NYC Health + Hospitals CEO Signals Willingness to Replace Radiologists with AI
A top public hospital CEO says AI could soon take over radiology functions to cut costs—raising similar automation questions for clinical labs—though critics warn the technology is not ready to replace physicians.
The CEO of NYC Health + Hospitals says his system is prepared to begin replacing radiologists with artificial intelligence (AI) in certain use cases, once regulatory barriers are addressed. From a clinical lab professional perspective, health systems are actively evaluating where AI can reduce reliance on highly trained specialists while maintaining diagnostic throughput.
According to an article from ZeroHedge, Mitchell H. Katz, MD, president and CEO of NYC Health + Hospitals recently spoke on panel hosted by Crain’s New York Business, he said AI is already capable of interpreting imaging studies such as mammograms and X-rays—creating an opportunity to lower labor costs amid rising demand for diagnostic services.
“We could replace a great deal of radiologists with AI at this moment, if we are ready to do the regulatory challenge,” Katz said.
AI as a Cost and Workflow Strategy
Katz noted that AI could expand access to screening—particularly in breast cancer—while lowering operational costs. One proposed model would shift radiologists into a secondary review role, validating only abnormal findings flagged by AI.
For clinical laboratories, this mirrors ongoing discussions around digital pathology, AI-assisted test interpretation, and automated workflows in areas such as hematology, microbiology, and molecular diagnostics. If imaging adopts a “AI-first, specialist-second” model, similar expectations could follow in the lab.
This approach could deliver what Katz described as “major savings,” particularly for large systems facing staffing shortages and increasing test volumes.
Other hospital leaders are already moving in this direction. David Lubarsky, MD, MBA, CEO of Westchester Medical Center Health Network, said his organization has seen strong performance from AI-assisted mammography interpretation.
“For women who aren’t considered high risk, if the test comes back negative, it’s wrong only about 3 times out of 10,000,” Lubarsky said, adding that the technology is “actually better than human beings.” (Photo credit: Westchester Medical Center Health Network)
Katz also questioned whether regulations should evolve to allow AI to interpret imaging independently—potentially establishing a precedent that could influence how regulators approach AI in laboratory medicine.
Why Clinical Labs Should Pay Attention
While the discussion centers on radiology, the underlying drivers—cost containment, workforce shortages, and demand for faster turnaround times—are identical pressures facing clinical laboratories.
If regulators permit AI to operate with reduced physician oversight in imaging, labs could see accelerated adoption of AI-driven decision support, automated result interpretation, and even reduced hands-on review in certain testing workflows.
At the same time, the debate highlights a key risk of balancing efficiency gains with diagnostic accuracy and patient safety.
Pushback Raises Safety Concerns
Not all healthcare professionals agree with the direction. Some radiologists warn that current AI tools are not ready for independent clinical use.
“Undeniable proof that confidently uninformed hospital administrators are a danger to patients: easily duped by AI companies that are nowhere near capable of providing patient care,” said Mohammed Suhail, MD, of North Coast Imaging.
“Any attempt to implement AI-only reads would immediately result in patient harm and death, and only someone with zero understanding of radiology would say something so naive.”
The debate signals what may be ahead for the broader diagnostics industry. As health systems test AI-driven models in radiology, clinical laboratories may soon face similar expectations to leverage automation for cost savings—while defending the continued role of expert oversight in ensuring quality and patient safety.
This article was created with the assistance of Generative AI and has undergone editorial review before publishing.
—Janette Wider
