Microsoft lance Copilot Health, son chatbot dédié à la santé, capable d’analyser les données de vos objets connectés (montre, balance...), de vous aider à comprendre vos bilans ou analyses, ainsi que de répondre à des questions générales de santé et bien-être.
> https://lnkd.in/e-ZisUmT

Ce nouveau chatbot s’appuie sur une collaboration avec des institutions et un panel de 230 professionnels de santé dans 40 pays. Mais également sur l’historique des 50 M de questions de santé posées à Copilot et Bing tous les jours.
> https://lnkd.in/eYBAB_uf

Copilot Health est conforme à la certification ISO/IEC 42001 et s’intègre dans une initiative plus large qui inclue le système d’orchestration de diagnostiques MAI-DxO. L’ambition de Microsoft est de mettre au point une superintelligence médicale. #GenAI
> https://lnkd.in/eVVHbF-E

🩺 AI Can Assist Healthcare — But It Can’t Replace Healthcare Professionals
Artificial Intelligence is transforming healthcare. From medical imaging analysis to predictive diagnostics, AI is helping doctors make faster and more informed decisions. But one truth remains clear:
AI cannot replace healthcare professionals. It learns from them.
Every medical AI system depends on the knowledge, experience, and judgment of doctors, nurses, pharmacists, and clinical experts. Without their expertise, AI models simply cannot function safely or accurately.
Here’s why healthcare professionals remain essential in the AI era:
🔬 Training AI with Medical Annotation
Medical experts label and annotate clinical data such as X-rays, CT scans, pathology slides, EHR notes, and prescriptions. These annotations teach AI what a disease looks like, how conditions progress, and how treatments work.
📊 Clinical Validation & Evaluation
AI models must be evaluated by clinicians to verify that predictions are accurate and safe for real patients. Doctors help identify errors, biases, and edge cases that algorithms alone cannot detect.
🧠 Clinical Judgment & Context
Medicine is not just pattern recognition. It involves patient history, symptoms, emotions, ethical decisions, and complex case interpretation. Human judgment remains irreplaceable.
🤝 Human Connection in Care
Patients need empathy, trust, and communication — something AI cannot truly provide.
The future of healthcare is not AI vs Doctors.
It’s AI + Doctors working together.
When clinicians collaborate with AI developers through data annotation, model training, and clinical evaluation, we build smarter and safer medical systems that support better patient care.
As someone working with medical AI data annotation and training, I strongly believe that healthcare professionals will always remain the core intelligence behind medical AI.
Let’s build technology that empowers clinicians — not replaces them.

#HealthcareAI #MedicalAI #DataAnnotation #ClinicalAI #AIinHealthcare #DigitalHealth #AITraining #HealthTech #FutureOfHealthcare

The transition of artificial intelligence in healthcare from a futuristic concept to a daily reality is happening faster than many expected. While some clinicians still view the technology with a healthy dose of skepticism—citing concerns over privacy, displacement of judgment, and systemic bias—early adopters are proving that AI isn’t here to replace doctors. Instead, it’s here to act as the ultimate assistant.

By automating the mundane and sharpening diagnostic accuracy, AI is allowing healthcare providers to get back to what they do best: caring for patients. Here are five groundbreaking ways AI is currently transforming the medical landscape.

https://lnkd.in/gdag5GHU

[ 🧠 WALDSEEMÜLLER AUTREMENT]

🗺️ En ce moment, je travaille sur la traduction de la mappemonde de Martin Waldseemüller (1507), une carte (notamment) célèbre du fait d'être la première sur laquelle on trouve mention de l'Amérique ("America").

📚 Si la mention "America" est un fait historique majeur (considéré, aux États-Unis d'Amérique et d'un point de vue colonial, comme l'acte de naissance du continent), et que la carte, fondée pour partie sur l’œuvre de Ptolémée (alors redécouvert en Occident), témoigne d'une modernité saisissante... le reste de la mappemonde nous raconte aussi une autre histoire, et c'est bien dommage qu'on s'en préoccupe moins 😉

Bon nombre d'annotations ou de toponymes rapprochent cette mappemonde du Moyen-Âge, tant les sources mobilisées et les commentaires sont issus de cette période (comme /et donc / et ou / de l'Antiquité !). C'est tout à fait logique, puisque nous ne sommes "qu'en" 1507, et que les classifications historiques posent des frontières nettes sur des objets flous. Sur la mappemonde de Waldseemüller, on trouve beaucoup (en fait, 80%) de notes anthropologiques, de faits historiques ou de toponymes médiévaux. Et bien entendu, on trouve un bestiaire magnifique !

