npj Digital Medicine, Published online: 24 January 2026; doi:10.1038/s41746-026-02359-1

Deep learning for malignancy and tumor origin prediction using cytology or histopathology whole slide images

npj Digital Medicine, Published online: 24 January 2026; doi:10.1038/s41746-025-02320-8

The diagnostic accuracy of wearable digital technology in detecting fertility window and menstrual cycles: a systematic review and Bayesian network meta-analysis

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.

“Dr. Google” had its issues. Can ChatGPT Health do better?  

For the past two decades, there’s been a clear first step for anyone who starts experiencing new medical symptoms: Look them up online. The practice was so common that it gained the pejorative moniker “Dr. Google.” But times are changing, and many medical-information seekers are now using LLMs. According to OpenAI, 230 million people ask ChatGPT health-related queries each week.  

That’s the context around the launch of OpenAI’s new ChatGPT Health product, which debuted earlier this month. The big question is: can the obvious risks of using AI for health-related queries be mitigated enough for them to be a net benefit? Read the full story. 

—Grace Huckins

America’s coming war over AI regulation  

In the final weeks of 2025, the battle over regulating artificial intelligence in the US reached boiling point. On December 11, after Congress failed twice to pass a law banning state AI laws, President Donald Trump signed a sweeping executive order seeking to handcuff states from regulating the booming industry.  

Instead, he vowed to work with Congress to establish a “minimally burdensome” national AI policy. The move marked a victory for tech titans, who have been marshaling multimillion-dollar war chests to oppose AI regulations, arguing that a patchwork of state laws would stifle innovation.

In 2026, the battleground will shift to the courts. While some states might back down from passing AI laws, others will charge ahead. Read our story about what’s on the horizon. 

—Michelle Kim

This story is from MIT Technology Review’s What’s Next series of stories that look across industries, trends, and technologies to give you a first look at the future. You can read the rest of them here.  

Measles is surging in the US. Wastewater tracking could help.

This week marked a rather unpleasant anniversary: It’s a year since Texas reported a case of measles—the start of a significant outbreak that ended up spreading across multiple states. Since the start of January 2025, there have been over 2,500 confirmed cases of measles in the US. Three people have died. 

As vaccination rates drop and outbreaks continue, scientists have been experimenting with new ways to quickly identify new cases and prevent the disease from spreading. And they are starting to see some success with wastewater surveillance. Read the full story.

—Jessica Hamzelou 

This story is from The Checkup, our weekly newsletter giving you the inside track on all things health and biotech. Sign up to receive it in your inbox every Thursday.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 The US is dismantling itself
A foreign enemy could not invent a better chain of events to wreck its standing in the world. (Wired $)  
+ We need to talk about whether Donald Trump might be losing it.  (New Yorker $)

2 Big Tech is taking on more debt to fund its AI aspirations
And the bubble just keeps growing. (WP $)
+ Forget unicorns. 2026 is shaping up to be the year of the “hectocorn.” (The Guardian)
+ Everyone in tech agrees we’re in a bubble. They just can’t agree on what happens when it pops. (MIT Technology Review)

3 DOGE accessed even more personal data than we thought 
Even now, the Trump administration still can’t say how much data is at risk, or what it was used for. (NPR)

4 TikTok has finalized a deal to create a new US entity 
Ending years of uncertainty about its fate in America. (CNN)
+ Why China is the big winner out of all of this. (FT $)

5 The US is now officially out of the World Health Organization 
And it’s leaving behind nearly $300 million in bills unpaid. (Ars Technica) 
+ The US withdrawal from the WHO will hurt us all. (MIT Technology Review)

6 AI-powered disinformation swarms pose a threat to democracy
A would-be autocrat could use them to persuade populations to accept cancelled elections or overturn results. (The Guardian)
+ The era of AI persuasion in elections is about to begin. (MIT Technology Review)

7 We’re about to start seeing more robots everywhere
But exactly what they’ll look like remains up for debate. (Vox $)
+ Chinese companies are starting to dominate entire sectors of AI and robotics. (MIT Technology Review)

8 Some people seem to be especially vulnerable to loneliness
If you’re ‘other-directed’, you could particularly benefit from less screentime. (New Scientist $)

9 This academic lost two years of work with a single click
TL;DR: Don’t rely on ChatGPT to store your data. (Nature)

10 How animals develop a sense of direction 
Their ‘internal compass’ seems to be informed by landmarks that help them form a mental map. (Quanta $)

Quote of the day

“The rate at which AI is progressing, I think we have AI that is smarter than any human this year, and no later than next year.”

—Elon Musk simply cannot resist the urge to make wild predictions at Davos, Wired reports. 

