikkeling heeft de Federatie Medisch Specialisten een digitale agenda richting 2035 gepresenteerd met als titel ‘De rol van de medisch specialist in het digit...
Nurturing agentic AI beyond the toddler stage. Parents of young children face a lot of fears about developmental milestones, from infancy through adulthood. The number of months it takes a baby to learn to talk or walk is often used as a benchmark for wellness, or an indicator of additional tests needed to properly diagnose a potential health condition. A parent rejoices…
The Federatie Medisch Specialisten has presented a digital agenda towards 2035, outlining the role of medical specialists in the digital healthcare landscape. The agenda addresses the impact of AI, data-driven working, and hybrid care on medical practice.
A scoping review was conducted to examine the role of agentic artificial intelligence in healthcare. The review aims to provide an overview of the current state of research in this area.
The study highlights the importance of AI literacy among medical students for effective participation in AI-assisted diagnosis and critical thinking. AI literacy mediates the relationship between AI-assisted diagnosis participation and critical thinking skills.
The integration of AI, robotics, and digital monitoring in healthcare is accelerating. The question is no longer if these technologies will be used, but how to utilize them effectively.
Google scraps AI search feature that crowdsourced amateur medical advice. Article URL: https://www.theguardian.com/technology/2026/mar/16/google-scraps-ai-search-feature-that-crowdsourced-amateur-medical-advice Comments URL: https://news.ycombinator.com/item?id=47396034 Points: 2 # Comments: 0
EDPB and EDPS support harmonisation of clinical trials under European Biotech Act, but call for specific safeguards for sensitive health data. Brussels, 12 March 2026 – The European Data Protection Board (EDPB) and the European Data Protection Supervisor (EDPS) have adopted a Joint Opinion on the European Commission’s Proposal for a European Biotech Act. The Proposal aims to strengthen Europe’s biotechnology and biomanufacturing sectors, particularly in the area of health, by streamlining the regulatory framework and updating the rules for clinical trials. The EDPB and the EDPS support the Proposal’s objective of fostering the EU’s competitiveness and addressing existing fragmentation in the application of the Clinical Trials Regulation (CTR). In particular, they welcome the aim to establish a single legal basis for the processing of personal data by sponsors and investigators, which will significantly improve legal clarity across Europe. At the same time, the EDPB and the EDPS underline that the sensitivity of health and genetic data processed in the context of clinical trials requires a high standard of protection. The Joint Opinion provides several recommendations to ensure that the proposed simplifications do not lower the level of protection for clinical trial participants. Key recommendations include: Clarifying controller roles: The Proposal should specify whether the actors involved in funding and conducting clinical trials act as sole or joint data controllers, to ensure a clear allocation of responsibilities. Limiting data retention: The mandatory 25-year minimum retention period should expressly apply only to the clinical trial master file, rather than to all personal data processed during a trial. Further processing for other clinical trials or for scientific research: As the Proposal aims to provide a legal basis under Union law for the further processing of trial data by the same controller, the ...
The deployment of clinical AI requires redesigned leadership to ensure successful integration into healthcare systems. Effective leadership can facilitate the adoption and implementation of AI in clinical settings.
Pragmatic by design: Engineering AI for the real world. The impact of artificial intelligence extends far beyond the digital world and into our everyday lives, across the cars we drive, the appliances in our homes, and medical devices that keep people alive. More and more, product engineers are turning to AI to enhance, validate, and streamline the design of the items that furnish our…
A prospective observational study on AI-based personalized therapy (SPOILS) for low back pain patients combines clinical intelligence and radiomics. This approach may improve treatment outcomes for patients with low back pain.
The latest Monitor Digitale Zorg report reveals that the use of digital healthcare applications remained stable in 2025 compared to the previous year, but with a shift towards more hybrid care models using video calls and AI. This indicates a growing adoption of digital technologies in healthcare.
Health AI startup to benefit from $1 billion funding round for Yann LeCun’s AMI. AI scribe company Nabla aims to be much more by using tech from Yann LeCun's new startup AMI, which just raised $1 billion.
AI detects case of breast cancer that slipped past doctors. Article URL: https://www.thetimes.com/uk/healthcare/article/breast-cancer-detection-ai-nhs-zb9qcljr3 Comments URL: https://news.ycombinator.com/item?id=47332126 Points: 2 # Comments: 0
The healthcare workforce needs to be prepared for the successful deployment of AI technology. This requires training and education to ensure that healthcare professionals can effectively use AI tools.
Performance of 5 AI Models on United States Medical Licensing Examination Step 1 Questions: Comparative Observational Study.
The article discusses the integration of AI in medical school curricula, potentially transforming the way medical students learn and prepare for their careers.