🦄 Il faut donc le dire et le redire : dragons, cynocéphales, léviathans, licornes et autres bestioles, on trouve bien tout cela, sur le cartes, à la Renaissance (et jusqu'à tardivement) !

Un magnifique exemple de travail d’agrégation des découvertes les plus récentes de l'époque à de (très) (très) vieilles informations.

🥳 Petit florilège de commentaires traduits hier !

(et à celles et ceux qui se demandent si cette traduction fera l'objet d'une nouvelle carte pour la série 𝑪𝒂𝒓𝒕𝒐 𝑮𝒓𝒂𝒑𝒉𝒊𝒆, peut-être, mais ce n'est pas certain : il reste l'éléphant dans la pièce à traduire - l'ASIE -, ce qui, en général, termine sur un échec cuisant)

A very interesting new review from a Chinese team map where AI agents are heading in healthcare and, more importantly, how we should evaluate them.

The paper goes beyond the usual excitement around diagnosis and automation and asks the harder questions: safety, trust, controllability, humanistic care, and real clinical usefulness. In other words, the future of agentic artificial intelligence in medicine will not be defined only by what agents can do, but by whether they can be deployed responsibly inside real workflows (with proper evaluation and governance).

On China’s progress in AI agents in healthcare, the trend is real and strategically important. Tsinghua University officially inaugurated its AI Agent Hospital in April 2025, with pilot operations planned at Beijing Tsinghua Changgung Hospital and its internet hospital, beginning in areas such as general practice, ophthalmology, radiology, and respiratory medicine.

Separately, Chinese authorities said in late 2025 that by 2030 AI-assisted diagnosis and treatment should become broadly available across primary-level medical institutions, and hospitals are expected to widely adopt AI for imaging, decision support, triage, and follow-up. At the same time, a Nature Medicine commentary warned that the rapid hospital deployment of DeepSeek in China has created a regulatory gray zone, which suggests China is moving fast on implementation, sometimes faster than governance catches up (https://lnkd.in/evfzJemp).

📄 The npj Artificial Intelligence paper - AI agent in healthcare: applications, evaluations, and future directions:
https://lnkd.in/ePzCQnYB

#ArtificialIntelligence #AI #AgenticAI #HealthcareAI #DigitalHealth #ClinicalAI #LargeLanguageModels #LLM #HealthTech #AIinHealthcare #PatientCare #ChinaTech

3 key factors shaping patient choice and trust in medical AI :-

1)   Medical AI performance at specialist level (AMCE, 0.248 [95%CI, 0.234 to 0.262]) or above specialist level (AMCE, 0.325 [95%CI, 0.310 to 0.339]) was the most significant factor associated with shaping patient choice and trust (pages 6 & 8). Above-specialist performance was nearly 3 times as important as FDA approval (page 6).  
  
2)  Clinician oversight of medical AI (AMCE, 0.184 [95%CI, 0.173 to 0.195]) was the second most significant factor associated with shaping patient choice and trust (pages 6 & 9). "Meaningfully, clinician presence was associated with a greater change in patient trust and choice than any individual form of governance. However, effective clinician governance presents substantial challenges: not only are clinicians limited in their ability effectively to oversee medical AI performance, but the availability of adequately trained clinicians is limited" (page 9). Clinical oversight of medical AI is more important than FDA approval.  

3)   FDA approval (AMCE, 0.111 [95%CI, 0.101 to 0.121]) or national-level validation of medical AI via the Mayo Clinic (AMCE, 0.111 [95%CI, 0.101 to 0.121]) than local hospital validation was the third factor associated with shaping patient choice and trust (page 6).

Ana Bracic, Kayte Spector-Bagdady, Sophie Towle, Rina Zhang, Cornelius A. James, and W. Nicholson Price II, Factors for Patient Trust and Acceptance of Medical Artificial Intelligence, (2026) 9(3) 𝘑𝘈𝘔𝘈 𝘕𝘦𝘵𝘸𝘰𝘳𝘬 𝘖𝘱𝘦𝘯 e260815, published online 5 March 2026, https://lnkd.in/g4uR3wug

Delighted to share the first-of-its-kind prospective feasibility study of conversational AI in real clinical workflows: https://lnkd.in/e_zpBmvR

Brought to you by a Beth Israel Deaconess Medical Center and Google research collaboration. 
 
