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AI evals and KPIs are essential for scaling healthcare AI, proving reliability, measuring impact and driving safe, measurable adoption in EMR platforms.
Via Emmanuel Capitaine
A summary of the ways in which AI has changed health care, how AI is being used in health care and by the FDA, and the legislation surround AI, data, and healthcare...
Via Emmanuel Capitaine
Philips Future Health Index 2025 reveals artificial intelligence (AI) and digital health tech can help solve cardiac care crisis...
Via Emmanuel Capitaine
L’entreprise californienne a recruté deux profils de haut niveau pour structurer son offensive dans le secteur de la santé. Avec le lancement de GPT‑5, plus performant en matière de raisonnement médical, OpenAI entend développer des produits directement destinés aux cliniciens et aux patients.
Via Philippe Marchal
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Scooped by
Lionel Reichardt / le Pharmageek
September 5, 4:06 AM
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Malgré son potentiel à alléger la charge administrative, améliorer le diagnostic ou optimiser les flux hospitaliers, l’intelligence artificielle (IA) reste peu déployée dans les hôpitaux européens, pointe un rapport de la Commission européenne. Plusieurs freins d’ordre technique, réglementaire, organisationnel ou culturel freinent son adoption. La Belgique n’échappe pas à cette dynamique.
A good night’s sleep is no longer seen as a luxury but as a foundation for health, performance, and mental clarity.Yet, sleep habits vary widely across...
Via Emmanuel Capitaine
Background Artificial intelligence (AI) technologies are expected to “revolutionise” healthcare. However, despite their promises, their integration within healthcare organisations and systems remains limited. The objective of this study is to explore and understand the systemic challenges and implications of their integration in a leading Canadian academic hospital. Methods Semi-structured interviews were conducted with 29 stakeholders concerned by the integration of a large set of AI technologies within the organisation (e.g., managers, clinicians, researchers, patients, technology providers). Data were collected and analysed using the Non-Adoption, Abandonment, Scale-up, Spread, Sustainability (NASSS) framework. Results Among enabling factors and conditions, our findings highlight: a supportive organisational culture and leadership leading to a coherent organisational innovation narrative; mutual trust and transparent communication between senior management and frontline teams; the presence of champions, translators, and boundary spanners for AI able to build bridges and trust; and the capacity to attract technical and clinical talents and expertise. Constraints and barriers include: contrasting definitions of the value of AI technologies and ways to measure such value; lack of real-life and context-based evidence; varying patients’ digital and health literacy capacities; misalignments between organisational dynamics, clinical and administrative processes, infrastructures, and AI technologies; lack of funding mechanisms covering the implementation, adaptation, and expertise required; challenges arising from practice change, new expertise development, and professional identities; lack of official professional, reimbursement, and insurance guidelines; lack of pre- and post-market approval legal and governance frameworks; diversity of the business and financing models for AI technologies; and misalignments between investors’ priorities and the needs and expectations of healthcare organisations and systems. Conclusion Thanks to the multidimensional NASSS framework, this study provides original insights and a detailed learning base for analysing AI technologies in healthcare from a thorough socio-technical perspective. Our findings highlight the importance of considering the complexity characterising healthcare organisations and systems in current efforts to introduce AI technologies within clinical routines. This study adds to the existing literature and can inform decision-making towards a judicious, responsible, and sustainable integration of these technologies in healthcare organisations and systems.
Artificial intelligence (AI) is a powerful and disruptive area of computer science, with the potential to fundamentally transform the practice of medicine and the delivery of healthcare. In this review article, we outline recent breakthroughs in th
While large language models (LLMs) achieve near-perfect scores on medical licensing exams, these evaluations inadequately reflect the complexity and diversity of real-world clinical practice. We introduce MedHELM, an extensible evaluation framework for assessing LLM performance for medical tasks with three key contributions. First, a clinician-validated taxonomy spanning 5 categories, 22 subcategories, and 121 tasks developed with 29 clinicians. Second, a comprehensive benchmark suite comprising 35 benchmarks (17 existing, 18 newly formulated) providing complete coverage of all categories and subcategories in the taxonomy. Third, a systematic comparison of LLMs with improved evaluation methods (using an LLM-jury) and a cost-performance analysis. Evaluation of 9 frontier LLMs, using the 35 benchmarks, revealed significant performance variation. Advanced reasoning models (DeepSeek R1: 66% win-rate; o3-mini: 64% win-rate) demonstrated superior performance, though Claude 3.5 Sonnet achieved comparable results at 40% lower estimated computational cost. On a normalized accuracy scale (0-1), most models performed strongly in Clinical Note Generation (0.73-0.85) and Patient Communication & Education (0.78-0.83), moderately in Medical Research Assistance (0.65-0.75), and generally lower in Clinical Decision Support (0.56-0.72) and Administration & Workflow (0.53-0.63). Our LLM-jury evaluation method achieved good agreement with clinician ratings (ICC = 0.47), surpassing both average clinician-clinician agreement (ICC = 0.43) and automated baselines including ROUGE-L (0.36) and BERTScore-F1 (0.44). Claude 3.5 Sonnet achieved comparable performance to top models at lower estimated cost. These findings highlight the importance of real-world, task-specific evaluation for medical use of LLMs and provides an open source framework to enable this.
