Medical Imaging AI Safety: Why Diagnostic Accuracy Is Not Enough

Edited by ScholarGen AIHealthcareInsight Team

How Artificial Intelligence Is Transforming Radiology—and Why Clinical Trust Requires More Than High Accuracy

Artificial intelligence is already changing medical imaging.

Across computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), mammography, ultrasound, and radiography, AI systems are increasingly being developed and deployed to detect abnormalities, segment anatomy, classify findings, quantify disease burden, prioritize examinations, reconstruct images, and support clinical decision-making.

The transformation is no longer theoretical.

The more important question is now:

Can medical imaging AI be trusted when it becomes part of real clinical care?

That question cannot be answered simply by asking whether an algorithm has high accuracy, sensitivity, specificity, or area under the receiver operating characteristic curve.

In medicine, an AI output is not the endpoint.

It becomes part of a clinical chain:

Image Acquisition → AI Analysis → Human Interpretation → Clinical Decision → Treatment → Biological Outcome

This distinction fundamentally changes what "accuracy" means.

An algorithm can perform extremely well under controlled testing and still create clinical risk when deployed in a different hospital, on a different scanner, in a different patient population, or within a workflow that encourages excessive reliance on its output.

The central challenge of medical imaging AI is therefore not merely whether an algorithm can recognize a pattern.

It is whether the entire human-AI clinical system can produce reliable decisions under real-world conditions.

Why Is AI Transforming Medical Imaging?

Medical imaging is particularly suitable for artificial intelligence because modern radiology generates enormous quantities of complex visual information.

A CT examination may contain hundreds or thousands of image slices. MRI examinations can include multiple sequences acquired with different parameters. PET provides functional information that can be integrated with anatomical imaging. Mammography requires careful evaluation of subtle changes in tissue density and architecture.

AI can process these data at a scale that would be difficult to reproduce manually.

Depending on the application, an imaging AI system may perform:

Abnormality detection
Lesion classification
Organ and lesion segmentation
Quantitative measurement
Image reconstruction
Examination triage
Risk prediction
Longitudinal comparison
Reporting assistance
Clinical decision support

But these functions are not interchangeable.

A system designed to detect pulmonary embolism on CT performs a fundamentally different clinical task from an algorithm designed to segment a brain tumor, quantify cardiac function, reconstruct an MRI examination, or predict treatment response.

Consequently, AI safety must always be evaluated in relation to the intended clinical use.

The integration of AI into radiology also requires careful consideration of how algorithms interact with PACS, RIS, reporting systems, and radiologist decision-making. This is why AI-Augmented Radiology Workflow Integration is an important part of the broader discussion of clinical AI safety.

What Does “AI Accuracy” Actually Mean in Radiology?

Accuracy is one of the most visible metrics in medical AI research, but it is not a complete description of clinical reliability.

A diagnostic model may be evaluated using:

Sensitivity
Specificity
Positive predictive value
Negative predictive value
Area under the ROC curve
Calibration
Dice similarity coefficient
Mean average precision
Task-specific error measures

Each metric describes a particular aspect of technical performance.

None, by itself, answers the broader clinical question:

What happens when the model encounters a patient whose images differ from the data used to develop and evaluate it?

This is where the distinction between technical performance and clinical reliability becomes essential.

Consider a simplified example.

An AI model is trained using CT examinations acquired with a particular combination of scanner characteristics, reconstruction algorithms, imaging protocols, patient demographics, and institutional practices.

The model is then introduced into another hospital.

The second hospital may use:

Different CT scanners
Different reconstruction kernels
Different slice thicknesses
Different contrast protocols
Different patient populations
Different disease prevalence
Different image-quality distributions
Different clinical workflows

The algorithm may continue to generate an output.

But the critical question is whether that output retains the same clinical meaning.

A trustworthy AI pipeline therefore requires more than initial model development. Trustworthy AI Pipelines should incorporate validation, monitoring, interpretability, and appropriate governance throughout the AI lifecycle.

Why Can a Highly Accurate AI System Still Be Unsafe?

The most important reason is simple:

Performance can change when the environment changes.

This can occur through dataset shift, domain shift, distribution shift, workflow changes, or other alterations in the clinical operating environment.

