@PHARMAMEDIX

Medicine 2.0 - eHealth

Joined October 2011
Pharmamedix Scientific Society is the first Italian #medicalinformation project to launch its own official #token to celebrate its fellows. Willing to join? Visit phmxsociety.com
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#GenAI: from sycophantic systems to emotionally intelligent ones for a healthier AI-human relationship. Focus on @npjDigitalMed shorturl.at/EgUT1 #digitalhealth
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PHARMAMEDIX retweeted
Real-world AI goes to the urgent care setting. A prospective feasibility assessment of patient-facing conversational Al held up very well according to patients and physicians for accuracy of diagnosis, among many other metrics Just published @TheLancet @AdamRodmanMD @PeterBrodeurMD @alan_karthi @GoogleDeepMind thelancet.com/journals/lance…
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Congratulations to Georg Nagel, PhD, from all of us in New Haven! We are proud of your remarkable accomplishment and extraordinary journey since your postdoctoral studies at YSM. Well deserved! #NobelPrize #Physiology #Medicine
“I didn't expect this, really... I thought it's still too early to get this prize in medicine.” Georg Nagel was in Italy when he received the news of his 2026 Nobel Prize in Physiology or Medicine. He was in Italy on a sun-drenched terrace, overlooking olive trees when he got the call. Listen as he recalls the key research moment leading to the development of optogenetics.
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UK. The government says yes all 44 NHS #AI commission recommendations shorturl.at/pB0Ws #digitalhealth #healthcare
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NEW: AI just became authorized to issue prescriptions in the US. Nolla Health says its AI can assess patients, recommend treatment, and issue initial prescriptions, starting with acne care in Utah, with physician oversight available.
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Karl Deisseroth, MD, PhD, a Stanford University professor of bioengineering and of psychiatry and behavioral sciences, has been awarded the 2026 Nobel Prize in physiology or medicine “for discoveries leading to optogenetics, which makes it possible to switch on, or off, the activity of individual nerve cells in a living brain.” stanford.io/4xY9K0D @StanfordEng @Stanford @bioe_stanford @NobelPrize
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PHARMAMEDIX retweeted
New Lancet Series: the way digital technologies are built & governed creates real health risks worldwide. A review of 9 million under-25s across 121 studies links digital media use to mental health & sleep problems plus reduced physical activity. Authors call for urgent, coordinated action. ➡️ spkl.io/60157VKUv
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PHARMAMEDIX retweeted
A randomized trial of a patient-facing chatbot vs web based search enhanced accuracy of diagnosis of lung conditions This is one of the first RCTs for public chatbot assessment in healthcare nature.com/articles/s44360-0…
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PHARMAMEDIX retweeted
Editorial @Nature this week calls out lack of real world data for AI medical algorithms nature.com/articles/d41586-0…
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This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. Who’s liable when AI agents go rogue? Over the past few months, a cascade of cyberattacks by AI agents has stunned the world. In July, OpenAI disclosed that a swarm of its agents… trib.al/vedHjSb
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John Tsang, PhD, and his Yale lab members Jacob Kim and Ao Huang recently posed the question of whether #AI immunologists are ready for prime time. Specifically, they were referring to large language models (#LLMs) and whether they are as creative as humans in developing new hypotheses and methods for research in the field of immunology. The answer, based on current studies, is no, not yet, says Tsang, Anthony N. Brady Professor of #Immunobiology and professor of biomedical engineering at Yale School of Medicine. While there are functions where AI can be effective, such as summarizing relevant literature, LLMs like ChatGPT aren’t able to consistently generate truly original hypotheses, scientific ideas, or experimental approaches. Read more here: medicine.yale.edu/news-artic…
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PHARMAMEDIX retweeted
It’s incredibly hard to see through the current AI hype in healthcare. Myriads of potential uses cases, a lot of companies with commercial interest and changing regulations dominate the field. To see what is really going on and what we can realistically expect from AI to deliver in practice, I mapped the rapidly expanding universe of AI use cases in healthcare from early-stage “on the horizon” innovations to “safe bets” that are already backed by strong evidence. I analyzed them on two scales: little evidence to evidence-based (meaning there are studies and peer-reviewed papers proving their efficiency and safety); and low risk to high risk (meaning patients’ lives might be at stake in case of an error). This yielded four groups: 1) Speculative and risky (little evidence, high risk) 2) On the horizon (little evidence, low risk) 3) Handle with care (evidence-based, high risk) 4) Safe bet (evidence-based, low risk)
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