| | | AI news you ought to know about: - If nurses fail to lead the way toward safe and responsible use of healthcare AI, the profession may lose its voice on emerging technologies. The warning is implied by Patricia McGaffigan, RN, MS, vice president of safety at the Institute for Healthcare Improvement. McGaffigan says she’s flash-surveyed hundreds of nurses and nurse leaders at recent presentations only to find sluggish uptake of the lead-or-follow principle. Very few attendees “have been involved in any way in the meaningful governance or engagement or user-centered approaches to considering and bringing generative AI into their worlds,” McGaffigan tells Healthcare Innovation. “And that, in and of itself, is a major reason why there will always be resistance to new ideas, whether they’re technology-based or not.” The outlet interviewed McGaffigan together with Kate Feske-Kirby, an IHI research associate focused on innovation. Here are some nuggets the two share in the July 7 transcript.
- McGaffigan: “We have compelling reasons to state that nurses should be meaningfully involved from stem to stern in [the AI governance] process. The way generative AI applications are developed might be coming from external parties and developers. There’s a lot of physician-led work in this space, and I’m really hopeful we will see more bright spots of opportunity for AI applications that will benefit nurses, such as event listening to help with documentation or scheduling—capabilities that make life for a charge nurse much less complex.”
- Feske-Kirby: “We want to make sure that nurses are equipped and feel comfortable using new technology, that they can apply it safely and provide quality care. But there’s also the question of making sure that they remain skilled without the technology should anything happen. Technical glitches happen. We’ve had lots of adverse weather conditions that can really impact a hospital’s ability to sustain technology that uses a lot of energy.”
- McGaffigan: “I get super-excited to hear about how nursing education is changing and evolving to be able to keep up with and hopefully lead and get ahead of technology changes. The inclusion of AI and other technology use into foundational pre-licensure education for all healthcare professionals is super-critical right now.”
- A funny thing happened to the One Big Beautiful Bill Act on its way through the Senate. Senators voted 99 to 0 to ice a provision that would have forced the 50 states to “pause” state-level AI regulations. And that’s how the bill stood, ban-free, when President Trump signed it into law on the Fourth of July. Commenting on the development, legal thinkers at Covington & Burling point out that Congress could still consider similar legislation via “another vehicle” later this year. “While the strong bipartisan resistance to the moratorium casts doubt on future attempts to pause state AI regulations, at least one member, House Energy and Commerce Committee Chair Brett Guthrie (R-KY) has pledged to keep trying,” the attorneys note. They quote Guthrie as having said, “We’re still gonna work it, and hopefully we’re gonna have to have state preemption in the end.”
- Here’s one of the first peer-reviewed case studies on responsible AI governance at scale in, specifically, oncology. It’s from Memorial Sloan Kettering Cancer Center. NPJ Digital Medicine published it July 4. “Improving our AI governance process is an ongoing learning endeavor,” write CHIO Peter Stetson, MD, and colleagues. “We are revisiting AI governance committee ‘decision rights’ and any revisions needed to our charter. One of our central concerns is balancing the two sides of our main AIGC guiding principle—‘promoting use’ and ‘responsible use.’” The authors expound on that point in some detail in their discussion section and share much more to boot. And the study is posted in full for free.
- Also looking at AI in the context of cancer care is the editor-in-chief of Oncology. After briskly surveying the landscape of opportunities and challenges before the specialty, Julie Vose, MD, MBA, comments in a personal note to readers that there’s “a lot of excitement about all the applications for AI in diagnosis, treatment and clinical trial matching to improve the outcome for our patients with malignancies. We have only started to scratch the surface of what is possible.” The full issue is posted in ezine format.
- When interacting socially, mouse brains and AI systems develop ‘remarkably similar’ neural patterns. Researchers at UCLA’s Brain Research Institute unveil the parallels in a study published July 2 in Nature. “Our findings suggest that shared neural dynamics represent a fundamental and generalizable feature of interacting neural systems present in both biological and artificial agents,” Weizhe Hong, PhD, and co-authors explain. In coverage by UCLA’s news operation, Hong adds: “This discovery fundamentally changes how we think about social behavior across all intelligent systems. … The implications are significant for both understanding human social disorders and developing AI that can truly understand and engage in social interactions.”