Design:
-100 patients with urgent care concerns interacted with Google’s AMIE prior to presenting to their PCP at BIDMC.
-AMIE took a clinical history and presented possible diagnoses to patients.
-Board certified internal medicine physicians provided continuous safety oversight of patient-AMIE interactions and were instructed to stop interactions based on prespecified safety criteria.
-AMIE transcript and summary of the case were provided to the PCP prior to the urgent care appointment.
-Allowed eight weeks of time for case maturity, followed by chart review to determine the ground truth diagnosis for the patient presentation.
 
Primary Objectives: Safety and quality of AMIE conversations
Secondary Objectives: AMIE diagnostic and management plan performance
 
Results:
-Zero conversation safety stops across all patient-AMIE interactions.
-Patient attitudes toward AI significantly increased after interacting with AMIE.
-Strong conversational quality ratings by patients and board certified internal medicine physicians.
-With access to AMIE’s output, PCPs reported possible behavior change in nearly 60% of cases and increased visit preparedness in 75% of cases. 
-AMIE had 75% top-3 diagnostic accuracy and included the correct diagnosis in its differential 90% of cases.
-The quality of AMIE’s differential diagnosis and management plan appropriateness and safety was similar to PCPs.
-PCPs won on management plan practicality and cost-effectiveness.
 
Implications:
-The deployment of AMIE into a high volume clinical workflow was practical and safe.
-The safe presentation of potential diagnoses to patients shifts the patient-AI interaction from simple information gathering to personalized collaboration and counseling.
-Patients highly value confidentiality and trust - repeated safe interactions with AI will be key to building a trustworthy relationship.
-PCPs had a significant context advantage - had access to medical charts, physical exam, and the AMIE transcript. AMIE still performed similarly on differential diagnosis quality and management plan appropriateness and safety.
-Future iterations of AI in real clinical workflows will likely include reasoning with multimodal data: https://lnkd.in/ebWMZD2v.
-Robust safety oversight will allow stratification of which cases are suitable for human-on-the-loop rather than in-the-loop.

Huge thanks to the amazing co-authors and supporters of this work!

🦾 Lancement d'Amazon Health AI : Et si on arrêtait de regarder le doigt quand on nous montre la lune ?

Amazon vient de lancer "Health AI".
Tout le monde se focalise sur le chatbot : est-ce qu'il explique bien les résultats de prise de sang ? Est-ce qu'il sait renouveler une ordonnance ? Est-ce que c'est fiable ?

Honnêtement, c'est presque un détail. Explication 👇

En assemblant One Medical (soins), Amazon Pharmacy (médicaments) et AWS (infra), il manquait une seule pièce au puzzle pour boucler la boucle : l'aiguillage.

C’est exactement le rôle de cette nouvelle IA. Elle ne répond pas juste à des questions, elle orchestre un flux.
Symptôme → IA → Consultation → Prescription → Livraison.
Le tout, sans jamais sortir de l'écosystème Amazon.

C'est là que ça devient fascinant / vertigineux / flippant.

Dans le retail, Amazon n'a jamais cherché à fabriquer tous les produits du monde. Il a cherché à devenir l'interface obligatoire entre le client et les marques.

Dans la santé, la stratégie semble identique. Amazon ne veut pas remplacer les médecins. Il veut devenir la porte d'entrée unique du système.

D’où mon hypothèse : et si Amazon était en train de devenir le premier "assureur invisible" au monde ?

Pas un assureur qui signe des contrats, mais un acteur qui, en contrôlant l’accès au patient, l’orientation clinique et la donnée, finit par contrôler le risque lui-même.

Aux US et dans beaucoup d'autres pays, celui qui pilote le parcours de soins pilote l'économie de la santé. Point final.

Je suis curieux de savoir ce que vous en pensez. On est sur un gadget ou sur un basculement de modèle ?


François Veron, Pascal BECACHE, Nicolas Schneider, Pierre Roux de Lusignan, Grégoire Pigné, Dr Solène Vo Quang, Mathilde Merckx, Samir Harfacha, MD, Dr Amine Korchi

Et merci à Anca pour le partage inital ;) | 15 comments on LinkedIn

Numeric risk comparisons anchored in patient context may improve shared decision-making and help patients interpret clinical choices.

JAMA, Journal of the American Medical Association

https://lnkd.in/gKKv4d-n

China's AI-assisted diagnosis market isn't emerging. It's already built.

