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. ($)
+ Want to eat healthier? Slow down and tune out food ‘noise’. ($)

Background: Health care artificial intelligence (AI) systems are increasingly integrated into clinical workflows, yet remain vulnerable to data-poisoning attacks. A small number of manipulated training samples can compromise AI models used for diagnosis, documentation, and resource allocation. Existing privacy regulations, including the Health Insurance Portability and Accountability Act and the General Data Protection Regulation, may inadvertently complicate anomaly detection and cross-institutional auditing, thereby limiting visibility into adversarial activity. Objective: This study provides a comprehensive threat analysis of data poisoning vulnerabilities across major health care AI architectures. The goals are to (1) identify attack surfaces in clinical AI systems, (2) evaluate the feasibility and detectability of poisoning attacks analytically modeled in prior security research, and (3) propose a multilayered defense framework appropriate for health care settings. Methods: We synthesized empirical findings from 41 key security studies published between 2019 and 2025 and integrated them into an analytical threat-modeling framework specific to health care. We constructed 8 hypothetical yet technically grounded attack scenarios across 4 categories: (1) architecture-specific attacks on convolutional neural networks, large language models, and reinforcement learning agents (scenario A); (2) infrastructure exploitation in federated learning and clinical documentation pipelines (scenario B); (3) poisoning of critical resource allocation systems (scenario C); and (4) supply chain attacks affecting commercial foundation models (scenario D). Scenarios were aligned with realistic insider-access threat models and current clinical deployment practices. Results: Multiple empirical studies demonstrate that attackers with access to as few as 100-500 poisoned samples can compromise health care AI systems, with attack success rates typically ≥60%. Critically, attack success depends on the absolute number of poisoned samples rather than their proportion of the training corpus, a finding that fundamentally challenges assumptions that larger datasets provide inherent protection. We estimate that detection delays commonly range from 6 to 12 months and may extend to years in distributed or privacy-constrained environments. Analytical scenarios highlight that (1) routine insider access creates numerous injection points across health care data infrastructure, (2) federated learning amplifies risks by obscuring attribution, and (3) supply chain compromises can simultaneously affect dozens to hundreds of institutions. Privacy regulations further complicate cross-patient correlation and model audit processes, substantially delaying the detection of subtle poisoning campaigns. Conclusions: Health care AI systems face significant security challenges that current regulatory frameworks and validation practices do not adequately address. We propose a multilayered defense strategy that combines ensemble disagreement monitoring, adversarial testing, privacy-preserving yet auditable mechanisms, and strengthened governance requirements. Ensuring patient safety may require a shift from opaque, high-performance models toward more interpretable and constraint-driven architectures with verifiable robustness guarantees.

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.