Publication date: June 2026

Source: Artificial Intelligence in Medicine, Volume 176

Author(s): Antonio Bozzani, Pietro Cerveri, Vittorio Arici, Giulia Maria Stella

Publication date: June 2026

Source: Artificial Intelligence in Medicine, Volume 176

Author(s): Luke Liang, Ryan Schuerkamp, Ketra L. Rice, M. Melissa Brown, Nisha Nataraj, Jennifer Mendoza-Alonzo, Curtis Florence, Andrew Page, Christopher R. Harper, Benjamin Maldonado, Brendan Moran, Philippe J. Giabbanelli

Publication date: June 2026

Source: Artificial Intelligence in Medicine, Volume 176

Author(s): Xiaobo Zhang, Jinyu Bao, Jianzhong Ye, Xiaoping Qiu, Yi Pan

The SEC is working on a proposal to allow public companies to release earnings reports twice a year instead of quarterly, per the WSJ. 

A redesigned cancer immunotherapy is showing striking early results after decades of disappointment with similar drugs. Researchers engineered a more powerful CD40 agonist antibody and changed how it’s delivered—injecting it directly into tumors instead of into the bloodstream. In a small clinical trial of 12 patients with metastatic cancers, six saw their tumors shrink and two experienced complete remission.

A new UCLA Health study suggests that long-term exposure to the pesticide chlorpyrifos may dramatically raise the risk of Parkinson’s disease. Researchers found that people living in areas with sustained exposure had more than 2.5 times the likelihood of developing the disorder. Lab experiments reinforced the finding: animals exposed to the chemical developed movement problems, lost dopamine-producing neurons, and showed the same toxic protein buildup seen in Parkinson’s patients.

Nvidia announced an open enterprise AI agent platform, called NemoClaw, that is built off of viral OpenClaw.

Nvidia CEO Jensen Huang said he expects $1 trillion worth of orders for the chips.

Sen. Elizabeth Warren noted that Grok, xAI's controversial chatbot, has created harmful outputs for users and poses a potential national security risk.

Memories.ai is building a large visual memory model that can index and retrieve video-recorded memories for physical AI.

GridBeyond's hardware and software coordinates several gigawatts of supply and demand to help balance the flow of electricity on the grid. The idea has attracted investors like Samsung Ventures.

The move could help Apple better compete with Adobe Premiere Pro and the Adobe Creative Cloud suite.

The three plaintiffs are seeking to represent anyone who had real images of them as a minor altered into sexual content by Grok.

Nvidia’s new DLSS 5 uses generative AI and structured graphics data to make video games more realistic. CEO Jensen Huang says the approach could eventually spread to other industries.

Background: “I’m not a doctor, but...” is a typical response when asking considerate laypeople for health advice. However, seeking medical advice has also shifted to digital settings, where the expertise of the other party is less transparent than in face-to-face interactions. Recently, large language models (LLMs) have emerged as easily accessible tools, offering a novel way to formulate medical questions and receive seemingly qualified advice. Given the sensitive nature of health-related queries and the lack of professional supervision, incorrect advice can pose serious health risks. Therefore, including explicit disclaimers and precise referrals in LLM responses to medical queries is crucial. However, little is known about how LLMs adapt their safety implementations in response to different urgency levels. Objective: This study evaluates disclaimer and referral patterns in responses from LLMs to authentic medical queries of different urgency levels using a systematic evaluation framework. Methods: This prospective, multimodel evaluation study generated and analyzed 908 responses from 4 popular LLMs (GPT-4o, Claude Sonnet-4, Grok-3, and DeepSeek-V3) to 227 authentic patient queries from a public dataset. Two human raters classified all 227 patient queries using a 3-level urgency scale. LLM responses were evaluated using a 5-point ordinal classification system for disclaimer and referral advice, ranging from “no disclaimer” to “urgent advice to consult a medical professional.” GPT-4o served as the primary rater model for this task after conducting a subset validation against human expert annotations. Statistical analyses included Jonckheere-Terpstra tests to examine the relationship between case urgency and disclaimer ratings and Kruskal-Wallis tests for intermodel comparisons. Results: The 227 patient queries were distributed as 77 (34%) low-urgency, 110 (48%) intermediate-urgency, and 40 (18%) high-urgency cases. All 4 LLMs demonstrated statistically significant ordered trends (all

Apple’s new MacBook Neo isn’t just the most affordable MacBook — it’s also the company's most repairable laptop in “about fourteen years."

At Nvidia CEO Jensen Huang's urging, Frore developed liquid-cooling tech for chips. That shift helped it raise $143 million.

The startup also announced a $5 million "rescue fund" to help credit unions ditch legacy software for its AI-native platform.

Encyclopedia Britannica and Merriam-Webster say that OpenAI violated the copyright of almost 100,000 articles by using them for LLM training.

