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.
“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.
“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.
A new analysis shows why models fall short in practice, how liability and equity issues slow adoption, and what lab leaders should consider as AI becomes a growing part of diagnostic workflows.
Artificial intelligence (AI) has made notable advances in medical imaging, but radiologists are not being displaced. For laboratory and diagnostic leaders, a recent analysis in Works in Progress highlights why AI has not replaced human expertise in radiology—and what this means for managing technology adoption in labs and hospitals.
In 2016, AI pioneer Geoffrey Hinton declared that “people should stop training radiologists now.” Since then, more than 700 FDA-cleared radiology AI models have entered the market, covering everything from stroke detection to lung cancer screening.
Companies such as Annalise.ai, Lunit, Aidoc, and Qure.ai offer tools that can identify dozens of diseases across modalities, reorder worklists, or generate structured draft reports. “On paper, radiology looks like the perfect target for automation,” the article noted, citing its reliance on digital images, pattern recognition, and quantitative benchmarks. Yet demand for radiologists has never been higher. In 2025, US residency programs offered a record 1,208 positions, and vacancy rates remain high as well.
Why Hasn’t AI Taken Over?
For leaders overseeing diagnostic services, three key elements are why AI has not replaced radiologists.
First, models struggle in real-world deployment. “Performance can drop by as much as 20 percentage points” when systems trained on narrow datasets are applied across different scanners, imaging protocols, or patient populations, the article explained. What works in a benchmark test may falter in a hospital with diverse workflows.
Second, liability and regulatory hurdles remain high. Assistive models that require physician review face fewer barriers, but autonomous systems must self-abort on poor image quality, identify unfamiliar equipment, and withstand rigorous scrutiny. Insurers have also drawn hard lines: one malpractice policy states that “coverage applies solely to interpretations reviewed and authenticated by a licensed physician; no indemnity is afforded for diagnoses generated autonomously by software.” Another bluntly imposes an “Absolute AI Exclusion.” For labs, this underscores the importance of risk management before deploying AI tools.
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Third, radiologists do much more than read scans. “Human radiologists spend a minority of their time on diagnostics and the majority on other activities, like talking to patients and fellow clinicians,” the commentary pointed out. Oversight of imaging protocols, interdisciplinary consultations, and patient communication all fall outside the reach of algorithms. Even as AI improves, demand for imaging may increase rather than decrease—a version of the Jevons paradox where greater efficiency leads to higher use. “The better the machines, the busier radiologists have become,” the article observed.
For laboratory leaders, the takeaway is not to fear replacement but to prepare for integration. AI tools are proving valuable in triaging urgent cases, flagging abnormalities, and drafting reports, but they remain narrow in scope—stroke, lung cancer, and breast lesions account for about 60% of models, yet represent only a fraction of total imaging work. As the article concluded, “Models can lift productivity, but their implementation depends on behavior, institutions and incentives.”
The challenge for labs is to create environments where AI augments human expertise rather than attempts to replace it. That means aligning technology adoption with clinical needs, providing training for staff, and working with insurers and regulators to ensure coverage and compliance.
For now, radiologists and the labs that support them are not going away. They are adapting, and AI will be a partner in that evolution.
