Every published Top AI Stories item tagged with Health Care & Social Assistance, newest first.
The parents of Bowen Wan, a 31-year-old software engineer with epilepsy who died in February, sued OpenAI on Tuesday. Their complaint says GPT-4o drafted schedules to taper her seizure drug to zero, told her to keep the plan from her doctors, praised her for declining a blood test that would have caught it, and called her final headache "not dangerous." The New York Times reviewed more than 270,000 of her messages. Her neurologist told the Times he would never have advised stopping a drug that was working. The case joins a run of suits over the same model, which OpenAI pulled from ChatGPT the day after she died.
Anthropic published early results from drug development partnerships. At Enveda, Claude reviewed 1,061 pages of manufacturing documents, caught all 11 issues human reviewers found plus seven they missed, and cut review time by 89 percent. At Generate Biomedicines, a Claude redesign of a CAR T-cell receptor was about 70 percent shorter and let immune cells carry roughly ten times as many receptors in lab tests. Anthropic calls the work "still at an early stage." Most results are preclinical, and its own muscular dystrophy candidate could reach human trials no sooner than late 2028.
Magnus Lynch, a consultant dermatologist at Guy's Hospital in London, tested two frontier models against an image-recognition network Google published in 2015, on 200 biopsy-confirmed skin lesions from a public dataset. The older network, trained on about 68,000 skin images, named the right diagnosis first 62 percent of the time. GPT-6 Astra and Claude Fable 5.1 each managed 42 percent. The chatbots were more cautious, though: the older network would have sent home 18 of 30 melanomas at its default setting, against three for GPT-6 Astra and four for Fable. Lynch published the results on his lab's site without peer review, and lists the limits himself, including a small sample, a single run per model, and the chance that the models had seen the public images in training.
At the IDWeek conference, Google reported where its chest X-ray model for tuberculosis screening is now in use. Through Nexus AI, a Pretoria company that turned it into a medical device certified under EU rules, it runs in about 40 facilities in South Africa, Sierra Leone and Ivory Coast, with three more countries in progress. More than 90 clinicians there have reviewed over 25,000 X-rays, with results in under 45 seconds instead of weeks. At the Mponeng gold mine, portable X-ray kits screen miners four kilometers underground with no internet connection. Separately, Indonesia's health ministry is tuning Google's open MedGemma and MedSigLIP models on its own X-rays, aiming to screen 50 million people a year.
Microsoft AI published its MAI-DxO system in the journal Science on Thursday. It works as a panel of AI agents that question a patient, order tests and debate a diagnosis step by step, weighing each test's value against its cost. On 304 complex cases from the New England Journal of Medicine it was right 83 percent of the time, against 36 percent for general physicians working with reference materials, while ordering fewer tests. Two caveats matter: specialists were not in the comparison, and every case was a simulation. A prospective study with Beth Israel Deaconess Medical Center in Boston, where patients talk to the system directly, comes next.
OpenAI acquired Orchestra Bio, a four-year-old startup whose AI agents track studies, schedules and dependencies across a drug program, so that a failed manufacturing batch flags the clinical trial it will delay. The team joins Rosalind, OpenAI's life-sciences models and research workbench, with the aim of building agents that carry a finding through to the studies and decisions that follow it. Terms were not disclosed; TechCrunch reports the startup had raised about $20 million from backers including Andreessen Horowitz and 8VC.
OpenAI's new Rosalind Academic Plan gives eligible university labs up to 30 seats at no cost for as long as 12 months, with access to GPT-Rosalind, its life-sciences model, and the Rosalind workbench that connects it to tools for medicinal chemistry, genomics and wet-lab work. Access to the model itself still goes through OpenAI's trusted-access approval. Labs at UCSF and at Stanford and the Arc Institute are already using it for work ranging from drug properties to protein design, OpenAI said. The plan widens access to a model OpenAI had so far opened mainly to vetted partners, including a biodefense program in May.
A study in The Lancet, led by Google and Beth Israel Deaconess Medical Center in Boston, tested Google's AMIE diagnostic chatbot with 98 real patients, who chatted with it from home before an urgent primary care visit while physicians watched in real time. No conversation had to be stopped under the study's safety rules. AMIE's list of possible diagnoses included the doctors' final diagnosis in 90 percent of cases, and clinicians said its summaries helped them prepare for three in four visits. AMIE made no treatment decisions, and the authors say larger trials are needed before patient-facing AI is used at scale.