- He was trained in AI before it was cool. And now Avner Schlessinger, PhD, directs the action at the AI Small Molecule Drug Discovery Center of Icahn School of Medicine at Mount Sinai in New York City. The center only opened in April, but already it’s designing novel drug-like molecules using generative AI, optimizing existing compounds to enhance their efficacy and safety, and predicting drug-target interactions to repurpose known drugs or natural products for new indications, reports Genetic Engineering & Biotechnology News. Thanks to his early AI education and much that came after, Schlessinger finds the present moment represents “particularly good timing to use Mount Sinai’s datasets and experts to improve our models for real solutions.” The news outlet spoke with Schlessinger and others for an article on “democratizing” AI for preclinical drug discovery. Read it here.
- Healthcare AI is cool for cats too. “By observing cats continuously in their natural, stress-free environment, new technologies are surfacing subtle early changes that humans alone”—including veterinarians—“often miss.” The insight is offered at DVM 360, where a vet and co-writer note that AI doesn’t force cats to communicate. Instead, it “interpret[s] what cats are already expressing through behavior.”
- It looks kind of right but also very wrong. That’s how one would-be enthusiast of AI filmmaking describes a simulated video of an actual wedding. The wedding was the enthusiast’s own. Google Gemini’s Veo was the videographer who recreated the memory 34 years after the fact. All it had to go on was some still photography showing the happy bride and groom with a few cheering wedding guests. That proved too tall an order for the AI. “Whatever the image starts with, everything after that first millisecond is false—or worse, it’s memory corruption,” TechRadar writer Lance Ulanoff learned the hard way. “In the case of my wedding memories, I realize they’re better left to the gray-matter movie projector in my head.”
- From AIin.Healthcare’s siblings:
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| | | Primary care providers are excited about the promise AI holds to help them help patients living with multiple chronic conditions. However, they believe these patients will continue to need the human connection they get from their PCPs. That’s according to an interview-based study conducted at the University of Birmingham in the United Kingdom. Dr. Jennifer Cooper and colleagues spoke with not only general practitioners but also geriatricians, nurses and pharmacists. Their study report went up July 4 in the Journal of Medical Internet Research. “Our findings are important to software developers, healthcare providers and policy makers in navigating the development and regulation of AI tools for managing multiple long-term conditions,” the authors write. “There is currently limited guidance on how AI tools should be safely integrated into clinical practice, and clarity on the medicolegal implications for clinicians of using these tools is urgently needed.” Here’s more from the paper. 1. Patients with multiple long-term conditions present complex care needs. These include difficulties prioritizing multiple conflicting needs, managing multiple medications and navigating care outside single-condition guidelines. More: These challenges highlight the need for AI tools that are not only technically robust in dealing with multiple factors but also user-centered and responsive to real-world complexity.
2. The potential for AI tools to change the clinician-patient relationship is a universal concern among primary care providers. These healthcare professionals commonly worry about the potential AI tools have to shave minutes off of doctor-patient facetime during office visits, Cooper and colleagues suggest. Another common concern among PCPs is how they would cope with nuance or gray areas inherent in supporting people with complex medical and social circumstances.
3. Patients’ perspectives are especially important to PCPs’ confidence in using AI during office visits. The minimum requirements for AI tools to effectively support clinicians and patients in managing multiple long-term conditions are, the authors report, as follows: - Integration with existing EHR systems to promote time efficiency and reduce workflow burden;
- Gradual introduction to support clinician and patient trust;
- Transparency in the rationale for recommendations that accommodates the complexity of polypharmacy and comorbidity management to support clinicians in adjusting treatment plans beyond single-disease guidelines;
- Preservation of clinician autonomy, allowing for adaptation to individual patient needs;
- Presentation in a patient-friendly format that supports shared decision-making;
- Design for accessibility across diverse patient and practice contexts to avoid exacerbating digital inequalities; and
- Rigorous medicolegal regulation and real-world testing.
Cooper and co-authors believe their study is the first to examine the attitudes of primary care providers toward the use of AI decision-making tools in the context of managing multiple long-term conditions. These clinicians are “optimistic about AI’s potential to improve decision-making safety and quality,” the authors write, but they remain convinced that “the human touch remains essential for patients with complex needs.” Read the full study. - In other research news:
- Funding:
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