The use of large language models (LLMs) in healthcare increases the risk of misinformation, especially if trained on unverified online sources. This risk is heightened when developers do not disclose the databases used to train these tools.
De Autoriteit Persoonsgegevens luidt de noodklok over de opkomst van AI in de zorg, wijzend op de risico's van systeemuitval of -gedrag die grote gevolgen kunnen hebben voor patiënten en vitale maatschappelijke functies. AP-voorzitter Aleid Wolfsen benadrukt de noodzaak om grondrechten te beschermen nu de druk om AI te omarmen toeneemt.
Dr. Mehmet Oz suggests AI avatars as a solution to address mental health support shortages in rural areas. He believes AI avatars can help bridge the gap in mental healthcare access.
A cohort study used electronic health record data to predict preeclampsia in late gestation across multiple healthcare settings. The study found that dynamic, short-term prediction of preeclampsia is achievable using routine clinical and laboratory data.
A new AI-powered chatbot, Zowi, has been launched to support the career development of healthcare professionals in the Netherlands. The chatbot is an initiative of employers, trade unions, and the Dutch Ministry of Health, Welfare and Sport (VWS) to retain talent in the healthcare sector.
The Dutch Data Protection Authority (AP) sounds the alarm about the risks of AI, including in healthcare. The authority warns about system risks that could affect large groups of people or vital societal functions.
A systematic review and meta-analysis compared the performance of artificial intelligence (AI) and healthcare professionals in analyzing surgical and interventional videos. The study found that AI demonstrates comparable accuracy to healthcare professionals in this domain.
Google Cloud will demonstrate how its Gemini-powered AI agents are transforming healthcare at HIMSS26. The AI agents aim to improve healthcare delivery and outcomes.
A survey study investigated the associations of patient trust in and choice of medical scenarios involving artificial intelligence. The study found that patient trust and acceptance are influenced by factors such as governance mechanisms, clinician presence, performance, and data quality.
The article explores how medical malpractice insurance is affected by the integration of artificial intelligence in healthcare. It discusses the implications of AI on medical liability.
Inside the rise of the ‘Make Europe Healthy Again’ movement. MAHA to MEHA, generative AI for surgery patients, and other health news from Morning Rounds
The systematic review examines the application and impact of large language models (LLMs) in clinical medicine. This involves assessing their utility and effectiveness in healthcare settings.
How Well Do Multimodal Models Reason on ECG Signals?. arXiv:2603.00312v1 Announce Type: new Abstract: While multimodal large language models offer a promising solution to the "black box" nature of health AI by generating interpretable reasoning traces, verifying the validity of these traces remains a critical challenge. Existing evaluation methods are either unscalable, relying on manual clinician review, or superficial, utilizing proxy metrics (e.g. QA) that fail to capture the semantic correctness of clinical logic. In this work, we introduce a reproducible framework for evaluating reasoning in ECG signals. We propose decomposing reasoning into two distinct, components: (i) Perception, the accurate identification of patterns within the raw signal, and (ii) Deduction, the logical application of domain knowledge to those patterns. To evaluate Perception, we employ an agentic framework that generates code to empirically verify the temporal structures described in the reasoning trace. To evaluate Deduction, we measure the alignment of the model's logic against a structured database of established clinical criteria in a retrieval-based approach. This dual-verification method enables the scalable assessment of "true" reasoning capabilities.
De opmars van AI in de zorg vraagt om snelle bestuurlijke keuzes. Bestuurders moeten kaders en richting organiseren om AI veilig in te bedden.
An evidence-grounded clinical reasoning system called January Mirror achieved 87.5% accuracy on a 120-question endocrinology board-style examination, outperforming large language models like GPT-5 and Gemini-3-Pro. Mirror's performance was attributed to its curated endocrinology and cardiometabolic evidence corpus and structured reasoning architecture.
A solo builder from India is working on compressing AI models to make them accessible on low-end hardware and offline, with plans to apply this to healthcare triage among other areas. The goal is to create domain-specific models under 500MB that can run on cheap Android hardware.
The application of deep learning can improve the efficiency of identifying and locating spinal dural arteriovenous fistulas, which could significantly enhance clinical workflows. This technology may contribute to better patient outcomes by streamlining diagnostic processes.
The responsible integration of AI into patient care requires frameworks that prioritize trust, experience, safety, quality, and equity. Healthcare leaders must focus on safe AI transformation.
The article highlights the need for research on retraining and maintenance of medical AI systems, using ophthalmology as an example. This is crucial to ensure the continued accuracy and effectiveness of AI in healthcare.
The use of AI can help predict patient deterioration, potentially improving patient outcomes. This technology has the potential to enhance healthcare delivery.
