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| | | News and views you ought to know about:- Just because it’s efficient doesn’t mean it’s effective. Nowhere is this thought more deserving of reflection than in healthcare AI—especially when it’s applied to clinical decision support. If uncarefully deployed, the technology can quickly take a clinical user down a dangerous path. Accordingly, the central challenge in integrating novel algorithms into healthcare workflows is—or should be—bias mitigation. That’s the position of Los Angeles Times writer Kevin Famuyiro. “If the training data does not reflect our diverse patient populations, we are not just failing to help; we are actively encoding and amplifying health disparities,” writes Famuyiro, whose formal job title is senior content strategist. “We are making care worse for some groups.” His primary point is that clinicians must remain at the center of care. “These are tools,” he stresses, “not replacements for human expertise.”
- The reminder is urgent, Famuyiro suggests, because healthcare is “racing ahead” with AI. “[T]he progress is undeniable,” and it’s encouraging to see the FDA reviewing so many AI-equipped devices before they’re sent into real-world care settings, he writes. “But for the doctors and nurses on the floor, these tools just have to be helpful. Not another box to click or an alert to ignore.” Hear him out.
- ChatGPT, make room for ChatEHR. A healthcare-specific LLM conceived and birthed at the Stanford Institute for Human-Centered AI, aka “HAI,” ChatEHR gives a broad base from which specific clinical applications can be designed and deployed, Stanford’s news operation explains. By combining secure access to patient data, LLMs and deep integration with clinical workflows, the platform “represents a significant step forward in bringing AI capabilities to healthcare delivery,” the team reports. The developers are now expanding ChatEHR to standardize integrations of vendor AI products within Stanford Health Care. Meanwhile clinicians using the ChatEHR user interface are “already envisioning new possibilities, proposing innovative workflow automations that were previously impossible to implement.” Learn more here.
- First she wanted to do something just because it was hard to do. But as she got a little older, she found herself wanting to do something that mattered. She wound up in healthcare AI. Who is she? Suchi Saria, PhD. Holder of several big titles at Johns Hopkins, including director of the institution’s machine learning & healthcare AI lab, Saria shares her story in Johns Hopkins Magazine. “I grew up in India in a tiny town,” says Saria, who is also founder and CEO of Bayesian Health, a successful company based in New York City and Baltimore. “My parents dealt in the tea industry, and my family has done this for generations: making tea, exporting tea, researching new types of tea.” Among her achievements with AI is an early-warning system that has cut sepsis rates across the U.S. by almost 20%. “It’s a privilege to be able to bring life-saving innovations to the bedside and help hospitals become a safer, more hospitable environment, both for patients and clinicians,” Saria says in the Q&A. Get the rest straight from her.
- The key to getting useful medical information from an AI chatbot is to ask specific, well-framed questions, providing as much context as possible. So advises Washington Post contributing healthcare columnist Leana Wen. “The more context you provide, the better the response will be,” Wen clarifies. “If the initial response isn’t what you were looking for, keep asking. Reformulate your question and engage in a dialogue, as you would with a human clinician.”
- Military veterans are benefiting by AI that helps colonoscopy operators work to the top of their license. The AI in use at the VA is “very precise. It learns, and in learning, it equalizes the playing field for all of us,” explains Deputy Chief of Staff Douglas Huntley, MD, in a VA publication. “It’s vital that we do anything that we can to reduce the morbidity and mortality for our Veterans. If there is a technology that is safe, and that we find is valuable for the care of the patients, we are going to use it.” Story here.
- Also worth your while:
- From AIin.Healthcare’s sibling news outlets:
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| | | Nabla launches Nabla Connect: The fastest way for any EHR to deliver world-class ambient AI
Nabla Connect is a new plug-and-play module that lets EHR vendors embed Nabla’s trusted AI assistant directly into their platform, making integration fast, compliant, and effortless.
In just a few days, EHRs can: - Integrate ambient AI without heavy engineering
- Enhance clinician experience with seamless, accurate note generation
- Stay future-proof through continuous updates
Learn more about Nabla Connect
Trustworthy clinical AI is built on accurate transcription
If the AI notes can’t be trusted, clinicians retype and workflows stall. Speechmatics’ new Medical Model achieves 93% overall accuracy and 17% fewer word errors vs the next best provider. It preserves clinical terms with 96% recall, separates speakers in real time, and captures drug names, ICD-10-CM and procedure codes with timestamps. That foundation turns ambient notes into records teams can rely on from intake to audit. Read more. - Don’t Let AI Touch Patient Data
Shadow AI is sneaking into hospitals and clinics faster than IT Teams can track. Download the free Security Checklist from Fellow to uncover where AI notetakers may be recording or storing sensitive conversations. Use it to flag compliance risks early and keep your organization on the safe side of privacy laws. Before Shadow AI spreads inside your team, download the checklist to reduce your risk here.
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