One more thing

Africa fights rising hunger by looking to foods of the past

After falling steadily for decades, the prevalence of global hunger is now on the rise—nowhere more so than in sub-Saharan Africa. 

Africa’s indigenous crops are often more nutritious and better suited to the hot and dry conditions that are becoming more prevalent, yet many have been neglected by science, which means they tend to be more vulnerable to diseases and pests and yield well below their theoretical potential.

Now the question is whether researchers, governments, and farmers can work together in a way that gets these crops onto plates and provides Africans from all walks of life with the energy and nutrition that they need to thrive, whatever climate change throws their way. Read the full story.

—Jonathan W. Rosen

We can still have nice things

A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or skeet ’em at me.)

+ The only thing I fancy dry this January is a martini. Here’s how to make one.
+ If you absolutely adore the Bic crystal pen, you might want this lamp. 
+ Cozy up with a nice long book this winter. ($)
+ Want to eat healthier? Slow down and tune out food ‘noise’. ($)

Nature Medicine, Published online: 23 January 2026; doi:10.1038/s41591-025-04198-1

We propose straightforward principles to foster an evaluation-forward operating system that can transform the adoption of clinical artificial intelligence from a leap of faith into a stepwise, trust-building process.

Humans&, a new startup founded by alumni of Anthropic, Meta, OpenAI, xAI, and Google DeepMind, is building the next generation of foundation models for collaboration, not chat.

arXiv:2601.15059v1 Announce Type: new Abstract: Modern CI/CD pipelines integrating agent-generated code exhibit a structural failure in responsibility attribution. Decisions are executed through formally correct approval processes, yet no entity possesses both the authority to approve those decisions and the epistemic capacity to meaningfully understand their basis. We define this condition as responsibility vacuum: a state in which decisions occur, but responsibility cannot be attributed because authority and verification capacity do not coincide. We show that this is not a process deviation or technical defect, but a structural property of deployments where decision generation throughput exceeds bounded human verification capacity. We identify a scaling limit under standard deployment assumptions, including parallel agent generation, CI-based validation, and individualized human approval gates. Beyond a throughput threshold, verification ceases to function as a decision criterion and is replaced by ritualized approval based on proxy signals. Personalized responsibility becomes structurally unattainable in this regime. We further characterize a CI amplification dynamic, whereby increasing automated validation coverage raises proxy signal density without restoring human capacity. Under fixed time and attention constraints, this accelerates cognitive offloading in the broad sense and widens the gap between formal approval and epistemic understanding. Additional automation therefore amplifies, rather than mitigates, the responsibility vacuum. We conclude that unless organizations explicitly redesign decision boundaries or reassign responsibility away from individual decisions toward batch- or system-level ownership, responsibility vacuum remains an invisible but persistent failure mode in scaled agent deployments.

arXiv:2601.14298v1 Announce Type: cross Abstract: The AI era has ushered in Large Language Models (LLM) to the technological forefront, which has been much of the talk in 2023, and is likely to remain as such for many years to come. LLMs are the AI models that are the power house behind generative AI applications such as ChatGPT. These AI models, fueled by vast amounts of data and computational prowess, have unlocked remarkable capabilities, from human-like text generation to assisting with natural language understanding (NLU) tasks. They have quickly become the foundation upon which countless applications and software services are being built, or at least being augmented with. However, as with any groundbreaking innovations, the rise of LLMs brings forth critical safety, privacy, and ethical concerns. These models are found to have a propensity to leak private information, produce false information, and can be coerced into generating content that can be used for nefarious purposes by bad actors, or even by regular users unknowingly. Implementing safeguards and guardrailing techniques is imperative for applications to ensure that the content generated by LLMs are safe, secure, and ethical. Thus, frameworks to deploy mechanisms that prevent misuse of these models via application implementations is imperative. In this study, wepropose a Flexible Adaptive Sequencing mechanism with trust and safety modules, that can be used to implement safety guardrails for the development and deployment of LLMs.

arXiv:2601.14305v1 Announce Type: cross Abstract: The increase in the number of Internet of Things (IoT) devices has tremendously increased the attack surface of cyber threats thus making a strong intrusion detection system (IDS) with a clear explanation of the process essential towards resource-constrained environments. Nevertheless, current IoT IDS systems are usually traded off with detection quality, model elucidability, and computational effectiveness, thus the deployment on IoT devices. The present paper counteracts these difficulties by suggesting an explainable AI (XAI) framework based on an optimized Decision Tree classifier with both local and global importance methods: SHAP values that estimate feature attribution using local explanations, and Morris sensitivity analysis that identifies the feature importance in a global view. The proposed system attains the state of art on the test performance with 99.91% accuracy, F1-score of 99.51% and Cohen Kappa of 0.9960 and high stability is confirmed by a cross validation mean accuracy of 98.93%. Efficiency is also enhanced in terms of computations to provide faster inferences compared to those that are generalized in ensemble models. SrcMac has shown as the most significant predictor in feature analyses according to SHAP and Morris methods. Compared to the previous work, our solution eliminates its major drawback lack because it allows us to apply it to edge devices and, therefore, achieve real-time processing, adhere to the new regulation of transparency in AI, and achieve high detection rates on attacks of dissimilar classes. This combination performance of high accuracy, explainability, and low computation make the framework useful and reliable as a resource-constrained IoT security problem in real environments.

