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| | | AI news you need to know about:- HL7 International is open for AI business. The interoperability organization, formally Health Level Seven International, announced the launch of its new AI office July 8. The department will work out of HL7 International’s headquarters in Ann Arbor, Mich., under the headship of Daniel Vreeman, who becomes its first chief AI officer. The role expands Vreeman’s current duties as chief standards development officer. He’ll continue working on collaborations with regulators and industry partners around the world but will now do so with a particular focus on AI. He’ll also oversee the HL7 International AI Challenge (which by the way remains open for submissions till July 30). In announcing the AI office and Vreeman’s leadership of it, HL7 International suggests the moves solidify the org’s presence “at the forefront of healthcare’s AI revolution, creating frameworks that ensure emerging technologies are trusted, explainable, interoperable and scalable across clinical, operational and research settings worldwide.”
- HL7 International’s CEO, Charles Jaffe, MD, PHD, adds: “Artificial intelligence will fundamentally reshape healthcare delivery, evaluation and payment. Our new AI office positions HL7 as the trusted global convener for responsible, standards-driven AI innovation—ensuring these transformative technologies deliver on their promise to improve health for all.”
- The organization says key initiatives likely to get a boost from the Vreeman-led AI office include “frameworks for AI explainability and transparency, partnerships to combat healthcare fraud through AI, and the development of implementation guides for safe AI deployment in clinical settings.”
- How do clinical AI tools affect patient outcomes? Come to think of it, can they really integrate with routine practice at all? Epidemiologist Alexander Sundermann, DrPH, ponders these questions out loud. “Without proper integration into real-world settings, even the most powerful tools can be rendered useless,” he writes in a piece published July 8 by MedPage Today. “AI holds immense promise, but, as we learned with electronic health records, implementation without guidance can create more problems than it solves.” Sundermann is an assistant professor of public health at the University of Pittsburgh. Hear him out.
- Patients recovering from surgery at home may take pics of their own incision sites and have the images remotely interpreted by AI. All that needs to happen is for the proof-of-concept research to get translated into clinical practice. A study bolstering the validity of the option was conducted at Mayo Clinic and now has been published in a scientific journal. “This work lays the foundation for AI-assisted postoperative wound care, which can transform how postoperative patients are monitored,” lead author Hala Muaddi, MD, PhD, tells Mayo’s news division. “It’s especially relevant as outpatient operations and virtual follow-ups become more common.” News item here, study report here.
- We know the U.S. Justice Department is using AI to bust healthcare crooks. What we don’t know is how. It turns out the opaqueness is by design. “We often do not share this type of information,” a spokesperson for HHS’s Office of Inspector General tells Politico, “because we are aware that fraudsters are monitoring.”
- The U.K.’s National Health Service is bobbing for air in choppy waters. And it’s looking to AI for rescue. The picture is painted in the words of U.K. Prime Minister Sir Keir Starmer. Writing in a report titled Fit for the Future: 10-Year Health Plan for England, Starmer says course corrections planned or already underway are “radical and urgent. It won’t be easy, but the prize will be worth” the trouble. The report, which is based on an investigation led by the distinguished surgeon and health policy expert Lord Ara Darzi, shows that doctor appointments are hard to get, hospital waiting lists have “ballooned” and healthcare workers are “demoralized and demotivated.” What can AI do about all of that? Or any of it, for that matter? Three things, for starters.
- The report, released July 3, says the technology will:
- Help bring “the very best of cutting-edge care to all patients. All hospitals will be fully AI-enabled within the lifetime of this plan.”
- “Liberate staff from their current burden of bureaucracy and administration, freeing up time to focus on the patient.”
Become “every nurse’s and doctor’s trusted assistant, saving them time and supporting them in decision making.” “Despite the scale of the challenge we face, there are more reasons for optimism than pessimism,” the report’s authors write. “The NHS is the best placed system in the world to harness the advances we are seeing in artificial intelligence and genomic science. … When coupled with our country’s excellence in science, innovation and academia, the U.K. can lead the world in developing the treatments and technologies of the future.”
To read the whole thing in your choice of format, click here.
- The small but mighty nation of Israel wants to give medical AI innovators a regulatory sandbox to play in. Stated another way, the country is looking to provide a supervised testing environment in which healthcare vendors can mitigate risks inherent in trying new things. Meantime the country will continue working out a comprehensive regulatory framework. Commentating in the Jerusalem Post, a fintech expert suggests healthcare learn from experience in her sector. “On the one hand, [sandboxes] allow entrepreneurs to develop and test innovative products in a supportive regulatory environment, with reduced oversight requirements during the trial phase,” writes the expert, Ruth Plato-Shinar of the Center for Banking Law and Financial Regulation at the Netanya Academic College. “At the same time, they enable regulators to assess the appropriate level and type of regulation throughout the development process—and adapt accordingly.” Regulatory sandboxes, she adds, “foster technological advancement, attract new players to the market and enhance competition.” Read the rest.
- From the ‘Too Much Happening to Sum Up Everything’ Department:
- From AIin.Healthcare’s sibling outlets:
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| | | The AI revolution in healthcare is not about technological development. It’s about how the technology will affect human relations between patients, their families and their physicians. This may be nowhere more true than in critical care, where the use of AI “presents unique challenges requiring specialized oversight,” an international panel of researchers state in a consensus paper published July 8 in the BMC journal Critical Care. The article’s first author is Maurizio Cecconi, MD, of Humanitas University in Milan, Italy. Last author is Azra Bihorac, MD, of the University of Florida. Among the statements on which they and 20 other critical-care specialists agree are these four: 1. AI integration into critical care demands coordinated efforts among clinicians, patients, industry leaders and regulators to ensure patient safety and maximize societal benefit. Without a structured approach to implementation, evaluation and control, the AI transformation “may be hindered or possibly lead to patient harm and unintended consequences,” the authors write. “Despite the need to support overwhelmed ICUs facing staff shortages, increasing case complexity and rising costs, most AI tools remain poorly validated and untested in real settings.” More: ‘To address this gap, we issue a call to action for the critical care community: the integration of AI into the ICU must follow a pragmatic, clinically informed and risk-aware framework.’
2. Standardized data collection is essential for creating generalizable and reproducible AI models and fostering interoperability between different centers and systems. A key challenge in acute and critical care is the variability in data sources. These include EHRs, multi-omics data (genomics, transcriptomics, proteomics, metabolomics), medical imaging (radiology, pathology, point-of-care ultrasound), and unstructured free-text data from clinical notes and reports. ‘These diverse data modalities are crucial for developing AI-driven decision-support tools, yet their integration is complex due to differences in structure, format and quality across healthcare institutions.’
3. In critical care, continuous evaluation and post-marketing surveillance of dynamic AI models is essential. “A major limitation in current regulation is the lack of established pathways for dynamic AI models,” Cecconi and colleagues write. “AI systems in critical care are inherently dynamic, evolving as they incorporate new real-world data, while most FDA approvals rely on static evaluation.” ‘In contrast, the EU AI Act emphasizes continuous risk assessment and post-market surveillance. This approach should be expanded globally to enable real-time auditing, validation and governance of AI-driven decision support tools in intensive care units.’
4. Physicians may increasingly use AI to support clinical decision-making, yet the core values of medical practice—human connection, empathy and the patient-physician relationship—must not be violated. ‘We call on the global critical care community to collaborate in shaping this innovative future to ensure that AI integration enhances, rather than erodes, the quality of care and patient well-being.’
The paper is posted in full for free. - In other research news:
- Regulatory green lights:
- Partnering:
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