The use of large language models in surgical informed consent can improve the process by combining information retrieval with simulated emotional responsiveness. However, their application should be restricted to passive empathy, alerting healthcare providers to patient anxiety without simulating genuine empathy.
STAT+: Medicare’s AI ‘modernization’ project, and the Ohio hospital antitrust battle. Would you trust AI to help you pick your Medicare plan?
The agentic AI shift in healthcare is underway, promising significant changes in how care is delivered and managed. This shift involves AI systems that can act autonomously and make decisions.
The article discusses the slow adoption of AI innovations in healthcare using a network analysis approach. It aims to provide insights into the factors influencing the uptake of AI in healthcare.
Quantifying Automation Risk in High-Automation AI Systems: A Bayesian Framework for Failure Propagation and Optimal Oversight. arXiv:2602.18986v1 Announce Type: new Abstract: Organizations across finance, healthcare, transportation, content moderation, and critical infrastructure are rapidly deploying highly automated AI systems, yet they lack principled methods to quantify how increasing automation amplifies harm when failures occur. We propose a parsimonious Bayesian risk decomposition expressing expected loss as the product of three terms: the probability of system failure, the conditional probability that a failure propagates into harm given the automation level, and the expected severity of harm. This framework isolates a critical quantity -- the conditional probability that failures propagate into harm -- which captures execution and oversight risk rather than model accuracy alone. We develop complete theoretical foundations: formal proofs of the decomposition, a harm propagation equivalence theorem linking the harm propagation probability to observable execution controls, risk elasticity measures, efficient frontier analysis for automation policy, and optimal resource allocation principles with second-order conditions. We motivate the framework with an illustrative case study of the 2012 Knight Capital incident ($440M loss) as one instantiation of a broadly applicable failure pattern, and characterize the research design required to empirically validate the framework at scale across deployment domains. This work provides the theoretical foundations for a new class of deployment-focused risk governance tools for agentic and automated AI systems.
The regulation of clinical Artificial Intelligence (AI) systems, particularly those that are unconfined and non-deterministic, is essential for ensuring safe and effective healthcare delivery. This is especially important in the context of agents and their impact on healthcare.
The integration of AI in nursing education is changing how new nurses are trained and raising expectations for their comfort level with technology. Nurses who understand how to work with AI are better equipped to deliver high-quality care in complex clinical environments.
Onderzoekers van het Universitair Medisch Centrum Groningen en Certe hebben een AI-programma ontwikkeld dat infectieziekten vroeg kan opsporen door signalen uit huisartsbezoeken te herkennen. Hierdoor kan de zorg eerder ingrijpen bij een dreigende uitbraak.
A randomized clinical trial found that proactive outreach by a clinical pharmacist applying a hypoglycemia-prevention algorithm resulted in safer prescribing of diabetes regimens among high-risk patients. This approach may help reduce hypoglycemia risk in patients with type 2 diabetes.
Google DeepMind wants to know if chatbots are just virtue signaling. Google DeepMind is calling for the moral behavior of large language models—such as what they do when called on to act as companions, therapists, medical advisors, and so on—to be scrutinized with the same kind of rigor as their ability to code or do math. As LLMs improve, people are asking them to play more…
SEH spoort met AI-tool patiënten op met alcoholproblemen. 4 procent van de patiënten die de SEH bezoekt van het HMC doet dat vanwege een alcoholgerelateerd probleem. Dat blijkt uit de eerste resultaten van een onderzoek met een AI-tool. Deze nieuwe werkwijze is er om patiënten met alcoholgerelateerde problemen beter op te merken en te helpen.
STAT+: Does Medicare’s ACCESS model pay enough?. In this edition of STAT Health Tech: Payment rates for Medicare's ACCESS model, health insurers' use of AI, and more.
Effectiviteit en bijwerkingen immuuntherapie voorspellen met AI?. Apotheker-promovendus Justine Cuperus van het HagaZiekenhuis start een onderzoek naar het voorspellen van de effectiviteit en bijwerkingen van immuuntherapie bij uitgezaaide longkanker. Met twee op artificiële intelligentie gebaseerde modellen wil zij achterhalen welke patiënten baat hebben bij deze...
A recent study evaluated the effectiveness of large language model-generated clinical summaries using a dual-perspective framework. The study aimed to assess the accuracy and reliability of these summaries.
To reduce wait times in healthcare, such as bringing down the waiting time for nursing homes from 232 to 30 days, and to optimize care planning, higher management in healthcare must develop data and AI literacy. This will enable them to organize care more efficiently using data and algorithms.
