"If you are interested in helping shape and deliver the next chapter of Palantir's work across DHS, please reach out," a Palantir executive wrote to employees about the massive purchasing agreement. The Department of Homeland Security struck a $1 billion purchasing agreement with Palantir last week, further reinforcing the software company's role in the federal agency that oversees the nation's immigration enforcement . According to contracting documents published last week, the blanket purchase agreement (BPA) awarded "is to provide Palantir commercial software licenses, maintenance, and implementation services department wide." The agreement simplifies how DHS buys software from Palantir, allowing DHS agencies like Customs and Border Protection (CBP) and Immigration and Customs Enforcement (ICE) to essentially skip the competitive bidding process for new purchases of up to $1 billion in products and services from the company. Palantir did not immediately respond to a request for comment.

Others say they don't need us. It's time for us to think of ourselves not as the high priests of health care but as what we have always been: healers. Not long ago, I cared for a middle-aged man I'll call Jim, who was generally healthy but had recently started to feel sluggish. One of his friends told him to try a hormone supplement. After Jim saw on social media that Robert F. Kennedy, Jr., the Trump Administration's Secretary of Health and Human Services, had endorsed supplements as a part of an "anti-aging" regimen, he ordered one from a telehealth company. A few months later, he noticed swelling and pain in his calf. ChatGPT warned him that he might have a blood clot.

Medical Large Language Models (LLMs) are increasingly deployed for clinical decision support across diverse specialties, yet systematic evaluation of their robustness to adversarial misuse and privacy leakage remains inaccessible to most researchers. Existing security benchmarks require GPU clusters, commercial API access, or protected health data -- barriers that limit community participation in this critical research area. We propose a practical, fully reproducible framework for evaluating medical AI security under realistic resource constraints. Our framework design covers multiple medical specialties stratified by clinical risk -- from high-risk domains such as emergency medicine and psychiatry to general practice -- addressing jailbreaking attacks (role-playing, authority impersonation, multi-turn manipulation) and privacy extraction attacks. All evaluation utilizes synthetic patient records requiring no IRB approval. The framework is designed to run entirely on consumer CPU hardware using freely available models, eliminating cost barriers. We present the framework specification including threat models, data generation methodology, evaluation protocols, and scoring rubrics. This proposal establishes a foundation for comparative security assessment of medical-specialist models and defense mechanisms, advancing the broader goal of ensuring safe and trustworthy medical AI systems.

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This study examines the extent to which U.S. federal agencies responded to and implemented the principles outlined in the White House's October 2022 "Blueprint for an AI Bill of Rights." The Blueprint provided a framework for the ethical governance of artificial intelligence systems, organized around five core principles: safety and effectiveness, protection against algorithmic discrimination, data privacy, notice and explanation about AI systems, and human alternatives and fallback. Through an analysis of publicly available records across 15 federal departments, the authors found limited evidence that the Blueprint directly influenced agency actions after its release. Only five departments explicitly mentioned the Blueprint, while 12 took steps aligned with one or more of its principles. However, much of this work appeared to have precedents predating the Blueprint or motivations disconnected from it, such as compliance with prior executive orders on trustworthy AI. Departments' activities often emphasized priorities like safety, accountability and transparency that overlapped with Blueprint principles, but did not necessarily stem from it. The authors conclude that the non-binding Blueprint seems to have had minimal impact on shaping the U.S. government's approach to ethical AI governance in its first year. Factors like public concerns after high-profile AI releases and obligations to follow direct executive orders likely carried more influence over federal agencies. More rigorous study would be needed to definitively assess the Blueprint's effects within the federal bureaucracy and broader society.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Northeastern University in Boston will be given $17.5 million by the Centers for Disease Control and Prevention (CDC) to lead an innovation center focused on infectious disease detection and preparation, the university announced. The Center for Advanced Epidemic Analytics and Predictive Modeling Technology, or EPISTORM, will "help detect and prepare the United States for the next outbreak of infectious disease, especially in rural areas," according to the university's Northeastern Global News (NGN). The funds will be used to coordinate the work of various consortium members across the U.S. to prepare local communities for outbreaks, including RSV and the seasonal flu.

As more doctors and nurses leave the profession, providers are turning to AI technology to help bridge the coverage gap, especially among older Americans. As many as 55% of older adults fail to take their prescribed medications, according to data from the Department of Health and Human Services. Now a new artificial intelligence app aims to change that. "Together," a free iPhone app built on generative AI, is designed to help aging adults and their caregivers manage medications and other health care tasks. Using a smartphone camera, the person simply snaps a picture of a prescription bottle.

Health and human services (HHS) agencies often struggle to serve some of society's most needy populations. At many HHS agencies today, tight budgets limit the size of the workforce, even as the volume of caseloads continues to grow. That imbalance makes it hard to provide efficient and effective solutions to address the critical needs of individuals and families, and can leave employees feeling stressed and overworked. Those same employees may also see few opportunities for career development or advancement. High rates of turnover can put a steady stream of inexperienced staff into critical jobs with little training to prepare them.

The Department of Health and Human Services is developing an artificial intelligence playbook to help teams overcome common obstacles and challenges that come with implementing AI technologies. HHS Chief AI Officer Oki Mek discussed the playbook and how it plays into his overall priority of making AI a collaboratively cultivated technology at the agency during a NextGov event July 29. He said that one of the elements that he hopes to include in the playbook is to help with barriers to data acquisition, which he added is especially difficult within HHS. "Having a playbook could really help in terms of, what are the obstacles that you will encounter when you go on this AI, machine learning journey, because the two biggest obstacles are really the data acquisition, getting the data, especially with Health and Human Services because health records and data are very heavily regulated, so data acquisition will be tough," Mek said. "We could help provide some guidance and some lessons learned, some best practices around that." Mek added that the playbook could also provide some guidance around cleaning data, since cleaning and processing data is a big component of getting it ready for AI usage. The playbook will also provide definitions around AI, which Mek argued is a broad term that can have different meanings and applications.

This story was published in partnership with Type Investigations with support from the Puffin Foundation. In California, non-English speakers handed COVID-19 vaccination cards without information on what they mean. In Pennsylvania, people who speak Mandarin, Korean, and Japanese unable to make vaccine appointments due to a lack of interpreters at hospital call centers. These are just a few of the examples captured in a new complaint filed on Friday to the U.S. Department of Health and Human Services' Office for Civil Rights, Federal Emergency Management Agency's Office of Equal Rights, and Department of Homeland Security's Office for Civil Rights and Civil Liberties. The complaint, brought by the National Health Law Program, finds widespread problems across the country that inhibit access to COVID-19 resources for people with limited English proficiency (LEP).