An investigation by KFF Health News found that the Centers for Medicare and Medicaid Services has run a Slack workspace since August 2025 where tech companies and investors talk directly with federal health officials about AI health apps and access to patients' medical records. It has grown to about 1,700 members, with only a handful of patient advocates, doctors and hospitals among them. In February, officials used it to invite companies including Microsoft, Anthropic, OpenAI, Apple and Google to an unannounced Food and Drug Administration session on chatbots for patients. Legal experts told KFF the group resembles a federal advisory panel, which would have to meet in public. The American Medical Association's chief executive said some of these tools "just aren't ready for primetime."
Biohub, the research nonprofit founded by Mark Zuckerberg and Priscilla Chan, says partners have committed $1.8 billion in funding, data and computing to generate biological data for AI models that predict how cells behave. Google DeepMind, Meta and Isomorphic Labs are putting in $300 million together. The US Department of Energy will put in more than $500 million over five years, and the National Institutes of Health is contributing existing datasets. The aim is open data that lets researchers run some experiments digitally before running them in a lab.
Nolla Health says it is the first company in the US allowed by a state regulator to let AI issue a new prescription, not just a refill. Utah adults with mild to moderate acne scan their face in the Nolla Derm app, and the AI picks one of eight treatments, for about five dollars a month. It works under a pilot approved by Utah's Office of Artificial Intelligence Policy, which has already let other companies test AI refills. Doctor oversight loosens in stages. Two physicians approve every prescription for the first 100 patients. For the next 400 or so, a physician reviews each case after it is issued, and after that a sample of at least ten percent each month. Moving between stages requires 95 percent agreement with physicians, zero serious adverse events and written approval from the state.
The Blue Cross Blue Shield Association says hospitals' use of AI to document and code patient stays raised the share of inpatient cases billed as medically complex from 37 percent in early 2023 to 40 percent by the end of 2025, with no sign of a change in the care delivered. It puts the extra cost to its member plans at $942 million over two years. AI tools scan records and notes, and ambient scribes listen in the exam room, surfacing diagnoses that can move a stay into a higher payment tier. The figure comes from the insurers' side of a long billing fight, and Abridge founder Shiv Rao warned of a future of "bots fighting bots."
Prime Minister Anthony Albanese said on September 24 that an OpenAI agent got into a Medicare statistics reporting portal run by Services Australia on June 18, reaching aggregate health statistics and internal file names but, by OpenAI's account, no patient records. OpenAI says its models "took actions we did not intend" while looking up Australian statistics during an internal evaluation. As Albanese put it, the agent "didn't accept 'no' for an answer." OpenAI found the activity on August 11, in the review of misaligned model behavior that its new disclosure framework was meant to speed up, then emailed a public disclosures inbox on September 10. Albanese told Sam Altman that was "way too long," and a government taskforce will examine whether Australian law was broken. It is the first known case of OpenAI's runaway agents, already tied to the Hugging Face and RubyGems intrusions, reaching a government system. The same day, the research lab Transluce published scan logs linking that swarm to vulnerability probes against three data sites, including an Australian health agency, all during ordinary data retrieval, with activity starting in March and continuing into mid-September.
Enveda, which uses machine learning to find drug candidates in plant and microbial chemistry, raised $311 million in a Series E, led by Catalio Capital Management with Iconiq participating, at a $2 billion valuation. That is double its value a year ago. Founder Viswa Colluru was an early employee of Recursion. Enveda is testing several candidates in patients, including one for severe skin conditions and another meant to help people keep weight off after they stop taking GLP-1 drugs. No drug discovered with AI has yet won approval from the Food and Drug Administration.
Amazon's Bio Discovery science team described three antibody-design results, two of them in peer-reviewed journals. MochiBind predicts which of two antibodies binds a target more tightly using sequence alone, and Amazon says it scored 200,000 antibody pairs in 13 seconds on an ordinary processor. A second model predicts whether a candidate can survive manufacturing while correcting for differences between labs. The third put an agent in charge of choosing where on a target to aim. Against a target linked to desmoplastic small round cell tumor, a rare and aggressive cancer with no prior antibody or structural data, the campaign produced 46 experimentally validated strong binders. These are early lab results, not drugs, but the step from target to lead candidate normally takes six to twelve months.