An AI model does not encounter "disease" in isolation.

It encounters:

Pixels
Acquisition parameters
Reconstruction characteristics
Artifacts
Anatomy
Patient characteristics
Clinical context
Workflow conditions

A model can therefore be affected by factors that may not be obvious to the clinician.

For example, performance can change when:

Image quality decreases
A different scanner is introduced
Reconstruction software is upgraded
Slice thickness changes
Contrast timing changes
The patient population changes
Disease prevalence changes
Postoperative anatomy becomes more common
Multiple diseases coexist
The clinical workflow changes

The resulting problem is not necessarily that the AI suddenly "stops working."

More subtly, the AI may continue producing plausible-looking outputs while its error distribution changes.

That may be more difficult to recognize.

What Is Automation Bias in Medical Imaging?

Automation bias occurs when users place excessive reliance on an automated recommendation and reduce their independent evaluation.

Radiology is particularly relevant because AI results can be integrated directly into the reading environment.

Imagine an AI system highlighting a suspected pulmonary nodule.

The radiologist may immediately inspect the highlighted region.

That can be useful.

But if the system fails to highlight an abnormality, the same workflow may unintentionally reduce attention to areas that the AI has not marked.

The AI has therefore influenced not only interpretation but also the distribution of human attention.

This is an important distinction.

Human oversight is not simply a statement that "a radiologist reviewed the result."

Effective oversight requires that the clinician can:

Understand the intended use of the AI.
Recognize its limitations.
Review the original images.
Identify contradictory findings.
Override the AI when appropriate.
Escalate uncertainty.
Continue independent clinical reasoning.

Trustworthy clinical AI therefore depends on both technology and system design. A robust enterprise platform must connect model outputs with clinical context, human oversight, validation, and governance.

Why Is Medical AI Safety Ultimately a Biological Problem?

This is perhaps the most important conceptual point.

A computer does not directly experience a patient's biological state.

It analyzes representations of biology.

A CT image contains information generated by X-ray attenuation and image reconstruction.

An MRI image reflects tissue properties and acquisition physics.

A PET image reflects tracer distribution and metabolism.

AI interprets these representations.

The clinical consequence occurs later.

For example:

Image → AI Interpretation → Physician Decision → Treatment Pathway → Biological Consequence

If an AI system misses a malignant lesion, the immediate error is computational.

The downstream consequence may be delayed diagnosis.

If an AI system incorrectly classifies a lesion as benign, surveillance or intervention may be altered.

If an AI system incorrectly identifies a vascular emergency, triage may change.

If an AI-derived quantitative measurement is incorporated into treatment planning, an error may influence therapeutic decisions.

Therefore, the relevant safety question is not simply:

"Did the AI classify the image correctly?"

It is:

"Did the AI-supported clinical system produce an appropriate decision for this patient?"

That is a much higher standard.

The Medical Imaging AI Safety Chain

Figure 1. Medical imaging AI is part of a clinical chain rather than an isolated algorithm. The safety of an AI system depends on the interaction between image acquisition, AI processing, human verification, clinical context, decision-making, and downstream biological outcomes.

What Are the Major Sources of Risk in Medical Imaging AI?

AI safety in radiology can be considered across several interconnected domains.

Table 1. Major Risk Domains in Medical Imaging AI

Risk Domain	Example	Potential Clinical Consequence
Data quality	Motion, artifacts, incomplete examination	Incorrect interpretation
Dataset shift	Different scanner or patient population	Reduced generalizability
Model error	False negative or false positive	Missed or unnecessary diagnosis
Calibration	Confidence does not reflect actual probability	Misleading clinical trust
Automation bias	Excessive reliance on AI output	Reduced independent review
Workflow integration	AI result arrives at the wrong time	Delayed or inappropriate action
Alert burden	Excessive false-positive notifications	Alert fatigue
Model drift	Performance changes over time	Undetected degradation
Bias	Unequal performance across populations	Potential healthcare disparities
Cybersecurity	Manipulation or unauthorized access	Unsafe clinical operation
Governance failure	No defined monitoring responsibility	Persistent undetected risk

The critical observation is that not all risks exist inside the neural network.

Some arise from the socio-technical system surrounding the model.