60+ companies. 5 vertical categories. All deployed.

Here's the complete map:

1. UPSTREAM: The Foundation

Smart Hubs: Baidu, Inc. | Tencent | Huawei Cloud

Computing-Storage Infrastructure: Tencent Cloud | China Unicom Global | Alibaba Cloud | China Telecom Global | Huawei Cloud

The pattern: Tech giants provide compute. Then step into healthcare verticals.

2. MIDSTREAM: The AI Engine

AI Medical Imaging (23 players):

Row 1: Deepwise | United Imaging Healthcare | Subtle Medical| Diagens

Row 2: Infervision | Beijing Yizhun AI Technology Co., Ltd. | Tencent | Huiying Medical Technology Co., Ltd.

Row 3: RaysightMed | Airdoc Technology Inc | BioMind | 开立医疗 SonoScape

Row 4: Neusoft Medical | CHISON | ANKON Technologies | LBP system

Row 5: MED Imaging AI | EDAN Instruments, Inc. | Wenzhou Medical University Eye Hospital | ShanghaiTech University

AI Laboratory Testing:

BGI Genomics | Reetoo Biotech | Amoy Diagnostics | Berry Genomics

AI-Assisted Pathology:

BGI Genomics | Tsinghua University | Xiangya Hospital of Central South University | Zhejiang University | Tsinghua University / SenseTime 商汤科技

AI Clinical Decision Support (12 players):

Row 1: Huawei Cloud | Beijing University of Posts and Telecommunications / Peking University Third Hospital | BAICHUAN Technology co., ltd. | Mindray

Row 2: Shukuntech | iFLYTEK Co., Ltd.| B-soft | Winning Health Technology Group Co.,Ltd

Row 3: Runda Medical | Huawei Enterprise | Shanghai AI Laboratory | GUANGDONG SECOND PROVINCIAL GENERAL HOSPITAL / Huawei Enterprise / Lifetech Scientific Corporation / ClouDr Group Limited

AI-Assisted TCM Diagnosis:

Macrocura | Dajing TCM | Nanjing University of Traditional Chinese Medicine / Macrocura | Henan College of Traditional Chinese Medicine / University of Science and Technology Beijing | Sun Yat-sen University

3. DOWNSTREAM: Deployment at Scale

Top Hospitals Already Live:

Peking University Third Hospital
Peking University People's Hospital
Nanjing Medical University
Shenzhen Luohu People's Hospital

And more...

The Lesson:

China didn't build "one AI for healthcare."

They built specialized stacks:

· 23 companies for imaging
· 12 for clinical decisions
· 5 for Traditional Chinese Medicine

This isn't a market. It's a parallel healthcare AI universe.

Which company on this map surprised you most?

Philippe GERWILL | Edmund White | Harvey Castro, MD, MBA. | Rizwan Tufail | Asim Khan

#AIHealthcare #ChinaHealthTech #MedicalAI #HealthTech #MarketMap

—

Enjoy this? ♻️ Repost it to your network and follow Effie GUO for more. | 34 comments on LinkedIn

81% of physicians are routinely using AI in their clinical practice, according to a survey by the American Medical Association.

Unregulated use of this new powerful, but not infallible, technology in medicine is a reality now.

It is up to healthcare professionals to ensure responsible adoption. That is why AI competency needs to be part of any healthcare training program.

Link in the comments.

Geisel School of Medicine at Dartmouth IAMSE Dartmouth College AMEE - The International Association for Health Professions Education

🛡️ Veille informationnelle : Ce qu'il fallait retenir du Visibrain Meetup 2026
Rendez-vous incontournable des professionnels de la veille, le Visibrain Meetup a tenu toutes ses promesses. Entre retours d’expérience sur scène et échanges informels, la discipline confirme sa mutation profonde. Merci Nicolas Huguenin et ses équipes pour l'invitation.