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It’s been just over two weeks since OpenAI reached a controversial agreement to allow the Pentagon to use its AI in classified environments. There are still pressing questions about what exactly OpenAI’s agreement allows for; Sam Altman said the military can’t use his company’s technology to build autonomous weapons, but the agreement really just demands that the military follow its own (quite permissive) guidelines about such weapons. OpenAI’s other main claim, that the agreement will prevent use of its technology for domestic surveillance, appears equally dubious.

It’s unclear what OpenAI’s motivations are. It’s not the first tech giant to embrace military contracts it had once vowed never to enter into, but the speed of the pivot was notable. Perhaps it’s just about money; OpenAI is spending lots on AI training and is on the hunt for more revenue (from sources including ads). Or perhaps Altman truly believes the ideological framing he often invokes: that liberal democracies (and their militaries) must have access to the most powerful AI to compete with China.

The more consequential question is what happens next. OpenAI has decided it is comfortable operating right in the messy heart of combat, just as the US escalates its strikes against Iran (with AI playing a larger role in that than ever before). So where exactly could OpenAI’s tech show up in this fight? And which applications will its customers (and employees) tolerate?

Targets and strikes

Though its Pentagon agreement is in place, it’s unclear when OpenAI’s technology will be ready for classified environments, since it must be integrated with other tools the military uses (Elon Musk’s xAI, which recently struck its own deal with the Pentagon, is expected to go through the same process with its AI model Grok). But there’s pressure to do this quickly because of controversy around the technology in use to date: After Anthropic refused to allow its AI to be used for “any lawful use,” President Trump ordered the military to stop using it, and Anthropic was designated a supply chain risk by the Pentagon. (Anthropic is fighting the designation in court.)

If the Iran conflict is still underway by the time OpenAI’s tech is in the system, what could it be used for? A recent conversation I had with a defense official suggests it might look something like this: A human analyst could put a list of potential targets into the AI model and ask it to analyze the information and prioritize which to strike first. The model could account for logistics information, like where particular planes or supplies are located. It could analyze lots of different inputs in the form of text, image, and video. 

A human would then be responsible for manually checking these outputs, the official said. But that raises an obvious question: If a person is truly double-checking AI’s outputs, how is it speeding up targeting and strike decisions?

For years the military has been using another AI system, called Maven, which can handle things like automatically analyzing drone footage to identify possible targets. It’s likely that OpenAI’s models, like Anthropic’s Claude, will offer a conversational interface on top of that, allowing users to ask for interpretations of intelligence and recommendations for which targets to strike first. 

It’s hard to overstate how new this is: AI has long done analysis for the military, drawing insights out of oceans of data. But using generative AI’s advice about which actions to take in the field is being tested in earnest for the first time in Iran.

Drone defense

At the end of 2024, OpenAI announced a partnership with Anduril, which makes both drones and counter-drone technologies for the military. The agreement said OpenAI would work with Anduril to do time-sensitive analysis of drones attacking US forces and help take them down. An OpenAI spokesperson told me at the time that this didn’t violate the company’s policies, which prohibited “systems designed to harm others,” because the technology was being used to target drones and not people. 

Anduril provides a suite of counter-drone technologies to military bases around the world (though the company declined to tell me whether its systems are deployed near Iran). Neither company has provided updates on how the project has developed since it was announced. However, Anduril has long trained its own AI models to analyze camera footage and sensor data to identify threats; what it focuses less on are conversational AI systems that allow soldiers to query those systems directly or receive guidance in natural language—an area where OpenAI’s models may fit.

The stakes are high. Six US service members were killed in Kuwait on March 1 following an Iranian drone attack that was not intercepted by US air defenses. 

Anduril’s interface, called Lattice, is where soldiers can control everything from drone defenses to missiles and autonomous submarines. And the company is winning massive contracts—$20 billion from the US Army just last week—to connect its systems with legacy military equipment and layer AI on them. If OpenAI’s models prove useful to Anduril, Lattice is designed to incorporate them quickly across this broader warfare stack. 

Back-office AI

In December, Defense Secretary Pete Hegseth started encouraging millions of people in more administrative roles in the military—contracts, logistics, purchasing—to use a new AI tool. Called GenAI.mil, it provided a way for personnel to securely access commercial AI models and use them for the same sorts of things as anyone in the business world. 

Google Gemini was one of the first to be available. In January, the Pentagon announced that xAI’s Grok was going to be added to the GenAI.mil platform as well, despite incidents in which the model had spread antisemitic content and created nonconsensual deepfakes. OpenAI followed in February, with the company announcing that its models would be used for drafting policy documents and contracts and assisting with administrative support of missions.

Anyone using ChatGPT for unclassified tasks on this platform is unlikely to have much sway over sensitive decisions in Iran, but the prospect of OpenAI deploying on the platform is important in another way. It serves the all-in attitude toward AI that Hegseth has been pushing relentlessly across the Pentagon (even if many early users aren’t entirely sure what they’re supposed to use it for). The message is that AI is transforming every aspect of how the US fights, from targeting decisions down to paperwork. And OpenAI is increasingly winning a piece of it all.