That means improving the algorithm alone may not eliminate clinical risk.

What Is Model Drift and Why Does It Matter?

A medical AI model is developed at a particular point in time.

But clinical environments change continuously.

Imaging protocols evolve.

New scanners are installed.

Patient populations change.

Disease prevalence changes.

Clinical pathways change.

Software is updated.

The model itself may also be modified.

As these variables change, the relationship between the input data and the desired output can change.

This creates the possibility of model drift.

Model drift does not necessarily mean that the original model was poorly designed.

It may mean that the environment in which the model operates is no longer sufficiently similar to the environment in which it was developed and evaluated.

This is why AI safety cannot end at regulatory authorization or initial deployment.

It requires continuous monitoring.

For a deeper examination of this issue, see AI Model Drift Detection in Medical Imaging, which examines how changes in data, imaging environments, and clinical workflows can affect deployed AI systems.

How Should Hospitals Monitor Medical Imaging AI?

A mature monitoring strategy should examine multiple layers rather than relying exclusively on model accuracy.

Table 2. Enterprise Medical Imaging AI Monitoring Framework

Monitoring Layer	What to Monitor	Example Signal	Possible Response
Infrastructure	Availability and latency	Increased inference delay	Technical investigation
Data	Input distribution	Scanner/protocol shift	Data review
Image quality	Acquisition quality	Increased motion or artifacts	Workflow review
Model	Prediction behavior	Confidence distribution changes	Performance investigation
Clinical	Reader interaction	Rising override rate	Clinical review
Workflow	Alert burden	Increasing ignored alerts	Workflow redesign
Population	Subgroup performance	Performance divergence	Bias assessment
Governance	Version and change history	Uncontrolled model update	Governance escalation
Security	Access and system integrity	Unusual activity	Cybersecurity investigation

The key principle is:

Monitoring should be risk-based and clinically meaningful.

A system that simply reports that the AI service is online is not necessarily monitoring clinical safety.

A hospital dashboard may show:

AI Availability: 99.9%

while the clinically relevant question is:

Is the AI still producing appropriate results for today's patients?

This distinction is fundamental.

Is Software Uptime the Same as Clinical Reliability?

No.

Software uptime measures whether the technology is operational.

Clinical reliability asks whether the technology remains appropriate and useful for the clinical task.

Consider two scenarios.

Scenario A

The AI system is technically available, but a scanner upgrade changes image characteristics and model sensitivity declines.

Scenario B

The AI system is technically available, but excessive false-positive alerts cause radiologists to ignore its notifications.

In both cases, the software is "working."

But the clinical system may no longer be functioning as intended.

This is why Post-Deployment AI Monitoring and Governance is important: deployment should be regarded as the beginning of ongoing clinical surveillance rather than the end of validation.

Why Is Workflow Integration Part of AI Safety?

A clinically accurate algorithm can still fail to provide clinical value if its output is delivered at the wrong time or in the wrong place.

Suppose an AI system identifies a potentially life-threatening abnormality.

The result is generated correctly.

But the notification appears:

After the radiologist has completed the case
In a separate application
Without patient context
Without appropriate prioritization
Among dozens of competing alerts

The algorithm may be technically correct.

The clinical system may still fail.

This is why AI workflow integration is not merely an IT concern.

It is a patient-safety concern.

A well-designed AI-Augmented Radiology Workflow must consider not only the algorithm itself but also how its output reaches the radiologist and influences the clinical workflow.

Trustworthy AI Clinical Workflow Architecture

Figure 2. Enterprise clinical AI architecture for safe medical imaging deployment. An orchestration layer coordinates clinical data, AI services, validation, human review, clinical action, monitoring, and governance.

What Happens When Hospitals Deploy Multiple AI Systems?

A modern hospital may use separate AI systems for:

Stroke detection
Intracranial hemorrhage
Pulmonary embolism
Pulmonary nodules
Fracture detection
Cardiac imaging
Mammography
Organ segmentation
Risk prediction
Clinical documentation

If each application creates its own interface, notification mechanism, data pathway, and governance process, the result can become an AI island architecture.

Instead of reducing complexity, AI can create another layer of complexity.

This is why AI orchestration has become increasingly important.

