The Food and Drug Administration’s refusal to review Moderna’s flu vaccine this month has renewed fears that Trump administration policies could paralyze the vaccine industry, dissuading companies from developing new shots in the U.S. and leaving the country flat-footed in the event of future pandemics. 

“I consider it an unprecedented action that really violates the basic principles of a data-driven regulatory agency and the fundamentals of public health, and it’s that simple,” said Gary Nabel, former head of the National Institutes of Health’s Vaccine Research Center and chief scientist at Sanofi, who now runs a vaccine and cancer startup. “It’s a destructive precedent that will undermine the future of vaccine development and the preeminence of American research.”

Executives at large vaccine developers were already grappling with a litany of changes to vaccine policy. Under Robert F. Kennedy Jr., a longtime vaccine critic, the Department of Health and Human Services has unilaterally removed six shots from the childhood vaccination schedule, canceled hundreds of millions of dollars in grants for mRNA shots, and fired and replaced a key immunization advisory board. 

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Good morning health tech readers!

Today, we’ve got a ton of updates including news about venture capital funding, telehealth policy, the government’s progress on information blocking, and research into the accuracy of Apple’s new hypertension feature.

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Hello, everyone, and welcome to the middle of the week. Congratulations on making it this far. It is an accomplishment, after all. The next step is to… keep going. And why not? Just consider the alternatives. On that optimistic note, please join us for a needed cup or three of stimulation. Our choice today is coconut rum. Meanwhile, here are some items of interest to get you going. Have a wonderful day and do drop us a line when you hear something juicy …

The U.S. Food and Drug Administration refused to review Moderna’s application for a new influenza vaccine, a surprise decision that could  raise concerns about the agency’s posture toward drug companies and the Trump administration’s policies on vaccines, STAT writes. Moderna, revealing the rejection, took the unusual step of releasing the letter it had received from Vinay Prasad, who heads the FDA’s biologics division. They also issued a strongly worded statement from its chief executive officer Stephane Bancel, who said the decision “does not further our shared goal of enhancing America’s leadership in developing innovative medicines.” At the heart of the dispute is what existing influenza vaccine Moderna should have used as a control when testing the efficacy of its new shot, which utilizes the same mRNA technology the company used in its Covid-19 vaccine.

The recent moves by the Trump administration against Hims & Hers might only be the start of a crackdown on compounding, STAT explains. In recent days, the Food and Drug Administration issued a warning, the Department of Health & Human Services asked the Department of Justice to open an investigation and, meanwhile, Novo Nordisk filed a patent infringement lawsuit against the company. But while compounded weight-loss drugs proliferated during recent shortages and continued to remain available, the flurry of developments underscores growing unease among regulators with mass-marketed compounded drugs sold by national, vertically integrated telehealth platforms. The FDA has so far focused publicly on misleading marketing, but signs that it may scrutinize compounding practices themselves have the industry on edge, given how many telehealth companies rely on compounded versions of everything from acne treatments to libido drugs.

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npj Digital Medicine, Published online: 11 February 2026; doi:10.1038/s41746-026-02418-7

Advancing healthcare AI governance through a comprehensive maturity model based on systematic review

arXiv:2602.06609v1 Announce Type: cross Abstract: Background: High-level system testing of applications that use data from e-Government services as input requires test data that is real-life-like but where the privacy of personal information is guaranteed. Applications with such strong requirement include information exchange between countries, medicine, banking, etc. This review aims to synthesize the current state-of-the-practice in this domain. Objectives: The objective of this Systematic Review is to identify existing approaches for creating and evolving synthetic test data without using real-life raw data. Methods: We followed well-known methodologies for conducting systematic literature reviews, including the ones from Kitchenham as well as guidelines for analysing the limitations of our review and its threats to validity. Results: A variety of methods and tools exist for creating privacy-preserving test data. Our search found 1,013 publications in IEEE Xplore, ACM Digital Library, and SCOPUS. We extracted data from 75 of those publications and identified 37 approaches that answer our research question partly. A common prerequisite for using these methods and tools is direct access to real-life data for data anonymization or synthetic test data generation. Nine existing synthetic test data generation approaches were identified that were closest to answering our research question. Nevertheless, further work would be needed to add the ability to evolve synthetic test data to the existing approaches. Conclusions: None of the publications really covered our requirements completely, only partially. Synthetic test data evolution is a field that has not received much attention from researchers but needs to be explored in Digital Government Solutions, especially since new legal regulations are being placed in force in many countries.

