Pathologists face uncertain future in AI era: Will they lead or be left behind?

Artificial intelligence is advancing at a pace that could fundamentally alter the medical field of pathology. A new study by researchers from the American University of Beirut Medical Center examines how AI is reshaping diagnostic workflows and whether pathologists will continue as indispensable partners in healthcare or risk being sidelined.

The research, titled “The Rise of AI-Assisted Diagnosis: Will Pathologists Be Partners or Bystanders?” and published in Diagnostics in 2025, traces the digital evolution of pathology, explores the transformative potential of AI-driven tools, and raises critical questions about professional identity, patient safety, and the future of medical diagnostics.

How AI is transforming the diagnostic landscape

Pathology has historically revolved around the microscope, but the transition to digital platforms has created the foundation for AI integration. High-resolution whole-slide imaging now allows for remote consultation, seamless data storage, and computational analysis at scales previously unimaginable. These advances are not just logistical; they are opening the door for automation across key diagnostic tasks.

AI is already proving effective in areas such as cancer detection, tumor grading, and biomarker quantification. Clinical deployments in leading healthcare systems, including the U.S. Food and Drug Administration’s approval of Paige Prostate Detect and trials within the National Health Service, underscore that these technologies are moving from research into practice.

The study highlights two categories of AI tools currently shaping pathology. The first are task-specific models, which focus narrowly on functions such as detecting mitotic figures or quantifying immunohistochemical stains. The second are foundation models such as PathChat, which leverage multimodal learning and interactive interfaces to support broader diagnostic reasoning. These more advanced systems can assist with differential diagnoses and integrate data across imaging, clinical history, and molecular markers.

What risks and challenges accompany AI integration

Despite these advancements, the study warns that the path to fully AI-driven diagnostics is fraught with challenges. Training datasets often lack diversity, raising the risk of bias and inconsistent performance across demographic groups. Systems trained in one clinical setting may underperform in others, limiting generalizability.

Another major obstacle is interpretability. Deep learning algorithms function as “black boxes,” producing results without transparent reasoning. For clinicians and regulators, this lack of explainability raises concerns about accountability. If an AI system misdiagnoses a patient, determining liability becomes a serious legal and ethical dilemma.

The authors also highlight equity concerns. Wealthier healthcare systems may adopt advanced AI faster, while under-resourced regions risk being left behind. Without careful planning, the digital divide in pathology could worsen, undermining global efforts to improve cancer care and other diagnostic services.

Perhaps the most provocative scenario explored in the study is the possibility of a “pathologist-free” diagnostic model. In this system, AI would analyze digital slides and generate reports directly for referring physicians, bypassing human pathologists altogether. While this could lower costs and reduce turnaround times, it would also remove a critical layer of human oversight, raising profound questions about patient safety, professional accountability, and trust in medicine.

What future awaits pathologists in an AI-driven era

The study outlines three potential futures for the profession. The first is a symbiotic model in which AI augments pathologists, automating repetitive tasks and improving efficiency while physicians retain ultimate authority. The second is a transformational model where pathologists evolve into supervisors of AI systems, focusing on quality assurance, data interpretation, and multidisciplinary consultation. The third is a disruptive model where AI achieves superhuman diagnostic accuracy, potentially displacing human pathologists from frontline roles.

As per the research, the trajectory will depend on how pathologists and healthcare systems respond now. If pathologists embrace digital tools, cultivate expertise in AI oversight, and redefine their professional roles, they are likely to remain indispensable partners in the diagnostic chain. If they resist adaptation, they risk becoming bystanders to a technological revolution that could bypass them.

The authors argue that while full replacement is unlikely in the immediate term, the development of multimodal foundation models and the potential for domain-specific artificial general intelligence make the disruptive model increasingly plausible. These systems could eventually replicate not just narrow tasks but holistic diagnostic reasoning.

For pathology to remain relevant, the study calls for proactive adaptation. Training programs should integrate digital pathology and AI literacy, regulatory bodies must establish frameworks for accountability and explainability, and professional societies should position pathologists as leaders in guiding responsible AI adoption.