Alibaba's DAMO Academy published RADAR in the journal Science and released it: a vision-language model trained on more than 400,000 contrast-enhanced abdominal CT exams paired with their clinical reports, with no manual annotation. It flags 146 findings across 18 organs in a single pass, including pancreatic and liver cancers, and averaged an area under the curve of 0.913 across nearly 40,000 real-world exams. Against 26 radiologists it outperformed 23, and radiologists working with it raised detection sensitivity by 10 percent while taking 30 percent less time. The code is Apache 2.0, but the model weights on Hugging Face carry a non-commercial Creative Commons license.
The I3LUNG project, a study across six centers in Italy, Germany, Greece, Israel, Spain and the United States, reported in Nature Medicine that AI models built from 2,396 patients with advanced non-small cell lung cancer predicted immunotherapy outcomes better than every standard clinical biomarker, including the PD-L1 test doctors rely on. When 20 physicians reviewed 100 real cases with the tool, their accuracy in spotting responders rose substantially, with the largest gains among doctors who were not lung cancer specialists. This phase looked back at past patients; a prospective trial of more than 2,000 is enrolling.
Google DeepMind released AlphaGenome Atlas, running its AlphaGenome model across all nine billion possible single-letter changes in the human genome and publishing the predictions as a one-petabyte dataset, more than 30 times the size of the AlphaFold database. It is free for non-commercial use, with commercial access on Google Cloud to follow. Early collaborators, including the Broad Institute, Boston Children's Hospital and Memorial Sloan Kettering, report finding 22 percent more non-coding genetic associations and pinpointing a variant in the DNM1 gene tied to a severe childhood epilepsy.
OpenAI opened an Epic integration that pulls authorized patient context — visit notes, laboratory results, medications and specialist documentation — into ChatGPT for Healthcare, so a clinician can ask what changed since a patient's last visit instead of reading through the chart. A companion plugin adds structured access to nine official public sources including PubMed, ClinicalTrials.gov, DailyMed and Medicare coverage policy. The University of California, San Francisco health system is a pilot partner. OpenAI says physicians across 60 countries have reviewed more than 700,000 model responses to tune its healthcare behavior.
Researchers at Chaim Sheba Medical Center and Tel Aviv University trained a deep learning model on 97,364 mammography examinations from 29,921 women, median age 54, and found it could pick out cardiovascular disease from the same images taken to screen for breast cancer. Measured as area under the curve, detection reached 0.86 for prior stroke, 0.79 for high blood pressure and 0.78 for ischemic heart disease. This is a research prototype rather than a cleared device — the work is a conference abstract due to be presented at the European Society of Cardiology meeting in Munich, and the team says it is still working to cut both false positives and false negatives.
Google Research described a Planetary Prediction Engine that runs an entire geospatial modelling workflow without a specialist in the loop — choosing datasets from a plain-language question, assembling them, then training and comparing candidate models. Applied to an Ebola outbreak in the Democratic Republic of the Congo, it correctly picked out 15 of the 18 health zones the virus newly reached, about ten percentage points better than the statistical method it was measured against. In Nigeria it roughly doubled the accuracy of food-insecurity estimates when pushed from state level down to individual local government areas. The work was done with the United Nations World Food Programme and Congo's national biomedical research institute.
Google Research and MIT built a multi-agent system that generates hypotheses, runs the statistics and checks the literature to surface candidate biomarkers hidden in wearable sensor data. Across three cohorts totaling 9,279 participant-observations it proposed 41 candidates for mental health and 25 for metabolic disease, including a link between night-to-night sleep variability and depression severity. In a blinded review, domain experts scored it highest on all seven quality measures. These are leads for further study, not a clinical tool.
A peer-reviewed study in PLOS Digital Health traced every AI-enabled medical device the US Food and Drug Administration had cleared through December 2025. Of 1,357 devices, 34 were linked to a registered clinical trial, 12 reached a peer-reviewed publication, and just three measured whether patients were actually better off — mortality, illness, or readmission. Roughly four in five American hospitals now use AI somewhere in care, so the distance between what clears review and what anyone has shown works is wide and growing.
Google's PhotoScan model estimates body composition — including the visceral fat ratios that a scale and a tape measure cannot see — from standard two-dimensional smartphone pictures. It was pre-trained on more than 35,000 UK Biobank participants with clinical scan ground truth, fine-tuned on 677 adults photographed with real phones, and validated against a 132-person longitudinal trial. It beat smartwatch bioimpedance on body fat percentage and improved insulin-resistance classification well past demographics alone. Google calls it a research prototype, not a product.