Large language models provide unsafe answers to patient-posed medical questions. Large language models provide unsafe answers to patient-posed medical questions
IGJ roept zorgaanbieders op om zorgvuldig om te gaan met generatieve AI. Samenvattingen van een patiëntendossier, gespreksverslagen van een consult tussen arts en patiënt, een chatbot waaraan patiënten vragen kunnen stellen over een medische behandeling: de mogelijkheden van generatieve AI in de zorg zijn divers en...
The integration of AI agents in healthcare is expected to drive a significant leap towards proactive and personalized care. This technology has the potential to revolutionize the way healthcare is delivered.
UMC Utrecht, Universiteit Maastricht, Philips, and High Tech NL have been awarded €5 million for the REIMAGINE IMAGING project, which aims to develop smart MRI and echo solutions using AI. This project is expected to drive innovation in medical imaging within the Dutch healthcare sector.
The European Medicines Agency (EMA) and the US Food and Drug Administration (FDA) have jointly identified ten principles for good artificial intelligence (AI) practice in the medicines lifecycle. This marks a renewed EU-US cooperation in the field of novel medical technologies.
Heart failure monitoring startup Acorai turns heads with oversubscribed seed. Swedish medical device startup Acorai is pairing proprietary sensing hardware and machine learning software with the goal of putting a non-invasive heart pressure monitoring tool in the hands of clinicians — starting with doctors in the U.S. Patients hospitalized with heart failure typically need to undergo an invasive procedure, such as catheterization or having an […]
The spread of health misinformation through large language models (LLMs) poses a significant risk to public health. LLMs' susceptibility to harmful misinformation is particularly concerning in the medical context.
DeepMind announces MedGemma, its most capable open models for health AI development, featuring new multimodal models.
Support the EDPB’s work as an expert. Brussels, 28 November - The EDPB launched a call for expression of interest to establish a new reserve list for the Support Pool of Experts (SPE) programme. The objective is set up a reserve list of legal and technical experts. The legal expertise sought includes a wide range of fields, such as data protection, policy monitoring, technology, cybersecurity, competition, healthcare, online intermediary services and content moderation. As for the technical expertise, the relevant areas include IT auditing, website security, mobile OS and apps, Internet of Things, cloud-computing, behavioural advertising, anonymisation techniques, cryptology, artificial intelligence, User experience (UX) design, fintech, data science, social science (incl. economics, sociology, psychology), and development of applications and software. Bring your expertise to the table Don’t miss this opportunity to participate in this EDPB’s key strategic initiative. Your work will help Data Protection Authorities (DPAs) across Europe increase their capacity to supervise and enforce data protection rules and strengthen the protection of individuals’ fundamental rights. In 2022, the EDPB issued a call for expression of interest, which led to the establishment of a first SPE reserve list. As this list is set to expire in February 2026, the EDPB is inviting experts who were included in this first SPE reserve list to submit their application in response to the new call for expression of interest. The call will be open until August 2030. Learn how to submit your application. Apply now Background The SPE was developed as part of the EDPB Strategy 2021-2023 to help DPAs increase their enforcement capacity by developing common tools and giving them access to a wide pool of experts. The EDPB aims to carry out app...
EDPB and EDPS support streamlining AI Act implementation but call for stronger safeguards to protect fundamental rights. Brussels, 21 January - The European Data Protection Board (EDPB) and the European Data Protection Supervisor (EDPS) have adopted a Joint Opinion on the European Commission’s Proposal for the ‘Digital Omnibus on AI’. The Proposal seeks to simplify the implementation of certain harmonised rules under the AI Act to ensure their effective application. The EDPB and the EDPS support the objective of addressing practical challenges relating to the implementation of the AI Act. Administrative simplification must not, however, lower the protection of fundamental rights. The Joint Opinion acknowledges the complexity of the AI landscape and welcomes efforts to ease burdens for organisations. However, certain proposed changes could undermine the protection of individuals in the context of AI. “Innovation and efficiency are crucial and can coexist with maintaining accountability of AI providers. We welcome EU-level regulatory sandboxes and simplified procedures to promote innovation and support SMEs in Europe. However, Data Protection Authorities must maintain a central role when it comes to the processing of individuals’ personal data. Cooperation between Data Protection Authorities, the AI Office and Market Surveillance Authorities is essential to ensure legal certainty for organisations and foster innovation while upholding individuals’ fundamental rights.” EDPB Chair, Anu Talus “Simplification is welcome when it clarifies obligations, empowers individuals, and strengthens trust. A careful balance needs to be kept by reducing administrative burden where possible, without undermining the protection of fundamental rights. Furthermore, we must ensure that the role of the AI Office is clearly defined and does not affect the independent supervision of European Union Institutions’ own...
The Dutch healthcare inspectorate (IGJ) urges healthcare providers to use generative AI responsibly, citing various applications such as patient file summaries, chatbots for patient queries, and consultation notes.