Artificial intelligence (AI) is already changing medical imaging by supporting image interpretation, workflow optimization, and clinical decision support. Between 1995 and 2025, radiology accounted for 76.5% of the 1,430 AI/machine learning (ML)-enabled devices authorized by the U.S. Food and Drug Administration (FDA).
However, FDA authorization alone does not show whether a device is widely used in clinical practice or whether it genuinely improves patient care. Radiologists evaluating the future of AI in healthcare need evidence about performance, clinical fit, limitations, and risks — both before and after deployment. Without this, there is a risk of integrating tools that offer little real-world value or introduce new forms of risk.
Where is AI delivering value in medical imaging?
AI’s clinical value in radiology is evident in well-defined applications where researchers can measure changes in detection, workflow optimization, or clinical decision-making. These practical results provide a clearer basis for evaluating AI than adoption or technical capability alone.
AI for detection and diagnosis in medical imaging
A randomized Mammography screening with artificial intelligence (MASAI) trial of 105,934 women found that AI-supported screening increased cancer detection rate by 29%, compared to standard double reading.
Cancer detection is one application of a broader role for AI during interpretation. A Cureus scoping review of 12 studies mapped out where existing research has looked, finding applications that included:
- Detecting abnormalities
- Flagging overlooked findings
- Reducing reported errors in interpretation and reporting
AI for radiology workflow optimization
AI can reduce the number of images radiologists need to review or shorten interpretation time. For instance, the same MASAI trial that improved detection rate also reduced screen-reading workload by 44.2%, by determining which mammograms needed single reads versus full double reads.
This demonstrates that AI’s value is not limited to a single metric — it can create complementary improvements within the same workflow. For institutions, workflow optimization must translate into defined outcomes, such as fewer readings, shorter interpretation times, or other measurable changes that support patient care.
AI in imaging clinical decision support
AI can support clinical decision-making by organizing information, prioritizing cases, assessing imaging requests against defined criteria, and surfacing relevant inputs for review. Large language models (LLMs) extend that capability to text-heavy clinical work, creating potential applications in reporting, summarization, and information retrieval.
However, clinicians remain responsible for interpreting AI outputs and making final patient care decisions. AI can inform, but not replace, clinical judgment.
In a 2024 survey of European Society of Radiology members, 48% believed patients would not accept AI-only radiology reports without physician supervision and approval. The finding points to an expectation that human oversight will remain part of AI-assisted patient care.
How should institutions evaluate AI in medical imaging?
An AI model that performs well in testing can still behave differently once deployed, because deployment brings real patients, existing clinical workflows, and guardrails that the testing environment doesn’t account for.
Governing medical imaging AI across its lifecycle
Responsible AI governance continues throughout deployment, defining what a system should do, who is accountable for its use, and how performance will be monitored. These decisions need to begin before implementation, because problems can emerge in the gap between testing and deployment.
Some questions healthcare leaders should ask when evaluating AI include:
- Validation: How does the model perform with real patient population, not just the one it was trained on?
- Accountability: What role do clinicians play in the development, validation, and monitoring processes?
- Safeguards: What measures are in place to identify and mitigate risks, supporting patient safety?
- Reliability: Is the model grounded in peer-reviewed, expert-authored literature?
- Modernity: How does the AI tool adapt to new evidence and advancing radiological technology?
External requirements add another layer of consideration. No single regulatory framework governs AI in healthcare. Instead, institutions must navigate a patchwork of existing healthcare regulations layered with AI-specific requirements, such as those from the FDA or the EU AI Act.
An institution adopting a new AI tool still has to identify which rules apply to that specific technology, its data, its intended use, and the jurisdiction it operates in, rather than assuming that approval anywhere means compliance everywhere.
Reducing bias in medical imaging AI models
AI performance can shift when real-world patients or environments differ from the data used to develop the model. These differences make representative datasets, external testing, and continued monitoring important to evaluation.
Demographic imbalance is one source of mismatch. Another risk is shortcut learning, where a model relies on incidental features that happened to correlate with the correct answer in training, rather than learning the intended clinical signal. Both issues can undermine generalizability and patient safety if not detected and addressed.
A model's published bias profile does not transfer unchanged from one setting to another. Testing outside its development environment allows researchers to compare patterns and identify challenges that show up with different patients and clinical protocols.
What evidence does AI in medical imaging still need?
AI in radiology still needs a wider evidence base beyond any single study — a way to compare new findings against previous research, established clinical knowledge, and specialty evidence.
An AI study can establish how a model performed under defined conditions, but only access to the broader literature allows institutions to judge how those results fit into the larger radiology field. Researchers and clinicians can compare new findings against prior evidence to understand what each contribution means for practice.
For example, a question about AI-assisted lung cancer imaging may require previous diagnostic studies, cancer imaging research, and educational material. Different teams follow different parts of that evidence trail:
- Researchers: Prior methods, citation trails, and current AI studies
- Clinicians: Specialty and clinical evidence that places an AI finding within an established diagnostic pathway
- Educators and students: Resources connecting emerging technologies with imaging knowledge and clinical applications
- Librarians and institutional decision makers: Resources that support overlapping research, clinical, educational, and specialty information needs across the institution
The evidence needed to evaluate imaging AI can extend from the model itself into the clinical problem it is designed to address.
Connecting medical imaging AI research to the wider literature
The Radiological Society of North America (RSNA) has a journal portfolio that spans research, education, AI evaluation, clinical practice, and specialty imaging — brought together in the RSNA Complete Collection on Ovid®.
Medical imaging shows both the potential and the evaluation challenge of AI in healthcare. As applications expand, institutions need evidence that shows where results hold, how systems perform after implementation, and how new findings fit within established clinical knowledge.
The RSNA Complete Collection on Ovid answers these questions with one connected subscription to literature spanning nearly a century of prior research, including Radiology®, RadioGraphics®, Radiology: Artificial Intelligence®, Radiology: Cardiothoracic Imaging®, and Radiology: Imaging Cancer®, to support research in medical imaging AI.
Explore the RSNA Complete Radiology Collection on Ovid to connect current AI research with the broader radiology evidence base.