arXiv:2601.14525v1 Announce Type: cross Abstract: Automated AI research holds great potential to accelerate scientific discovery. However, current LLMs often generate plausible-looking but ineffective ideas. Execution grounding may help, but it is unclear whether automated execution is feasible and whether LLMs can learn from the execution feedback. To investigate these, we first build an automated executor to implement ideas and launch large-scale parallel GPU experiments to verify their effectiveness. We then convert two realistic research problems - LLM pre-training and post-training - into execution environments and demonstrate that our automated executor can implement a large fraction of the ideas sampled from frontier LLMs. We analyze two methods to learn from the execution feedback: evolutionary search and reinforcement learning. Execution-guided evolutionary search is sample-efficient: it finds a method that significantly outperforms the GRPO baseline (69.4% vs 48.0%) on post-training, and finds a pre-training recipe that outperforms the nanoGPT baseline (19.7 minutes vs 35.9 minutes) on pre-training, all within just ten search epochs. Frontier LLMs often generate meaningful algorithmic ideas during search, but they tend to saturate early and only occasionally exhibit scaling trends. Reinforcement learning from execution reward, on the other hand, suffers from mode collapse. It successfully improves the average reward of the ideator model but not the upper-bound, due to models converging on simple ideas. We thoroughly analyze the executed ideas and training dynamics to facilitate future efforts towards execution-grounded automated AI research.

arXiv:2601.14822v1 Announce Type: cross Abstract: Melanoma detection is vital for early diagnosis and effective treatment. While deep learning models on dermoscopic images have shown promise, they require specialized equipment, limiting their use in broader clinical settings. This study introduces a multi-modal melanoma detection system using conventional photo images, making it more accessible and versatile. Our system integrates image data with tabular metadata, such as patient demographics and lesion characteristics, to improve detection accuracy. It employs a multi-modal neural network combining image and metadata processing and supports a two-step model for cases with or without metadata. A three-stage pipeline further refines predictions by boosting algorithms and enhancing performance. To address the challenges of a highly imbalanced dataset, specific techniques were implemented to ensure robust training. An ablation study evaluated recent vision architectures, boosting algorithms, and loss functions, achieving a peak Partial ROC AUC of 0.18068 (0.2 maximum) and top-15 retrieval sensitivity of 0.78371. Results demonstrate that integrating photo images with metadata in a structured, multi-stage pipeline yields significant performance improvements. This system advances melanoma detection by providing a scalable, equipment-independent solution suitable for diverse healthcare environments, bridging the gap between specialized and general clinical practices.

arXiv:2601.15161v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used for clinical decision support, where hallucinations and unsafe suggestions may pose direct risks to patient safety. These risks are particularly challenging as they often manifest as subtle clinical errors that evade detection by generic metrics, while expert-authored fine-grained rubrics remain costly to construct and difficult to scale. In this paper, we propose a retrieval-augmented multi-agent framework designed to automate the generation of instance-specific evaluation rubrics. Our approach grounds evaluation in authoritative medical evidence by decomposing retrieved content into atomic facts and synthesizing them with user interaction constraints to form verifiable, fine-grained evaluation criteria. Evaluated on HealthBench, our framework achieves a Clinical Intent Alignment (CIA) score of 60.12%, a statistically significant improvement over the GPT-4o baseline (55.16%). In discriminative tests, our rubrics yield a mean score delta ($\mu_{\Delta} = 8.658$) and an AUROC of 0.977, nearly doubling the quality separation achieved by GPT-4o baseline (4.972). Beyond evaluation, our rubrics effectively guide response refinement, improving quality by 9.2% (from 59.0% to 68.2%). This provides a scalable and transparent foundation for both evaluating and improving medical LLMs. The code is available at https://anonymous.4open.science/r/Automated-Rubric-Generation-AF3C/.

