Mapping AI’s role in imaging, surgery, pathology and drug discovery

Artificial intelligence is already reshaping clinical medicine, but its integration into patient care still faces major hurdles. A new study offers one of the most comprehensive reviews to date on how AI is being applied across diagnostic imaging, clinical decision support, surgery, pathology, and drug discovery, while also warning of the challenges that must be overcome.

The paper, Artificial Intelligence in Clinical Medicine: Challenges Across Diagnostic Imaging, Clinical Decision Support, Surgery, Pathology, and Drug Discovery, was published in Clinical Practice in 2025. The author systematically analyzed 150 studies drawn from more than 2,000 initial sources, focusing exclusively on applications with clinical impact and patient-level outcomes. The findings provide a critical snapshot of AI’s role in healthcare and the balance between innovation and caution.

Where is AI making the strongest impact in clinical medicine?

According to the review, diagnostic imaging is the most advanced field for AI adoption. Deep learning algorithms have demonstrated accuracy equal to or greater than radiologists in cancer detection, stroke assessment, and diabetic retinopathy screening. Reported performance metrics, such as area under the curve scores approaching 0.94, suggest that AI is capable of improving detection rates and reducing missed diagnoses.

Pathology is also showing rapid progress. Algorithms have been deployed to detect metastases, grade tumors, and even infer genetic mutations from histopathology slides. Studies found that pathologists working in collaboration with AI tools achieved higher accuracy than either human or machine alone, pointing toward a hybrid model of decision-making.

In surgery, AI is beginning to influence preoperative planning, intraoperative guidance, and robotic assistance. Pilot trials indicate that AI can improve surgical precision and safety, but the technology remains in its early stages of adoption. Concerns over reliability, liability, and regulatory standards have slowed broader deployment.

Meanwhile, in clinical decision support systems, AI has been used to analyze electronic health records to predict outcomes such as sepsis, atrial fibrillation, and in-hospital mortality. While the predictive power is strong, real-world evidence of improved patient outcomes is mixed due to workflow integration issues and variable clinician trust.

Drug discovery represents another frontier. AI has been employed for target identification, molecular screening, and breakthroughs such as AlphaFold’s protein structure predictions. These advances have accelerated discovery timelines but still face validation hurdles and complex regulatory processes before clinical use.

What challenges prevent AI from becoming routine in healthcare?

The review highlights several obstacles that limit AI’s full integration into clinical practice. A recurring issue is bias in training data, which can lead to disparities in care outcomes across different patient populations. Models trained on limited datasets may fail when deployed in diverse real-world settings.

Transparency is another barrier. Many AI systems function as “black boxes,” making it difficult for clinicians to understand how decisions are reached. This lack of interpretability undermines trust and complicates regulatory approval. While explainable AI techniques are being developed, they are not yet standard in clinical applications.

The review also underscores the lack of large-scale prospective trials. Much of the evidence to date comes from retrospective studies, leaving questions about real-world effectiveness unanswered. Without stronger trial data, regulatory agencies remain cautious about approving widespread clinical use.

Ethical challenges add further complexity. Questions of liability arise when AI errors contribute to patient harm. Privacy concerns persist around the massive datasets required to train algorithms. The potential for overreliance on AI also raises fears of deskilling clinicians and weakening human judgment in critical decision-making.

How can AI be safely integrated into clinical practice?

Despite these challenges, the author stresses that AI has the potential to transform medicine if developed and deployed responsibly. The key lies in synergy between humans and machines. Rather than replacing clinicians, AI should be seen as an augmentative tool that enhances accuracy, efficiency, and access to care while leaving contextual and ethical decision-making to humans.

The study calls for interdisciplinary collaboration between computer scientists, clinicians, ethicists, and regulators. Transparent evaluation frameworks are essential to ensure safety and fairness. Robust clinical trials must be prioritized to demonstrate effectiveness in real-world settings. Regulatory bodies, in turn, need to develop adaptive policies that keep pace with technological advances without compromising patient safety.

Education and training are also vital. Clinicians must be equipped with the skills to understand AI outputs, identify potential biases, and integrate these tools into clinical workflows. Health systems should invest in infrastructure that enables secure and ethical use of large datasets, balancing innovation with data protection.

The future of AI in medicine, the author asserts, depends not only on technological capability but also on governance and ethical responsibility. By aligning innovation with trust and transparency, healthcare systems can harness AI’s benefits while safeguarding patients.