Clinicians still cautious about AI in many areas of medicine. October 20, 2026 Paul Lukac, M.D., chief AI officer at UCLA Health, addresses attendees at AHCJ's Fall Summit on AI in health care. (Photo by Erica Tricarico/AHCJ) Artificial intelligence has been making its way into many areas of health, but experts at AHCJ's fall summit, AI in Health Care: Reporting What's Real, expressed cautious optimism, noting the continued need for human oversight of these applications. In the 1980s or around that time, if an engineer wanted to build a program to help detect breast cancer on a mammogram, they would interview expert radiologists about what they look for and then manually program methods to detect those features, said Curt Langlotz, M.D., director of the Center for Artificial Intelligence in Medicine and Imaging (AIMI Center) at Stanford University, in the opening panel discussion. "What really changed, and this was back in about 2012, is that we started to realize that these neural networks could learn from the data itself. We no longer had to talk to the expert," instead asking experts to label what is and isn't cancer on imaging scans, and feeding the data to a program that "learned" from these examples. The next transition, he said, happened around 2022, when ChatGPT (a form of self-supervision) came out that can train itself on large datasets. "We're now at a very different place, and it's a very exciting place for medicine," Langlotz said. The most common use of AI has been to summarize information such as in a chart or from a doctor's visit, said Paul Lukac, M.D., the chief AI officer at UCLA Health. Many medical centers use AI to help with diagnostics and prevention, searching electronic health records for changes in patient vital signs and symptoms so they can intervene, or helping predict health outcomes. Lily Lai, M.D., a surgeon with City of Hope, a comprehensive cancer center near Los Angeles, discussed a risk calculator she is using to predict postoperative complications for cancer patients before they head to the operating room. After implementing this machine learning model, she and her colleagues observed a 7% drop in complication rates among high-risk patients. Other panelists discussed tools that monitor for life-threatening sepsis infections and ensure that patients found to have lung nodules during other medical workups get needed follow-up care. AI also can be helpful in disaster relief. Climate disasters are becoming more intense and geographically widespread in the U.S. and worldwide, said Amir Behzadan, Ph.D., a professor of civil, environmental and architectural engineering at the University of Colorado Boulder. Despite this, many communities still lack detailed hazard mitigation plans from the Federal Emergency Management Agency (FEMA). "AI can offer a very low-hanging opportunity to help close this gap by circulating research, organizing community hazard data, identifying potential mitigation strategies, drafting plan components and helping communities move more efficiently toward a complete, locally informed plan," he said, with the idea of giving resource-strained communities a "powerful tool to do more with the resources they have." His lab aims to tap into the power of AI, coupled with community engagement, to design human-centered solutions for measuring, mapping and communicating the impact of disasters. AI can play a significant role in rebuilding trust in science by making complex information more accessible, understandable and transparent for communities, said Behzadan. Some of his team's efforts included using AI to estimate building damage and using visual analytics to better understand how disease transmission can occur in complex, densely populated areas. AI models are also being tested to predict the spread of wildfires. But for all of its promise, speakers said, there is still need for caution and for ensuring human experts have the final say in whether to adopt suggestions from AI. The adoption of AI and other technologies can result in harms for workers, said Mishal Khan, Ph.D., a senior researcher with the Technology and Work Program at UC Berkeley Labor Center. This can include deskilling, or a reduction in the level of skill needed to complete a job. "This is one of the key concerns, especially for nurses," Khan said, noting a worry that algorithms are taking over decision-making. With increasing remote monitoring of patients and automated handoffs, "health care workers are really concerned about what this is doing to the quality of in-person care and their connection with their patients." And these systems are not necessarily offering time savings, she added, in some cases adding time for humans to correct issues caused by technology. Other concerns include liability (who is responsible if a patient is harmed by an algorithmic decision), alert fatigue (becoming immune to the pinging and beeping of system alerts), and monitoring and surveillance (how data will be used in employment or discipline decisions). Laws are emerging to address these issues, Khan said. For example, California Gov. Gavin Newsom in September signed legislation including state bill 951, which in part prohibits employers from making employment decisions about workers without a human in the loop. AB 1883 and AB 1331 ban workplace surveillance tools that read workers' emotions and monitor them in bathrooms. Legislation in other states such as Oregon and Washington state make it illegal to advertise a chatbot as a licensed health care professional, she said. Attendees also heard updates in areas like AI chatbot technology, how AI is changing drug research and development, the evolving role of AI in medical education, and efforts to ensure rural and safety-net hospitals don't fall behind. In an interactive exercise, Graham Walker, M.D., co-founder of MDCalc, an online medical reference tool, walked attendees through the following scenario: A woman presents to the emergency department with "the worst headache of her life." An initial CT scan, with an accuracy of 98.7%, tests negative for a brain bleed, but an AI radiology system disagrees, flagging possible bleeding. Attendees worked in small groups and used ChatGPT to help decide what to do next. As an illustration of just how variable AI can be, depending on how people prompted the system, the answers included proceeding with additional testing, noting it was OK to discharge the patient without further testing, and asking the doctor in charge. Attendees also participated in a field trip to Cedars-Sinai Medical Center to learn more about topics such as AI ethics, sudden cardiac arrest prevention, and AI in robotic surgery.