arXiv:2602.05183v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly trained in complex Reinforcement Learning, multi-agent environments, making it difficult to understand how behavior changes over training. Sparse Autoencoders (SAEs) have recently shown to be useful for data-centric interpretability. In this work, we analyze large-scale reinforcement learning training runs from the sophisticated environment of Full-Press Diplomacy by applying pretrained SAEs, alongside LLM-summarizer methods. We introduce Meta-Autointerp, a method for grouping SAE features into interpretable hypotheses about training dynamics. We discover fine-grained behaviors including role-playing patterns, degenerate outputs, language switching, alongside high-level strategic behaviors and environment-specific bugs. Through automated evaluation, we validate that 90% of discovered SAE Meta-Features are significant, and find a surprising reward hacking behavior. However, through two user studies, we find that even subjectively interesting and seemingly helpful SAE features may be worse than useless to humans, along with most LLM generated hypotheses. However, a subset of SAE-derived hypotheses are predictively useful for downstream tasks. We further provide validation by augmenting an untrained agent's system prompt, improving the score by +14.2%. Overall, we show that SAEs and LLM-summarizer provide complementary views into agent behavior, and together our framework forms a practical starting point for future data-centric interpretability work on ensuring trustworthy LLM behavior throughout training.

Nature Medicine, Published online: 09 February 2026; doi:10.1038/s41591-025-04074-y

In a randomized controlled study involving 1,298 participants from a general sample, performance of humans when assisted by a large language model (LLM) was sensibly inferior to that of the LLM alone when assessing ten medical scenarios leading to disease identification and recommendations for treatment.

Nature Biotechnology, Published online: 09 February 2026; doi:10.1038/s41587-026-03003-9

Insights into regulatory T cell biology are accelerating therapeutic innovation in cancer immunotherapy, autoimmune diseases and transplant rejection.

npj Digital Medicine, Published online: 07 February 2026; doi:10.1038/s41746-026-02419-6

People process technology and operations framework for establishing AI governance in healthcare organizations

Background: Mobile phone ecological momentary assessment (EMA) methods are a well-established measure of eating and drinking behaviors, but compliance can be poor. Micro-EMA (μEMA), which collects information with a single tap response to brief questions on smartwatches, offers a novel application that may improve response rates. To our knowledge, there is no data evaluating μEMA to measure eating habits in children or in low-to-middle-income countries. Objective: In this study, we investigated the feasibility of micro-EMA to measure eating patterns in Malaysian children and adolescents. Methods: We invited 100 children and adolescents aged 7-18 years in Segamat, Malaysia, to participate in 2021-2022. Smartwatches were distributed to 83 children and adolescents who agreed to participate. Participants were asked to wear the smartwatch for 8 days and respond to 12 prompts per day, hourly, from 9AM to 8PM, asking for information on their meals, snacks, and drinks consumed. A questionnaire captured their experiences using the smartwatch and μEMA interface. Response rate (proportion of prompts responded to) assessed participants’ adherence. We explored associations between response rate with time of day, across days, age, and sex using multilevel binomial logistic regression modeling. Results: Eighty-two participants provided usable smartwatch data. The median number (IQR) of meals, drinks, and snacks per day was 2 (2-4), 3 (1-5), and 1 (0-2), respectively, on the first day of the study. The median response rate across the study was 68% (IQR 50-83). The response rate decreased across study days from 74% (68-78) on Day 1 to 40% (30-50) on Day 7 (odds ratio [OR] per study day 0.73, 95% CI 0.64-0.83). Response rate was lowest at the start of the day and highest between the hours of 12 PM and 2 PM. Female participants responded to more prompts than male participants (OR 1.72, 95% CI 1.03-2.86). There was no evidence of differential response by age (OR 0.73, 95% CI 0.41-1.28). Most participants (65%) rated their experience using the smartwatch positively, with 33% saying they were happy to participate in future studies using the smartwatch. For children that did not wear the smartwatch for the full study duration (n=22), discomfort was the most common complaint (41%). Conclusions: In this study of the feasibility of μEMA on smartwatches to measure eating in Malaysian children, we found the method was acceptable. However, response rates declined across study days, resulting in substantial missingness. Future studies (eg, through focus groups) should explore approaches to improving response to event prompts, trial alternative devices to increase children’s comfort, and evaluate revised protocols for reporting of intake events.

Extrachromosomal DNA (ecDNA) is a major source of oncogenic fusions across cancer types, generating tissue-specific fusion landscapes with diagnostic potential. EcDNA-borne PVT1 5′-end fusions stabilize partner RNAs and boost oncogene output.

