The study finds that a "delegation" strategy - where AI helps triage low-risk mammograms and flags higher-risk cases for closer inspection by human radiologists - could reduce screening costs by as much as 30% without compromising patient safety.
The findings could help shape how hospitals and clinics integrate AI into their diagnostic workflows amid a growing demand for early breast cancer detection and a shortage of radiologists, said Mehmet Eren Ahsen, a professor of business administration and Deloitte Scholar at Illinois.
"We often hear the question: Can AI replace this or that profession?" Ahsen said. "In this case, our research shows that the answer is 'Not exactly, but it can certainly help.' We found that the real value of AI comes not from replacing humans, but from helping them via strategic task-sharing."
The study, which was published by the journal Nature Communications, was co-written by Mehmet U. S. Ayvaci and Radha Mookerjee of the University of Texas at Dallas; and Gustavo Stolovitzky of the NYU Grossman School of Medicine and NYU Langone Health.
The researchers developed a decision model to compare three decision-making strategies in breast cancer screening: an expert-alone strategy - the current clinical norm in which radiologists read every mammogram; an automation strategy, in which AI assessed all mammograms without human oversight; and a delegation strategy, in which AI performed an initial screening and referred ambiguous or high-risk cases to radiologists.
The model accounted for a wide range of costs, including implementation, radiologist time, follow-up procedures and potential litigation. It evaluated outcomes using real-world data from a global AI crowdsourcing challenge for mammography, which was sponsored as part of the White House Office of Science and Technology Policy's Cancer Moonshot initiative of 2016-17.
The researchers found that the delegation model outperformed both the full automation and the expert-alone approaches, yielding up to 30.1% in cost savings, according to the paper.
While the idea of fully automating radiological tasks may seem appealing from an efficiency standpoint, the study cautions that current AI systems still fall short of replacing human judgment in complex or borderline cases.
"AI is excellent at identifying low-risk mammograms that are relatively straightforward and easy to interpret," said Ahsen, also the Health Innovation Professor at the Carle Illinois College of Medicine. "But for high-risk or ambiguous cases, radiologists still outperform AI. The delegation strategy leverages this strength: AI streamlines the workload, and humans focus on the toughest cases."
With nearly 40 million mammograms performed annually in the U.S. alone, breast cancer screening is a critical public health tool. Yet the process is time-intensive and costly, in both labor and follow-up procedures triggered by false positives. And when cancers are missed, the resulting false negatives can lead to significant harm for patients and health care providers, Ahsen said.
"One of the issues in mammography is, because of the sheer number of screenings performed, that it generates so many false positives and false negatives," Ahsen said. "If you have a 10% false positive rate out of 40 million mammograms per year, that’s four million women who are being recalled to the hospital for more appointments, screenings and tests, and potentially biopsies."
That whole process only increases stress and anxiety for the patient, Ahsen said.
"It's a nightmare scenario," he said. "Follow-up appointments often take weeks, leaving patients with a black cloud hanging over their heads. It’s a very stressful time for them."
With AI and the delegation model, it’s possible that health care providers could streamline the process.
"You get screened, AI sees something it doesn't like and immediately flags you for follow-up, all while you're still at the hospital," Ahsen said. "It has the potential to be that much more efficient of a workflow."
The research also raises broader questions about how AI should be implemented and regulated in medicine.
"The delegation strategy works best when breast cancer prevalence is either low or moderate," Ahsen said. "In high-prevalence populations, a greater reliance on human experts may still be warranted. But an AI-heavy strategy also might work well in situations where there aren't a lot of radiologists - in developing countries, for example."
Another potential landmine involves legal liability. If AI systems are held to stricter liability standards than human clinicians, then "health care organizations may shy away from automation strategies involving AI, even when they are cost-effective," Ahsen said.
The findings are potentially applicable to other areas of medicine such as pathology and dermatology, where diagnostic accuracy is critical, but AI is potentially able to improve workflow efficiency.
With the infinite work capacity of AI, "we can use it 24/7, and it doesn’t need to take a coffee break," Ahsen said. "AI is only going to continue to make inroads into health care, and our framework can guide hospitals, insurers, policymakers and health care practitioners in making evidence-based decisions about AI integration.
"We're not just interrogating what AI can do - we're asking if it should do it, and when, how and under what conditions it should be deployed as a tool to help humans."
Ahsen ME, Ayvaci MUS, Mookerjee R, Stolovitzky G.
Economics of AI and human task sharing for decision making in screening mammography.
Nat Commun. 2025 Mar 7;16(1):2289. doi: 10.1038/s41467-025-57409-1