4 ways to become an Innovative healthcare marketer. A lot of times, Greystone.Net, Inc. associate the word "innovative" with technology and/or doing something that nobody else has done before. But Linda Ho, who spent twenty-eight years at UCLA Health and two years at UCSF Health, has proven that innovation is often about seeing an old problem more clearly than everyone else. This year, she was nominated into The Healthcare Internet Hall of Fame (HIHOF) as an Innovative Individual. This award honors people who have been in the industry for at least ten years, served as leaders and brought significant change and contributions to the industry. "Linda is the consummate professional who brings structure, smarts and kindness to any role she is in," says Ahava Leibtag, Founder and CEO of Ahava Media and chair of HIHOF. "She always looks to uplevel talent and grow the future skills necessary for a marketing technologist in healthcare marketing. Anyone who has worked with her can see the magic she inspires." Ho, the Director, Marketing Technology, Operations & Analytics at UCSF Health, hasn't needed a bigger budget or a brand-new tool to make her mark. She asks questions no one else bothers to ask (like, "After a new mom gets discharged from the hospital, why doesn't she hear from her care team again?"), studies the budget cycle carefully and directly links her marketing efforts to hospital revenue. Tanya Andreadis, a former CMO at UCLA Health, and now SVP and Chief Marketing and Digital Officer at University of Miami, says that Ho's recognition is well-deserved. "Linda led all of marketing technology on my team at UCLA Health, and she was always a problem solver who would keep working until she found the answer," Andreadis says. "She sees the problem clearly, builds the solution and shows the results." For the next generation of healthcare marketers, Ho shared a few tips on embracing an innovative mindset. Show and tell how you're spending your budget Ho says it's important to show how your marketing dollars are spent. If you don't, you won't be able to prove why you need more capital dollars to support new patient volume and service lines. At UCLA Health, she and her team built a live Tableau dashboard tracking new patient volume by source: website traffic, campaign clicks and QR codes scanned at Dodgers games. The dashboard was consistently updated and C-suite leaders could look at it at any time. The dashboard tied everything back to revenue. "For example, with the dashboard, it made it easier for our marketing team to say, 'We brought in $45 million in new patient revenue,'" Ho says. "Many times, people don't know all the work your team is doing and this dashboard was just a way to show how our work is bringing revenue into the hospital." Another budgeting tip: Pay attention to the budget cycle. She says her biggest marketing projects didn't start with dedicated funding, but with leftover money. At UCLA Health, the fiscal year ran from July through June. She paid attention to final few months, to see what money was left unspent. "You need to be in tune with your budget, year-over-year, and figure out the best time to get extra money for testing, launching a pilot or trying something new," Ho says. Let the data make your case for more funding A pilot turns into a bigger program if you can prove it worked. "How do you know that your website did well or your CRM campaign did well?" Ho says. "It always has to go back to what we measured and how did the data prove it." For example, at UCLA Health, she created a maternity-focused marketing automation program after winning internal seed funding through a Shark Tank-style employee innovation challenge contest. She used existing clinical data (discharge status, delivery type, breastfeeding plans) to trigger personalized follow-up content automatically. For example, a healthy mom and baby discharged the next day would get a congratulations message paired with a list of four nearby UCLA Health pediatricians (each with an online scheduling link). Moms got messages on how to feed their baby. C-section patients got different recovery guidance than those who delivered vaginally. "The campaign was effective because I could tie every touchpoint back to measurable engagement and show how many new moms transitioned to establish primary care with UCLA Health from the messages," Ho says. Fix problems nobody else thinks about When she arrived at UCSF Health, the provider directory was updated manually. New doctors were waiting up to three months to appear online. She'd already automated the same process at UCLA Health more than a decade earlier, so she wanted to check it off the list immediately. "I introduced automation, like pulling data on a regular feed from a source of truth, which automates out on a new doctor," Ho says. "If they're starting tomorrow, they're on the website tomorrow." Recently, her UCSF Health team used AI to figure out what conditions a specialist treats, without relying on doctors to self-report. By analyzing the most recent year of de-identified clinical notes against a standardized medical language system, her team can determine that a neurosurgeon treats pituitary tumors 90% of the time versus another condition 60% of the time. Then, they can put those on the provider's web profile. "Patients are searching on ChatGPT, Claude and Perplexity," Ho says. "A patient is going to search, 'pituitary tumors' and you want to make sure the right doctor is flagged for that expertise." Let patients point you toward your next idea Ho says she's most inspired by hearing patient perspectives. At UCLA Health, they had a PFAC (Patient Family Advisory Council), where she'd frequently ask the group questions about a new app they were going to roll out or pain points in their MyChart account. At UCLA Health, she helped build a consolidated feedback form for C-suite leaders to look at. It pulled in patient feedback from the website, Yelp, Google and social media. Every night, the report would go out to the C-suite. "When I review patient feedback, I always think, 'Do we have a solution to solve their problem?'" Ho says. "But you can't hear about these problems, if you aren't asking." Pattie Cuen, a former CMO at UCLA Health, and now the SVP at Jarrard, Inc., says that what makes Ho stand out is the empathy she brings to every project she works on. "She understand patients not as customers, but as people who are navigating vulnerable and difficult moments in their lives," Cuen says. "This is at the core of Linda's values as a person, colleague and leader. Linda's empathy drives her to find solutions that make the patient journey compassionate, connected and human."