arXiv:2503.01733v3 Announce Type: replace-cross Abstract: Smart homes equipped with ambient sensors offer a transformative approach to continuous health monitoring and assisted living. Traditional research in this domain primarily focuses on Human Activity Recognition (HAR), which relies on mapping sensor data to a closed set of predefined activity labels. However, the fixed granularity of these labels often constrains their practical utility, failing to capture the subtle, household-specific nuances essential, for example, for tracking individual health over time. To address this, we propose DISCOVER, a framework for discovering and annotating Patterns of Daily Living (PDL) - fine-grained, recurring sequences of sensor events that emerge directly from a resident's unique routines. DISCOVER utilizes a self-supervised feature extraction and representation-aware clustering pipeline, supported by a custom visualization interface that enables experts to interpret and label discovered patterns with minimal effort. Our evaluation across multiple smart-home environments demonstrates that DISCOVER identifies cohesive behavioral clusters with high inter-rater agreement while achieving classification performance comparable to fully-supervised baselines using only 0.01% of the labels. Beyond reducing annotation overhead, DISCOVER establishes a foundation for longitudinal analysis. By grounding behavior in a resident's specific environment rather than rigid semantic categories, our framework facilitates the observation of within-person habitual drift. This capability positions the system as a potential tool for identifying subtle behavioral indicators associated with early-stage cognitive decline in future longitudinal studies.

Nature, Published online: 04 February 2026; doi:10.1038/s41586-025-10087-x

A multiancestry phenome-wide analysis of copy number variants in the UK Biobank genomes increases power to detect genetic associations with complex traits across human populations.

This AI doctor is licensed in all 50 states, the startup says. The deal was led by CRV and Kleiner Perkins.

When is an AI doctor a medical device?

Call it a sign of things to come. A startup called Doctronic made a splash recently when it announced the use AI to renew prescriptions without clinician input in the state of Utah. Something didn’t sit right with me about the announcement. Sure it got approval from Utah, but why isn’t it a medical device subject to Food and Drug Administration review? The company claimed it was “the practice of medicine” and so exempt from FDA authority. That didn’t seem entirely right either. 

So I did some asking around and after talking to over a dozen executives, legal scholars, and policy experts, it turns out the question is not nearly as clear-cut as Doctronic would have us believe. Indeed, it appears the company may be planning to market a medical device without authorization. In my story, I explain the law and why it all matters.

Read more here

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© Adobe

Utah’s recent announcement that it was partnering with a health tech startup that will use artificial intelligence to renew drug prescriptions may offer a glimpse of the futuristic version of AI medicine that’s long been foretold by technologists and venture capitalists.

But it’s only possible because of some pre-approved rule breaking — and may prove a broader test of the Food and Drug Administration’s authority to evaluate a new wave of clinical AI products, according to interviews with executives and experts.

In January, Utah regulators said they had signed an agreement with a startup called Doctronic to launch an AI system that will perform a clinical evaluation of patients and, when deemed appropriate, renew some 200 common medications autonomously.

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© Illustration: Camille MacMillin/STAT; Photos: Adobe

Background: The transformation of digital health technologies has reshaped how healthcare is delivered, particularly in primary care. However, despite the advantages of these innovations, older adults remain among the most resistant users. Traditional technology adoption models may not fully capture the complexity of this reluctance, which is shaped not only by usability challenges but also by emotional, psychological, and identity-related concerns. Innovation Resistance Theory (IRT) offers a complementary framework focused on understanding barriers to adoption rather than solely on facilitators. Objective: To map and synthesize evidence on older adults’ resistance to digital health technologies in primary care through the lens of IRT, and to examine how empirically observed resistance factors align with, extend, or refine IRT’s functional and psychological barriers. Methods: A scoping review combined with concept-driven thematic synthesis was conducted. Empirical studies published between 2014 and 2025 were identified through systematic searches across five databases: PubMed, CINAHL, Ovid Medline, Web of Science, and Scopus. Inclusion criteria focused on studies examining barriers or resistance to digital health use among older adults aged 60 and above in primary care settings. The search was guided by terms related to “older adults”, “digital health”, “eHealth”, “telemedicine”, and “technology resistance”. After screening and reviewing the full texts, data were extracted into a structured matrix, and findings were organized according to the five dimensions of the IRT: usage, value, risk, tradition, and image barriers. Results: Of 4,976 identified records, seventeen studies met the inclusion criteria. Functional barriers included usability challenges, interface complexity, and age-related impairments. Psychological resistance was frequently linked to emotional discomfort, symbolic misalignment, and concerns about the loss of relational care. Value and risk concerns included distrust in diagnostics accuracy, concerns regarding privacy and data security, and skepticism about care quality. Traditional preferences for face-to-face interactions and generational digital divides further reinforced image-based resistance. A key finding was the interaction between barriers, where low self-efficacy and technology anxiety create feedback loops that reinforce avoidance behaviors. Conclusions: Resistance to digital health among older adults is not simply a lack of adoption but a complex, emotionally grounded process involving functional, psychological, and identity-based barriers. Interventions must go beyond technical usability to rebuild emotional trust, preserve the relational aspects of care, and align digital solutions with the values and expectations of older adults. Innovation Resistance Theory offers a comprehensive framework for understanding these multifaceted dynamics and serves as a valuable guide for policy development, user-centered design, and future research Clinical Trial: None

New research from Accenture has discovered insurance executives are planning on increased investment into AI during 2026 despite a widening skills gap in insurance organisations.