Both Democratic and Republican administrations have long tried to make data sharing seamless across the disjointed U.S. health care system.
AI-native clinical documentation startup Ambience Healthcare raises $243M - SiliconANGLE...
Via Emmanuel Capitaine
Once cautious, OpenAI, Grok, and others will now dive into giving unverified medical advice with virtually no disclaimers.
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For nursing to shape its future in an AI-driven world, its leaders and practitioners must be present, informed, and engaged in every step of its development and deployment.
Via Emmanuel Capitaine
Des scientifiques ont mis au point un modèle d'intelligence artificielle (IA) capable de prédire la probabilité de maladies chez un individu, et la prévalence dans une population, plusieurs années en amont, en se basant sur la même technologie que ChatGPT d'OpenAI.
Via Philippe Marchal
Un robot doté d’intelligence artificielle a effectué sans assistance humaine une ablation de la vésicule biliaire sur un cadavre de porc, avec un taux de réussite de 100 %. Ce jalon, rapporté par New Scientist, ouvre la voie à des interventions automatisées sur des patients vivants d’ici dix ans.
Via Philippe Marchal
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Scooped by
Lionel Reichardt / le Pharmageek
September 5, 4:06 AM
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Le premier baromètre sur l’adoption de l’intelligence artificielle (IA) dans les hôpitaux en Belgique lancé par AI4Belgium, avec Le Spécialiste et le bureau de consultants EY a livré ses résultats. 95% des répondants trouvent que le développement de l’IA est un enjeu important pour les hôpitaux mais 59% des répondants ne perçoivent pas le développement de l’IA comme une priorité stratégique des établissements dans lesquels ils exercent.
This systematic review examines the cost-effectiveness, utility, and budget impact of clinical artificial intelligence (AI) interventions across diverse healthcare settings. Nineteen studies spanning oncology, cardiology, ophthalmology, and infectious diseases demonstrate that AI improves...
Via Emmanuel Capitaine
L'objectif des chercheurs du Massachusetts Institute of Technology (MIT) était de trouver des moyens totalement nouveaux pour lutter contre la résistance aux antimicrobiens.
Via François BARRAU
Healthcare organizations have realized that Artificial intelligence (AI) can provide a competitive edge through personalized patient experiences, impr…
AI that can accelerate research could drive a century of technological progress over just a few years. During such a period, new technological or political developments will raise consequential and hard-to-reverse decisions, in rapid succession. We call these developments *grand challenges*.
These challenges include new weapons of mass destruction, AI-enabled autocracies, races to grab offworld resources, and digital beings worthy of moral consideration, as well as opportunities to dramatically improve quality of life and collective decision-making.
We argue that these challenges cannot always be delegated to future AI systems, and suggest things we can do today to meaningfully improve our prospects. AGI preparedness is therefore not just about ensuring that advanced AI systems are aligned: we should be preparing, now, for the disorienting range of developments an intelligence explosion would bring.
The Holistic Evaluation of Language Models (HELM) serves as a living benchmark for transparency in language models. Providing broad coverage and recognizing incompleteness, multi-metric measurements, and standardization. All data and analysis are freely accessible on the website for exploration and study.
UPDATED 5 P.M | The White House met with health tech business leaders on Wednesday to discuss advancements in health tech, including a public-private partnership called the Health Tech Ecosystem Initiative.
Background: Digital nursing technologies (DNTs) are a promising solution to address challenges in health care systems, such as demographic shifts, nursing shortages, or difficulties in retaining nurses.
Via Emmanuel Capitaine
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![[2505.23802] MedHELM: Holistic Evaluation of Large Language Models for Medical Tasks | GAFAMS, STARTUPS & INNOVATION IN HEALTHCARE by PHARMAGEEK | Scoop.it](https://img.scoop.it/R3Nf-aV-zFCPDr3s1jV_-zl72eJkfbmt4t8yenImKBVvK0kTmF0xjctABnaLJIm9)
