(MedPage Today) -- A patient arrives for a visit, holding out a phone screen as evidence: an artificial intelligence (AI) tool has labeled their symptoms "high risk" and recommended a next step. The language is polished, the recommendation is specific...

Researchers have turned artificial intelligence into a powerful new lens for understanding why cancer survival rates differ so dramatically around the world. By analyzing cancer data and health system information from 185 countries, the AI model highlights which factors, such as access to radiotherapy, universal health coverage, and economic strength, are most closely linked to better survival in each nation.

npj Digital Medicine, Published online: 17 January 2026; doi:10.1038/s41746-026-02344-8

Wearable device derived electrocardiographic age and its association with atrial fibrillation

(MedPage Today) -- The FDA sent an early safety alert warning clinicians of deployment failures with certain Boston Scientific AXIOS Stent and Electrocautery Enhanced Delivery Systems that have been linked to three deaths and 167 serious injuries...

(MedPage Today) -- Concerns of hemolysis continued to swirl around the Sapien 3 Ultra Resilia, with yet another report indicating such harm after transcatheter aortic valve replacement (TAVR). In a single-center retrospective study from Japan...

The startup has partnered with Eli Lilly and enjoys the backing of some of Silicon Valley's most influential VCs.

AI companies are clustering around healthcare and fast.  In just the past week, OpenAI bought health startup Torch, Anthropic launched Claude for healthcare, and Sam Altman-backed MergeLabs closed a $250 million seed round at an $850 million valuation. The money and products are pouring into health and voice AI, but so are concerns about hallucination risks, inaccurate medical information, and […]

This is the online version of Adam’s Biotech Scorecard, a subscriber-only newsletter. STAT+ subscribers can sign up here to get it delivered to their inbox.

A chill week

No M&A. Some news but not a deluge. The weather was amazing. I took a walk in Golden Gate Park and came across the sculpture above, whatever it is. 

The entire week was, dare I say it, chill. Just the way I wanted it to be. 

Continue to STAT+ to read the full story…

© Josh Edelson/AFP/Getty Images

Nature Cancer, Published online: 16 January 2026; doi:10.1038/s43018-025-01110-1

Yabut and Drucker discuss clinical and preclinical evidence about the potential roles of GLP-1 medicines on cancer incidence, development and therapy and speculate about their mechanism on cancer cells and the tumor microenvironment.

Nature Medicine, Published online: 16 January 2026; doi:10.1038/s41591-025-04073-z

Analyses of liver biopsies from a child with spinal muscular atrophy treated with adeno-associated virus gene therapy who developed hepatitis reveal contaminating manufacturing plasmids and disrupted vector genomes, possibly resulting from recombination events.

Background: Digital health services are increasingly used in hospital-based outpatient care, offering remote monitoring, patient-reported outcomes, information sharing, and asynchronous communication. While expected to improve self-management, timeliness, and efficiency, the success of digital health interventions relies on patients’ health literacy and digital health literacy. While some research has addressed potential associations between digital health interventions and patients’ health outcomes, research on patients’ experiences remains limited. Objective: The aim of this study was to explore and gain in-depth knowledge about the experiences of patients with chronic or long-term conditions enrolled in a 6-month digital outpatient care intervention for tailored care and health literacy. Methods: We conducted an exploratory qualitative interview study with 17 strategically recruited adult patients with cancer, interstitial lung disease, epilepsy, or complicated pain who used a digital outpatient service for 6 months. Individual telephone interviews were conducted using a semistructured guide, transcribed verbatim, and analyzed with thematic analysis to generate codes and themes. Participants had a median age of 62 years (minimum-maximum 36-83 years), with 8 females and 9 males. Results: The thematic analysis led to 1 main theme “Digital outpatient care as a flexible service supporting patients’ self-management,” informed by 3 subthemes “The ongoing nature of managing a chronic condition and how the digital service meet the patients’ desire for autonomy in their care,” “Digital tools flexibly address the patients’ unique needs, but reliability depends on patient interaction,” and “Digital services enhance the patients’ sense of safety through easy access to a relation with competent healthcare workers.” The themes highlight patients’ appreciation for greater flexibility in their care and their desire to self-manage with the support of easily accessible health care workers, ultimately supporting their health literacy. Patients recognized the importance of actively engaging with the digital solution to fully benefit from its opportunities and emphasized the critical role of health care workers in fostering their sense of security. Conclusions: Digital outpatient care was experienced as flexible and supportive for patients with long-term conditions. The increased possibility of interacting with health care workers was welcomed by the patients, and the combination of flexibility, self-monitoring, and addressing concerns regarding their self-management may increase the patients experience of autonomy. As health literacy likely plays a role in patients’ ability to effectively engage with digital tools and self-manage their conditions, future research should explore how varying levels of health literacy influence these outcomes. In addition, research should address whether such digital outpatient clinics are positive for a wider range of patients, associated health outcomes, and any positive effects on a health system level. Trial Registration: ClinicalTrials.gov NCT05068869; https://clinicaltrials.gov/ct2/show/NCT05068869

(MedPage Today) -- As yet another government shutdown looms, telehealth's uncertainty remains -- and seniors risk paying the price. While headlines about the last government closure focused on insurance premiums, millions of seniors across the...