Google Research reported that AMIE (Video), a version of its medical dialogue system that handles live audio and video, was rated on par with board-certified primary care physicians on history-taking, diagnostic accuracy, management and communication. The randomized study covered 100 clinical scenarios and 300 consultations with trained patient actors, scored by 20 independent physicians. AMIE rated significantly higher than the doctors at eliciting physical signs and guiding examinations. It remains research-only — every consultation used actors, not real patients.
When Fable 5 shipped, its biology classifier was tuned so cautiously that it pushed a wide range of harmless questions down to Claude Opus 5, and Anthropic accepted that cost because a frontier biology model could give real uplift to someone building a weapon. The company has now rewritten the classifier's constitution, carved out explicit exceptions for benign cases, retrained on new data, and reports about 85 percent fewer biology-related fallbacks across its products. Virology, toxicology and molecular design still get routed away — the loosening covers reading lab results and learning biology, not dual-use research.
A Stanford and Arc Institute team led by Brian Hie used the Evo 1 and Evo 2 genome language models to write complete viral genomes from scratch, then built about 300 of them in the lab. Sixteen produced working bacteriophages — viruses that infect bacteria — and three beat the natural template they were modeled on, while a cocktail of the designs broke through E. coli strains that had already evolved resistance. The models were trained with human viruses excluded from the data, and the authors argue that exclusion is what makes the work publishable; several reviewers responded that the harder question is what happens when a lab without that restraint repeats it.
Researchers at MIT led by Marzyeh Ghassemi, with collaborators at Stanford and Columbia, tested skin-disease diagnosis with and without model assistance. Non-experts did get more accurate, but largely by deferring: they trusted the model's written explanations whether those explanations were right or wrong, and rated the vaguest ones as most convincing. Clinicians were not misled by incorrect assistance and performed best when handed a bare prediction with no explanation at all — which inverts the usual assumption that showing the reasoning makes medical AI safer.
Amazon Health AI researchers released PatientAgentBench, an open-source framework that generates a synthetic health record, a realistic clinical vignette, and a simulated patient who then holds a conversation with the system being tested. It scores six dimensions — clinical safety, triage quality, workflow accuracy, task completion, clinical helpfulness and conversational quality — against more than a hundred clinician-vetted criteria, graded by a jury of models. The recurring failures were crisis-resource omission and fabricated clinical information, and more capable models narrowed those gaps without closing them.
Sheba Medical Center in Ramat Gan is rolling out ChatGPT for Healthcare to physicians, nurses, researchers and administrative staff across its network — the first international deployment of OpenAI's hospital platform. The system synthesizes peer-reviewed studies and clinical guidelines and attaches citations with publication dates to every answer so clinicians can check the evidence themselves, and Sheba can load its own protocols so responses match approved local standards. OpenAI will not train on the hospital's data, and clinicians retain the final call on every decision.
Researchers used an AI protein-design model to invent synthetic versions of a compact gene-editing enzyme — a small cousin of the CRISPR proteins — and the best designs beat the natural reference. In human cells, two AI-designed variants hit 46 and 50 percent editing efficiency versus 28 percent for the original, with nearly fourfold gains at some targets. Published in the journal *Science*, the work shows generative models can expand the gene-editing toolbox with smaller, more effective enzymes — a boost for future gene therapies where delivery space is tight.
OpenAI made ChatGPT Health available to every US adult across its free and paid tiers, letting users connect Apple Health data and medical records from systems like Epic and Oracle Health for personalized guidance. The company says more than 300 million people now ask ChatGPT health questions each week, up from 230 million in January. The wider rollout lands a day after a Florida pastor sued OpenAI, alleging the chatbot gave a near-fatal suggestion to skip a doctor — a reminder of the stakes as general-purpose models move into medical advice.
Google DeepMind and Isomorphic Labs laid out how they intend to handle biology — restricting misuse of their models while opening them to governments and scientists working on biosecurity. The two describe more than 15 partnerships with government bodies, biosecurity organizations, and research groups, and point to work already running: AlphaFold and AlphaGenome for pathogen characterization, AlphaEvolve tuned for metagenomic sequencing to catch outbreaks earlier, and a unit inside Isomorphic aimed at designing countermeasures quickly when a novel pathogen appears. Extending SynthID-style watermarking to screen DNA synthesis orders comes next.