arXiv:2505.20310v2 Announce Type: replace Abstract: Meta-analysis is a systematic research methodology that synthesizes data from multiple existing studies to derive comprehensive conclusions. This approach not only mitigates limitations inherent in individual studies but also facilitates novel discoveries through integrated data analysis. Traditional meta-analysis involves a complex multi-stage pipeline including literature retrieval, paper screening, and data extraction, which demands substantial human effort and time. However, while LLM-based methods can accelerate certain stages, they still face significant challenges, such as hallucinations in paper screening and data extraction. In this paper, we propose a multi-agent system, Manalyzer, which achieves end-to-end automated meta-analysis through tool calls. The hybrid review, hierarchical extraction, self-proving, and feedback checking strategies implemented in Manalyzer significantly alleviate these two hallucinations. To comprehensively evaluate the performance of meta-analysis, we construct a new benchmark comprising 729 papers across 3 domains, encompassing text, image, and table modalities, with over 10,000 data points. Extensive experiments demonstrate that Manalyzer achieves significant performance improvements over the LLM baseline in multi meta-analysis tasks. Project page: https://black-yt.github.io/meta-analysis-page/ .

arXiv:2307.13658v3 Announce Type: replace-cross Abstract: We propose that future AI transparency and accountability regulations are based on an open global standard for exchanging information about AI systems, which allows co-existence of potentially conflicting local regulations. Then, we discuss key components of a lightweight and effective AI transparency and/or accountability regulation. To prevent overregulation, the proposed approach encourages collaboration between regulators and industry to create a scalable and cost-efficient mutually beneficial solution. This includes using automated assessments and benchmarks with results transparently communicated through AI cards in an open AI register to facilitate meaningful public comparisons of competing AI systems. Such AI cards should report standardized measures tailored to the specific high-risk applications of AI systems and could be used for conformity assessments under AI transparency and accountability policies such as the European Union's AI Act.

arXiv:2509.19774v2 Announce Type: replace-cross Abstract: Electrocardiography (ECG) is the clinical gold standard for cardiovascular disease (CVD) assessment, yet continuous monitoring is constrained by the need for dedicated hardware and trained personnel. Photoplethysmography (PPG) is ubiquitous in wearable devices and readily scalable, but it lacks electrophysiological specificity, limiting diagnostic reliability. While generative methods aim to translate PPG into clinically useful ECG signals, existing approaches are limited by the misalignment of physiological semantics in generative models and the complexity of modeling in high-dimensional signals. To address these limitations, we propose PPGFlowECG, a two-stage framework that aligns PPG and ECG in a shared latent space using the CardioAlign Encoder and then synthesizes ECGs with latent rectified flow. We further provide a formal analysis of this coupling, showing that the CardioAlign Encoder is necessary to guarantee stable and semantically consistent ECG synthesis under our formulation. Extensive experiments on four datasets demonstrate improved synthesis fidelity and downstream diagnostic utility. These results indicate that PPGFlowECG supports scalable, wearable-first CVD screening when standard ECG acquisition is unavailable.

Multiplex immunofluorescence proteomics is a powerful method in spatial biology to decipher cell types, states, and architecture in tissues. In this issue of Cell, Valanarasu et al. develop GigaTIME, an expansive population-scale analysis of the tumor immune microenvironment of over 14,000 patients across 24 cancer types, enabling clinical discovery and patient stratification.

npj Digital Medicine, Published online: 22 January 2026; doi:10.1038/s41746-026-02363-5

Large language models improve transferability of electronic health record-based predictions across countries and coding systems

GigaTIME leverages multimodal AI to generate virtual multiplex immunofluorescence (mIF) profiles from standard H&E slides, enabling comprehensive tumor immune microenvironment modeling across a large (>14,000) and diverse patient population. This virtual approach unlocks new opportunities for large-scale clinical discoveries that were previously hindered by the scarcity of mIF data.

The medical info database has doubled in valuation since last raise in October, despite encroachment from model makers.

OpenEvidence, maker of a popular chatbot that helps doctors search clinical evidence, on Wednesday announced $250 million in new funding.

The new round led by Thrive Capital and DST Global values OpenEvidence at $12 billion, and the company has announced $735 million in funding in the last 12 months. OpenEvidence is free to use by any clinician with a national provider identifier number. The company’s primary business model is advertising shown to clinicians. 

Founded in 2022, OpenEvidence is one of the most prominent and best-funded companies from a wave of health artificial intelligence companies that emerged since the widespread availability of large language models. Reflecting on the eye-popping fundraising for health AI, a Silicon Valley Bank report released earlier in January raised an eyebrow at the ability of companies like OpenEvidence to deliver on their stratospheric valuations with advertising and software-as-a-service business models. “It won’t be a surprise to see them tap the value of the data they’re already collecting” to offer more services to pharma or other customers, the authors wrote.

Continue to STAT+ to read the full story…

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