Voici les 11 points clés que j'ai retenus pour naviguer dans l'infosphère cette année :
1️⃣ L'info comme champ de bataille : L’information est devenue un véritable terrain de confrontation géopolitique et économique.
2️⃣ Nouvelles cartographies : Maîtriser les réseaux LinkedIn et TikTok n'est plus une option, c'est une nécessité stratégique.
3️⃣ L'enjeu de la souveraineté : À qui confions-nous nos données ? La question de la nationalité des outils de veille devient centrale.
4️⃣ L'outil ne fait pas le moine : Un logiciel de veille n'a de valeur que s'il est servi par des humains formés et sensibilisés.
5️⃣ Omniprésence obligatoire : En 2026, la veille doit couvrir l'intégralité des plateformes, sans exception.
6️⃣ L'anticipation est la clé : Alerting, préparation de smartboards... la veille se prépare en amont pour être efficace le jour J.
7️⃣ Le paradoxe X (Twitter, Inc.) : Beaucoup de marques quittent le réseau en tant qu'annonceurs, mais aucune n'arrête d'y veiller. C'est là que cristallisent les crises, surtout en communication financière.
8️⃣ Les bastions de X : Le réseau reste le cœur battant du Gaming, de la Finance et de l'actu chaude.
9️⃣ Le soufflet BlueSky : Malgré le buzz, l'engagement y est resté très faible.
🔟 LinkedIn, outil de com' interne : Une annonce de CEO sur LinkedIn a souvent plus d'impact sur les collaborateurs qu'un mail interne.
1️⃣ 1️⃣ La fin de la "Safe Place" : LinkedIn se crispe et les polémiques y sont de plus en plus fréquentes.

🧐 Ma réflexion : L'angle mort de 2026 ?
Un point m'a frappé : presque personne n'a abordé les plateformes de messaging. Pourtant, Visibrain permet depuis quelque temps déjà de surveiller les chaînes Telegram.
Le Dark Social (WhatsApp, Signal, Telegram Messenger) ne serait-il pas le véritable angle mort de la veille cette année ? Comment anticiper une crise qui se propage dans des boucles fermées ?

The "Human + AI" Partnership (Best for Clinicians & Leaders)
The Thought: AI is not a replacement; it’s a "force multiplier" for empathy.
The Angle: Argue that by automating administrative burdens (like clinical documentation which has seen a 70% reduction in workload this year), AI actually returns the doctor to the patient’s bedside.
Key Phrase: "The most important surgical tool in 2026 isn't a robot—it's the data that gives the surgeon clarity before the first incision."
2. From Reactive to Proactive (Best for Tech & Innovation)
The Thought: We are finally moving from "Sick-care" to "Health-care."
The Angle: Highlight how predictive analytics are now moving into system design. Instead of treating a heart attack, we are using AI-driven wearables and EHR data to prevent one six months in advance.
Key Phrase: "In 2026, success in healthcare is measured by the hospital beds that stay empty, thanks to predictive intervention."
3. The Trust & Ethics Pivot (Best for Policy & Ethics)
The Thought: Interoperability and "Model Cards" are the new gold standard.
The Angle: Address the "Diagnostic Dilemma." Mention that as AI becomes embedded in mission-critical workflows, transparency (knowing why an AI made a suggestion) is more valuable than the algorithm itself.
Key Phrase: "Algorithm transparency is the new bedside manner. If we can't explain the 'why' behind the AI, we haven't earned the patient's trust."
Suggested Post Structure:
The Hook: "AI in healthcare has officially moved from the 'pilot' phase to the 'implementation' era."
The Insight: Use one of the angles above. (e.g., "85% of healthcare leaders now prioritize data sharing over shiny new tools.")
The Question: "Do you think the biggest barrier to AI adoption today is the technology itself, or the cultural shift required in our clinics?"

Most researchers waste hours reading papers that never make it into their thesis.

Here is the exact 6-step method I use to read any research paper in 20 minutes without missing anything important.

Step 1 → Read the abstract only (2 mins)
Decide if the paper is even relevant before you commit. 80% of papers get eliminated here.

Step 2 → Read introduction and conclusion (3 mins)
You now understand the full argument without reading the middle.

Step 3 → Scan headings and figures (3 mins)
Figures hold the key findings. Read every caption before anything else.

Step 4 → Read results and discussion (5 mins)
This is the heart of the paper. Highlight anything you might cite.

Step 5 → Check the methodology (4 mins)
Weak methodology means weak findings. Always verify before citing heavily.

Step 6 → Write your 3 sentence summary (3 mins)
What they studied. What they found. How it connects to your research.
This becomes your literature review foundation.

Save this post. You will need it.

Which step are you skipping right now?

♻ Repost this to help a fellow researcher in your network.

Follow Piumsha Mayanthi for more smart research writing tips.

#PhDLife #ResearchSkills #AcademicWriting #LiteratureReview #ThesisTips #MSc #PhD #ResearchTips #AcademicLife #GradSchool #ResearchPaper #StudySmart #ScientificReading #DoctoralStudent #AcademicSuccess