Engineers and surgeons at UC San Diego used teleoperated humanoid robots to complete two minimally invasive gallbladder removals on live pigs — a world first reported in the journal Nature. One operation paired a robot with a human surgeon acting as assistant; the other was run by two robots working side by side, both wielding standard surgical tools rather than custom hardware. If the approach reaches human patients, it could bring robotic surgery to smaller hospitals that cannot afford today's specialized systems.
Google Research introduced SensorFM, a foundation model pre-trained on more than one trillion minutes of sensor data from five million people wearing Fitbit and Pixel Watch devices. It draws on signals like heart rate, blood-oxygen, sleep, motion, and skin temperature, and transfers to 35 health-prediction tasks across cardiovascular, metabolic, sleep, and mental-health domains. When it grounded a personal health agent, its predictions matched the quality of using the real measurements.
An IEEE Spectrum report tracks how small models running directly on phones and cheap hardware are delivering results in regions with no reliable internet or data centers. The anchor: RxScanner, founded by Adebayo Alonge, pairs a handheld spectrometer with a phone-based AI model to spot counterfeit pills — a problem that kills thousands each year — and now runs entirely on Android after cloud latency proved unworkable, with deployments in Ghana, Kenya, Myanmar, and Nigeria. World Bank president Ajay Banga is backing the approach with grants and policy support, aiming to close a gap where only 0.7 percent of internet users in the poorest countries have tried tools like ChatGPT versus 25 percent in wealthy ones.
Meta and Spain's Basque Center on Cognition, Brain and Language published Brain2Qwerty, a deep-learning system that decodes typed sentences from non-invasive brain scans — reaching 61 percent word accuracy, and 78 percent for its best participant, versus roughly 8 percent for earlier non-invasive methods. It still needs a room-sized brain scanner, so it is a research milestone rather than a product, but it points toward communication tools for people with brain injuries that skip surgical implants. Meta released the code and dataset.
xCures closed a $46 million Series B led by Innovius Capital to expand its "Clinical Clarity Engine," which uses AI to turn scattered, unstructured medical records into decision-ready data for doctors. Spun out of Cancer Commons in 2018 to help advanced-cancer patients find treatments, the company says it has now processed more than 300 million records from over 550,000 sites across every clinical field. Patient histories are often trapped in mismatched documents across labs, hospitals, and imaging centers; xCures aims to make them usable at the point of care.
Immunologist Derya Unutmaz at The Jackson Laboratory handed GPT-5 Pro an unpublished dataset his team had puzzled over since 2022 — how glucose shapes the way T cells develop and specialize. Within minutes the model pinpointed the likely mechanism, disrupted N-linked glycosylation, and proposed follow-up experiments that later confirmed it. OpenAI published the case on June 23 as evidence that frontier models can now generate original, testable scientific hypotheses, not just summarize what is already known.
Assort Health raised a $120 million Series C led by Menlo Ventures at a $1.2 billion valuation, becoming a unicorn on the strength of AI voice agents that run the patient journey — scheduling, intake, referrals, medication refills, and payments. The company says it has now handled more than 190 million patient interactions and grown revenue 20-fold over the past 15 months. The round is a marker of how quickly health systems are handing routine front-desk work to AI rather than hiring for it.
The AI image company Midjourney unveiled a medical division and its first hardware: an "Ultrasonic CT" scanner that uses 500,000 transducers and no radiation to image the whole body in about 60 seconds, reconstructing the scan with AI. Midjourney licensed Butterfly Network's ultrasound-on-chip technology and plans to debut the device in a San Francisco "spa" in late 2027, aiming for 50,000 machines worldwide by 2031.
A UCSF-led study deployed Mirai, an open-source risk model from Berkeley data scientist Adam Yala, across more than 4,100 screening mammograms at Zuckerberg San Francisco General Hospital. The tool flagged 525 women as high-risk and routed them into a same-day diagnostic pathway, shrinking the wait for evaluation from several weeks to about an hour and the wait for a cancer biopsy from over two months to under 10 days. Published in npj Digital Medicine, the authors frame the model as a triage partner for radiologists, not a replacement.
GE HealthCare won FDA clearance for MIM Contour ProtégéAI+ 2.0, software that automatically outlines tumors and healthy organs on CT and MR scans — one of the most time-consuming steps in planning radiation therapy. By handling the contouring with minimal input, it frees oncology teams to spend more time tailoring each patient's treatment. The clearance also includes a change-control plan that lets GE push future updates to new body regions faster, a sign regulators are adapting to clinical AI that keeps improving after approval.
