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.
WHO is developing its first global guidelines on multiplex testing for HIV, hepatitis, and STIs, offering lab leaders a framework to optimize diagnostics, streamline workflows, and lead the shift toward integrated, multi-disease testing.
As diagnostic technologies rapidly evolve and healthcare systems shift toward integrated service delivery, laboratory leaders are at the forefront of implementing efficient, multi-disease testing strategies.
In a landmark move, the World Health Organization (WHO) has convened a Guideline Development Group (GDG) to develop its first-ever evidence-based recommendations on multiplex testing, a method that enables simultaneous detection of HIV, viral hepatitis, and STIs from a single sample. This initiative will establish foundational principles for integration, resource prioritization, and diagnostic efficiency, offering lab professionals critical guidance to shape the future of testing across diseases.
The GDG, comprised of international experts and stakeholders, will provide evidence-based recommendations to support integrated, people-centered diagnostic strategies.
As global health systems shift toward more integrated service delivery, multiplex testing is emerging as a practical tool for increasing diagnostic access, streamlining care, and maximizing limited resources, particularly in low- and middle-income countries, where testing gaps remain significant.
“Access to timely and accurate diagnostic testing is essential for the prevention, detection, and management of HIV, viral hepatitis and STIs,” WHO stated in its announcement. “Multiplex testing has emerged as a promising strategy to improve efficiency, expand testing coverage across diseases, and enhance cost-effectiveness.”
A First-of-Its-Kind Guideline
This is the first WHO guideline to explicitly address multiplex testing using the most up-to-date evidence. While the focus will be on HIV, viral hepatitis, and STIs, the guidelines aim to establish critical principles for integration that can be extended to other disease areas over time.
“This guideline will provide critical principles for integration that drive public health impact and chart the course for further multi-disease testing approaches,” WHO noted.
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The GDG will address both provider-based testing and self-testing, recognizing the growing role of community-led and patient-driven diagnostics. Key issues on the agenda include how to prioritize limited testing resources, ensure quality assurance, and optimize public health outcomes through integrated diagnostic models.
Guideline Development Group: Global, Inclusive, Independent
The GDG is composed of members from all WHO regions, selected based on their technical expertise, field experience, and perspectives as either implementers or members of affected communities. Importantly, all members participate in their individual capacities and not as representatives of any affiliated organizations.
“In accordance with WHO guidelines for developing recommendations, the GDG is composed of members from all WHO regions, serving in their individual capacities,” the organization explained. “Members do not receive financial compensation for their contributions to this process.”
The GDG includes program managers, healthcare providers, researchers, and community advocates, reflecting the full spectrum of stakeholders involved in diagnostic service delivery. Their diverse backgrounds are intended to ensure that the guidelines are evidence-informed, practical, and contextually relevant across settings.
A virtual meeting is scheduled for November 4-5, 2025, during which the group will discuss evidence, identify priority recommendations, and finalize key principles around multiplex testing and integration.
WHO Opens Public Comment Period
To promote transparency and inclusivity, WHO has opened a public comment period and is inviting feedback on the composition of the GDG. Stakeholders, organizations, and individuals can review member biographies and submit comments via email to hiv-aids@who.int by September 29, 2025.
The upcoming guideline represents a significant step in advancing integrated diagnostics and expanding access to care, particularly in resource-limited settings. By combining clinical evidence with real-world insights, WHO aims to provide countries and implementers with practical, scalable recommendations that improve testing coverage and disease detection at all levels of the health system.
As testing technologies evolve and the demand for multi-disease platforms grows, this guideline may serve as a blueprint for future diagnostic integration efforts, laying the foundation for efficient, patient-centered, and cost-effective care delivery worldwide.
