Artificial intelligence (AI) is already changing clinical care, but health systems should consider how it affects clinicians' daily work, not just what the tools can do, experts said during a panel discussion at Cleveland Clinic's AI Summit for Healthcare Professionals. 

The discussion was moderated by Sarah Hatchett, MBA, senior vice president and chief information officer at Cleveland Clinic. She asked panelists about the clinical value of AI, how to prepare the workforce to use it, and lessons learned from putting AI into practice. 

AI and the clinical relationship

Hatchett first asked Erin Losey, RN, a nursing informatics specialist at Cleveland Clinic, how AI could improve care without weakening the human connection between nurses and patients. 

“So much of our conversation, especially around nursing, is how do we relieve admin burden and how do we create efficiencies,” Losey said. Less attention has been paid to “how do we strengthen that clinical relationship between the patient and the nurse.” 

Losey described an AI-supported handoff tool used at some Cleveland Clinic sites to highlight important personal information that could otherwise be missed when patients transition between care team members. In one example, a patient spoke English well but preferred to receive important medical information in Spanish with an interpreter present. Highlighting that preference meant the patient did not have to ask for an interpreter each time. “Handoff can sometimes become a game of telephone, and information that's important to them can get lost,” Losey said. 

From medication safety to treatment response

Hatchett then asked Scott Nelson, PharmD, MS, of Vanderbilt University Medical Center in Nashville, about where he sees the greatest potential for AI to improve medication safety, efficiency, or pharmacists' work. Dr. Nelson, of the university's department of biomedical informatics, said AI could help in areas ranging from drug discovery to clinical decision-making. It could use more patient-specific information to guide medication decisions, improve safety, and reduce unnecessary alerts. AI could also help educate patients and improve communication among clinical teams. 

“How do we improve the efficiency of our systems” while getting medications to patients “in a safe and effective way?” Dr. Nelson asked. AI-supported clinical decision tools could “bring an additional patient context that can promote safety for the patient.” 

Turning to Benjamin Kann, MD, PhD, a radiation oncologist at Dana-Farber Cancer Institute and Brigham and Women's Hospital in Boston, Hatchett asked where AI is already improving care. Dr. Kann pointed to radiation oncology as one example. AI can automatically outline organs and tumors on medical images, reducing the time clinicians spend doing this work manually. “We have tools that we are using on a daily basis that are really helping with our bandwidth,” he said. 

AI may also help physicians assess tumor response in greater detail. Traditionally, he noted, response has often been measured in a single imaging plane because more detailed analysis was too time-consuming. AI can now identify tumor boundaries, calculate tumor volume, and track changes in size and location over time. 

“We can really look at granular changes and where they're happening spatially too and learn much more about how patients are responding to treatments,” Dr. Kann said. He added that this could have implications for clinical trials as well as treatment decisions in practice. 

But automation may come with a trade-off. Radiation oncology trainees once spent hours learning to outline normal structures by hand. AI can now do much of that work, leaving clinicians to review the results. Dr. Kann raised the “fear of de-skilling” as clinicians get less hands-on practice and said training may need to change as AI becomes more common. 

Preparing the workforce for AI

Hatchett followed up on that issue by asking Travis Zack, MD, PhD, chief medical officer at OpenEvidence, how health systems should begin preparing their workforce to use AI. 

Clinicians need “fundamental understandings of what an AI model is” before focusing on individual tools, Dr. Zack said. That includes understanding how models are built and learning to recognize “failure modes,” accuracy problems, and data set shifts. Such knowledge can help clinicians understand effective uses of AI and “the potential pitfalls that you have to be very careful of when you start.” 

Lisa Christensen, global head of learning design and innovation at McKinsey & Company, said AI education should include hands-on experience. “We need to get people experimenting and using AI,” she said. Some health care workers are eager to use AI, she added, while others remain hesitant. Encouraging staff to start with small steps can help them understand how AI may change their work and which human skills will remain essential. 

Avoiding unintentional consequences

Hatchett then asked a question from a conference attendee: What mistakes have panelists seen when teaching people about AI or putting the technology into practice? 

Losey emphasized the importance of starting on the right foot. Nurses need clear information about how AI tools are developed and used, she said, especially because some nurses already have concerns about AI use in health care. “We only get one shot with technology to make a first impression,” Losey said. “We also have to build trust with nurses.” 

Dr. Zack said another lesson is to create ways for users to provide feedback from the start. “No system is perfect,” including AI, he said. Organizations should learn from errors and negative feedback to improve AI systems over time. Without that feedback, the systems can become “stagnant and stuck.” 

Dr. Nelson described another unintended effect: AI may shift work from one person or team to another rather than reduce the overall workload. He cited a radiology practice where AI identified normal chest X-rays and sent abnormal studies to radiologists for review. The system improved triage, but it also meant radiologists were seeing one difficult case after another instead of a mix of routine and complex cases. As a result, they read fewer studies per hour, prompting questions from leadership about why productivity had fallen despite AI assistance. 

“Are we actually easing the burden on our clinicians or are we just expecting them to do more with more complicated edge cases?” Dr. Nelson asked. 

The approach could also affect training, Dr. Nelson said. Residents who rarely see normal studies may have a harder time learning to recognize what is abnormal. Similar concerns could arise if AI handles routine medication orders, leaving pharmacists to review mostly complex or unusual ones. 

Dr. Kann highlighted another risk: placing too much confidence in an AI result. Even highly accurate AI models encounter cases where experts disagree. Yet clinicians may see an AI result and think, “It knows what it's talking about,” even when the case itself is uncertain. 

Regular use of AI could also make clinicians less careful when reviewing its work, he said. “The need to be extra vigilant when you're reviewing something versus doing it from scratch can be a real problem,” Dr. Kann said.