Surveying 3,650 C-suite leaders over 20 industries and 20 countries, the Pulse of Change poll revealed 90% of the 218 senior insurance executives intend to spend more on AI over the next year. In all, 85% of the respondents view AI as a tool for revenue expansion not one that reduces costs.

While organisations are upping their AI investment to drive growth, 35% of leaders acknowledge that true progress depends on getting core data strategies and digital abilities right. 54% of employees reported that low-quality or misleading AI outputs are undermining AI’s benefits, leading to reduced productivity and time-wasting.

AI investment may not be enough, Accenture says. Its survey suggests sustainable growth relies on data quality and trusted outputs.

AI adoption enters enterprise scale

The Pulse of Change survey indicates a shift in AI adoption as it goes beyond experimental phases to large scale organisational levels. With 34% of insurance companies now rolling out AI agents in multiple functions, insurers are heading into operational use and away from isolated experiments.

almost a third of senior C-suite leaders are frequently using generative AI, highlighting increased implementation at the highest level. Therefore, AI is undoubtedly shaping workflows, strategies, and key decisions, affecting all facets of businesses.

Nearly a third of businesses are rebuilding entire processes with AI. No longer is the technology a supporting addition to existing workflows; it has become a central component, signalling a more mature stage of AI adoption.

Despite redesigning processes to include AI, fewer than 10% are redesigning employee roles to match such changes, resulting in many employees feeling unprepared. Just 40% claimed their training has equipped them for new AI responsibilities, and only 20% feel like they have any say in how AI affects their work.

AI adoption by companies may be accelerating, but employee use lags behind. There has been a 10 percentage point drop in regular AI use by employees since summer 2025, while only 39% are trying AI tools independently, a drop of 15 points. For effective AI use and to speed up AI adoption among the workforce, companies must be prepared to redesign job roles, align incentives, and provide improved training programmes as, right now, employees are feeling hesitant and unprepared to use AI on their own.

AI investment still fuelling executive optimism amid bubble fears

Although talks around a potential AI bubble continue to cloud the industry, insurance executives remain confident. 47% claimed they would increase AI spending if the bubble burst, and 37% would escalate recruitment.

Altogether, 6% said they would “decrease investments ([by] 20% or more),” 22% would “somewhat decrease investments ([by] up to 20%),” 24% would make “no change,” 40% would “somewhat increase investments (up to 20%),” and 7% would “increase investments (20% or more).”

Khalid Lahraoui, Accenture’s insurance industry group lead, commented, “It’s clear that insurance leaders are confident in AI’s capacity to drive growth, and as such, they are decisively increasing investments, despite ROI uncertainty.”

Lack of AI skills blocking AI’s potential value

As insurance executives prepare to invest heavily in AI, obstacles lie in wait. For instance, a quarter of executives said skill shortages are a core concern and a key player in determining the value they extract from AI. Although these challenges persist in different industries, just 24% of respondents have implemented continuous learning programmes associated with AI. Moreover, only 5% said they are adjusting job positions to support the adoption of AI.

AI adoption disconnect

The disconnect between C-suite leaders and employees is evident from the survey’s data. Although talent is the main driver of AI scaling, employees feel less confident and secure than leadership assumes. 23% of C-suite leaders said improved access to skilled talent would accelerate their AI implementation strategies. 38% of employees believe their organisation would respond effectively to technological disruption, but just 30% feel confident about how their company would handle talent disruption.

Job security is also waning, with 48% feeling secure in their roles, down from 59% in summer 2025. Meanwhile, 59% of workers believe young professionals are finding it more challenging to find jobs due to automation and AI. Leadership may see talent as an accelerator for AI, but anxiety around job security and organisational readiness persists.

Key focus is on investment

Approximately two thirds of executives are prioritising investments in digital technologies and AI amid the rapid changes facing global industries. 67% reported feeling well-prepared for technological disruption, but only 39% felt confident if there was environmental disruption, and 44% for geopolitical disruption.

Again, there is a divide between leadership and employees, with only 29% of insurance workers feeling confident during economic disruption compared to 43% of leaders.

Optimism among insurance executives and C-suite leaders as a whole remains high, despite 82% expecting further changes in 2026, a 24 percentage gap with employees. 78% anticipate stronger and faster revenue growth in the next year and 82% have plans to increase recruitment.

According to Accenture’s report, the key challenge is not AI technology itself; it’s getting employees on board, engaged, and ready to work with AI.

As the report notes, bridging the gap between technology and people is the key to success. “2026 will favour those that align the confidence in their technological investments with commitment to workforce needs,” the report concludes.

(Image source: “Accenture Building City View Plaza San Jose” by mrkathika is licensed under CC BY-SA 2.0.)

 

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