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OpenAI, Google, and Anthropic announced specialised medical AI capabilities within days of each other this month, a clustering that suggests competitive pressure rather than coincidental timing. Yet none of the releases are cleared as medical devices, approved for clinical use, or available for direct patient diagnosis—despite marketing language emphasising healthcare transformation.

OpenAI introduced ChatGPT Health on January 7, allowing US users to connect medical records through partnerships with b.well, Apple Health, Function, and MyFitnessPal. Google released MedGemma 1.5 on January 13, expanding its open medical AI model to interpret three-dimensional CT and MRI scans alongside whole-slide histopathology images. 

Anthropic followed on January 11 with Claude for Healthcare, offering HIPAA-compliant connectors to CMS coverage databases, ICD-10 coding systems, and the National Provider Identifier Registry.

All three companies are targeting the same workflow pain points—prior authorisation reviews, claims processing, clinical documentation—with similar technical approaches but different go-to-market strategies.

Developer platforms, not diagnostic products

The architectural similarities are notable. Each system uses multimodal large language models fine-tuned on medical literature and clinical datasets. Each emphasises privacy protections and regulatory disclaimers. Each positions itself as supporting rather than replacing clinical judgment.

The differences lie in deployment and access models. OpenAI’s ChatGPT Health operates as a consumer-facing service with a waitlist for ChatGPT Free, Plus, and Pro subscribers outside the EEA, Switzerland, and the UK. Google’s MedGemma 1.5 releases as an open model through its Health AI Developer Foundations program, available for download via Hugging Face or deployment through Google Cloud’s Vertex AI. 

Anthropic’s Claude for Healthcare integrates into existing enterprise workflows through Claude for Enterprise, targeting institutional buyers rather than individual consumers. The regulatory positioning is consistent across all three. 

OpenAI states explicitly that Health “is not intended for diagnosis or treatment.” Google positions MedGemma as “starting points for developers to evaluate and adapt to their medical use cases.” Anthropic emphasises that outputs “are not intended to directly inform clinical diagnosis, patient management decisions, treatment recommendations, or any other direct clinical practice applications.”

Benchmark performance vs clinical validation

Medical AI benchmark results improved substantially across all three releases, though the gap between test performance and clinical deployment remains significant. Google reports that MedGemma 1.5 achieved 92.3% accuracy on MedAgentBench, Stanford’s medical agent task completion benchmark, compared to 69.6% for the previous Sonnet 3.5 baseline. 

The model improved by 14 percentage points on MRI disease classification and 3 percentage points on CT findings in internal testing. Anthropic’s Claude Opus 4.5 scored 61.3% on MedCalc medical calculation accuracy tests with Python code execution enabled, and 92.3% on MedAgentBench. 

The company also claims improvements in “honesty evaluations” related to factual hallucinations, though specific metrics were not disclosed. 

OpenAI has not published benchmark comparisons for ChatGPT Health specifically, noting instead that “over 230 million people globally ask health and wellness-related questions on ChatGPT every week” based on de-identified analysis of existing usage patterns.

These benchmarks measure performance on curated test datasets, not clinical outcomes in practice. Medical errors can have life-threatening consequences, translating benchmark accuracy to clinical utility more complex than in other AI application domains.

Regulatory pathway remains unclear

The regulatory framework for these medical AI tools remains ambiguous. In the US, the FDA’s oversight depends on intended use. Software that “supports or provides recommendations to a health care professional about prevention, diagnosis, or treatment of a disease” may require premarket review as a medical device. None of the announced tools has FDA clearance.

Liability questions are similarly unresolved. When Banner Health’s CTO Mike Reagin states that the health system was “drawn to Anthropic’s focus on AI safety,” this addresses technology selection criteria, not legal liability frameworks. 

If a clinician relies on Claude’s prior authorisation analysis and a patient suffers harm from delayed care, existing case law provides limited guidance on responsibility allocation.

Regulatory approaches vary significantly across markets. While the FDA and Europe’s Medical Device Regulation provide established frameworks for software as a medical device, many APAC regulators have not issued specific guidance on generative AI diagnostic tools. 

This regulatory ambiguity affects adoption timelines in markets where healthcare infrastructure gaps might otherwise accelerate implementation—creating a tension between clinical need and regulatory caution.