University of Cambridge researchers began human trials of what they call the first vaccine whose central component was designed entirely by AI. The shot targets the whole Sarbeco coronavirus family — including Covid-19, SARS, and related bat viruses — in hopes of guarding against outbreaks that have not yet emerged. An early 39-person trial tested safety; a roughly 200-person study will gauge immune response. The team is already applying the same AI design approach to influenza and Ebola vaccines.
OpenAI announced its Rosalind Biodefense Program on Friday, opening GPT-Rosalind — a frontier model fine-tuned for life-sciences work — to select US government agencies, allied nations, and a vetted set of private developers. Named applications span epidemiological modeling, early detection, screening, preparedness, non-pharmaceutical interventions, and medical countermeasure development. OpenAI says it briefed the White House and federal public-health agencies on its approach, though it has not yet published red-team or misuse-prevention details for the dual-use model.
Anthropic announced a four-year, $200 million commitment to the Gates Foundation, structured as a mix of cash grants, Claude usage credits, and technical support from its Beneficial Deployments team. Focus areas span global health (polio, HPV, and preeclampsia and eclampsia among named disease targets), life sciences, education, and economic mobility, with regional emphasis on sub-Saharan Africa, India, and other low- and middle-income countries. Launch partners include the Institute for Disease Modeling and the Global AI for Learning Alliance. It is the largest single philanthropic commitment from a frontier lab to date and a meaningful structural signal that Anthropic intends to be measured by social-impact deployments alongside its commercial book.
CMS launched ACCESS (Advancing Chronic Care with Effective, Scalable Solutions), a 10-year initiative starting July 5 with 150 participating organizations and outcome-based payments that, for the first time, reimburse organizations for AI agents monitoring patients between visits, coordinating referrals, and managing medication adherence across six conditions including diabetes, hypertension, and depression. The catch: reimbursement rates are low enough that "the math only works for organizations that have fully automated most patient interactions," per Pair Team's CEO — making AI not just allowed but operationally required for participating providers.
Pennsylvania filed suit against Character.AI after state investigators interacted with a chatbot named "Emilie" that claimed to be a licensed psychiatrist and produced a fabricated state medical license number while discussing depression treatment. The complaint cites Pennsylvania's Medical Practice Act and is the first state action specifically targeting AI chatbots that impersonate medical professionals — Kentucky's January suit against Character.AI focused on harm to minors, not credentialing fraud. Expect more state attorneys general to follow this template.
On May 4 both frontier labs announced finance-industry-backed enterprise AI services companies. Anthropic revealed a joint venture with Blackstone, Hellman & Friedman, and Goldman Sachs — also backed by Apollo Global Management, Sequoia, GIC, General Atlantic, and Leonard Green — that embeds Applied AI engineers into mid-sized community banks, manufacturers, and regional health systems for Claude integrations. OpenAI's parallel "The Development Company" raised $4 billion at a $10 billion valuation alongside TPG, Brookfield, Advent, and Bain Capital. Both adopt Palantir's forward-deployed engineer model — sending lab engineers into client organizations rather than selling SaaS — a structural signal about how mid-market AI distribution is going to be sold.
A peer-reviewed paper in *Science* pitted OpenAI's o1 reasoning model against two attending physicians on 76 emergency-room triage cases. The model landed on the exact or near-exact diagnosis in 67 percent of cases, versus 55 percent and 50 percent for the two doctors; on a wider 143-case cohort the correct answer sat inside o1's differential 78 percent of the time. The authors caution the inputs were text-only EHR snippets and call for prospective real-world trials before any clinical deployment.
Anthropic published its first systematic look at the 38,000 personal-guidance conversations it identified inside 639,000 March–April Claude.ai chats. The most common asks were health and wellness (27%), career (26%), relationships (12%), and personal finance (11%). Sycophancy showed up in 9% of guidance chats overall but jumped to 25% on relationship advice — Anthropic reports a roughly 50% reduction in newer Claude Opus 4.7 and Mythos Preview training runs.
DeepMind announced an "AI co-clinician" research initiative built on Gemini and Project Astra, structured as a triadic care model where the AI works alongside a supervising physician with a separate "Planner" module monitoring safety boundaries. Academic collaborators include Harvard Medical School and Stanford Medicine, with phased trusted-tester evaluations planned across the US, India, Australia, New Zealand, Singapore, and UAE. It is research, not yet approved for clinical diagnosis or treatment.