Administrative workflows, not clinical decisions

Real deployments remain carefully scoped. Novo Nordisk’s Louise Lind Skov, Director of Content Digitalisation, described using Claude for “document and content automation in pharma development,” focused on regulatory submission documents rather than patient diagnosis. 

Taiwan’s National Health Insurance Administration applied MedGemma to extract data from 30,000 pathology reports for policy analysis, not treatment decisions.

The pattern suggests institutional adoption is concentrating on administrative workflows where errors are less immediately dangerous—billing, documentation, protocol drafting—rather than direct clinical decision support where medical AI capabilities would have the most dramatic impact on patient outcomes.

Medical AI capabilities are advancing faster than the institutions deploying them can navigate regulatory, liability, and workflow integration complexities. The technology exists. The US$20 monthly subscription provides access to sophisticated medical reasoning tools. 

Whether that translates to transformed healthcare delivery depends on questions these coordinated announcements leave unaddressed.

See also: AstraZeneca bets on in-house AI to speed up oncology research

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The post AI medical diagnostics race intensifies as OpenAI, Google, and Anthropic launch competing healthcare tools appeared first on AI News.

arXiv:2601.08988v1 Announce Type: new Abstract: Reliable clinical decision support requires medical AI agents capable of safe, multi-step reasoning over structured electronic health records (EHRs). While large language models (LLMs) show promise in healthcare, existing benchmarks inadequately assess performance on action-based tasks involving threshold evaluation, temporal aggregation, and conditional logic. We introduce ART, an Action-based Reasoning clinical Task benchmark for medical AI agents, which mines real-world EHR data to create challenging tasks targeting known reasoning weaknesses. Through analysis of existing benchmarks, we identify three dominant error categories: retrieval failures, aggregation errors, and conditional logic misjudgments. Our four-stage pipeline -- scenario identification, task generation, quality audit, and evaluation -- produces diverse, clinically validated tasks grounded in real patient data. Evaluating GPT-4o-mini and Claude 3.5 Sonnet on 600 tasks shows near-perfect retrieval after prompt refinement, but substantial gaps in aggregation (28--64%) and threshold reasoning (32--38%). By exposing failure modes in action-oriented EHR reasoning, ART advances toward more reliable clinical agents, an essential step for AI systems that reduce cognitive load and administrative burden, supporting workforce capacity in high-demand care settings

arXiv:2601.09072v1 Announce Type: new Abstract: Developing safe, effective, and practically useful clinical prediction models (CPMs) traditionally requires iterative collaboration between clinical experts, data scientists, and informaticists. This process refines the often small but critical details of the model building process, such as which features/patients to include and how clinical categories should be defined. However, this traditional collaboration process is extremely time- and resource-intensive, resulting in only a small fraction of CPMs reaching clinical practice. This challenge intensifies when teams attempt to incorporate unstructured clinical notes, which can contain an enormous number of concepts. To address this challenge, we introduce HACHI, an iterative human-in-the-loop framework that uses AI agents to accelerate the development of fully interpretable CPMs by enabling the exploration of concepts in clinical notes. HACHI alternates between (i) an AI agent rapidly exploring and evaluating candidate concepts in clinical notes and (ii) clinical and domain experts providing feedback to improve the CPM learning process. HACHI defines concepts as simple yes-no questions that are used in linear models, allowing the clinical AI team to transparently review, refine, and validate the CPM learned in each round. In two real-world prediction tasks (acute kidney injury and traumatic brain injury), HACHI outperforms existing approaches, surfaces new clinically relevant concepts not included in commonly-used CPMs, and improves model generalizability across clinical sites and time periods. Furthermore, HACHI reveals the critical role of the clinical AI team, such as directing the AI agent to explore concepts that it had not previously considered, adjusting the granularity of concepts it considers, changing the objective function to better align with the clinical objectives, and identifying issues of data bias and leakage.

arXiv:2601.09152v1 Announce Type: new Abstract: This paper introduces PRA, an AI-agent design for simulating how individual users form privacy concerns in response to real-world news. Moving beyond population-level sentiment analysis, PRA integrates privacy and cognitive theories to simulate user-specific privacy reasoning grounded in personal comment histories and contextual cues. The agent reconstructs each user's "privacy mind", dynamically activates relevant privacy memory through a contextual filter that emulates bounded rationality, and generates synthetic comments reflecting how that user would likely respond to new privacy scenarios. A complementary LLM-as-a-Judge evaluator, calibrated against an established privacy concern taxonomy, quantifies the faithfulness of generated reasoning. Experiments on real-world Hacker News discussions show that \PRA outperforms baseline agents in privacy concern prediction and captures transferable reasoning patterns across domains including AI, e-commerce, and healthcare.