An Enterprise AI Orchestration architecture can help coordinate clinical information, AI services, workflow priorities, human review, and governance.

Similarly, AI Orchestration Layers can provide an operational layer connecting data movement, AI services, workflow integration, monitoring, and governance.

The goal is not to create a hospital in which every clinical problem has a separate AI application.

The goal is to create an environment in which AI services can operate as coordinated components of a larger clinical system.

What Changes When Generative AI Enters Medical Imaging?

Traditional imaging AI often performs a relatively defined task.

Generative AI introduces a broader class of capabilities.

A multimodal system may potentially process:

Medical images
Radiology reports
Electronic health record information
Laboratory data
Pathology results
Clinical notes
Prior examinations

This creates opportunities for richer clinical context.

But it introduces additional risks.

A generative model may produce fluent language that appears authoritative even when the underlying conclusion is incorrect.

Therefore:

Fluency is not evidence of diagnostic validity.

The distinction between generation and verification becomes particularly important.

A safer conceptual workflow is:

Retrieve → Analyze → Generate → Verify → Escalate → Approve → Document

The verification stage must remain clinically meaningful.

Is FDA Authorization the Same as Proof That AI Is Safe Everywhere?

No.

FDA authorization is an important component of medical-device oversight, but it should not be interpreted as a guarantee of identical performance in every clinical environment.

AI-enabled medical devices are evaluated according to their intended use and applicable regulatory requirements.

The phrase intended use is critical.

A device may be authorized for a defined purpose under defined conditions.

That does not automatically establish identical performance for every possible:

Patient population
Scanner
Imaging protocol
Institution
Workflow
Clinical environment

Hospitals therefore still need implementation-level evaluation.

They should ask:

Does the technology perform adequately in our patient population?
Does it work with our imaging protocols?
How does it interact with our PACS and RIS?
How are alerts delivered?
Who reviews the results?
What happens when the AI is unavailable?
How are false positives handled?
How are false negatives identified?
How is performance monitored after deployment?
Who owns the governance process?

These are governance questions as much as technical questions.

How Should Hospitals Evaluate Medical Imaging AI Before Deployment?

A hospital should not begin with:

"Is this AI accurate?"

A more useful question is:

"Is this AI appropriate for this clinical use in our environment?"

A practical evaluation pathway includes several stages.

1. Define the Clinical Use Case

Specify exactly what the AI is expected to do.

Detection?

Classification?

Quantification?

Triage?

Segmentation?

Prediction?

Reporting assistance?

The intended function determines the appropriate validation strategy.

2. Examine the Evidence

Review:

Study design
Patient population
Sample size
Reference standard
Inclusion and exclusion criteria
External testing
Performance metrics
Failure analysis
Subgroup performance
Conflicts of interest
Reproducibility

3. Perform Local Evaluation

Determine whether the technology performs adequately in the local clinical environment.

4. Test Workflow Integration

Technical accuracy is insufficient if the AI output is delivered at the wrong time or to the wrong person.

5. Define Human Oversight

Determine who reviews the AI output and who has authority to override it.

6. Establish Monitoring

Performance should be monitored after implementation.

7. Define Failure Procedures

A mature AI program needs explicit procedures for:

AI downtime
Unexpected output
Model degradation
Cybersecurity incidents
Clinical disagreement
Model updates
Revalidation

From Validation to Continuous Clinical AI Vigilance

Figure 3. Medical AI safety is a lifecycle rather than a one-time validation event. Continuous monitoring, drift detection, clinical review, revalidation, and controlled updating are required as the clinical environment evolves.

What Does a Radiologist Need to Know About AI Failure?

The most useful AI training for radiologists should not focus only on how to use the software.

It should also explain:

How can this system fail?

Radiologists should understand:

Intended use
Known limitations
Typical false positives
Typical false negatives
Image-quality limitations
Population limitations
Confidence interpretation
Appropriate override behavior
Workflow escalation
Model-update procedures

This represents a shift from simply learning how to operate AI toward understanding AI as a clinical instrument.

Just as radiologists understand the limitations of CT, MRI, ultrasound, and PET, they increasingly need to understand the limitations of the computational systems interpreting those examinations.

That creates a new form of professional literacy:

AI literacy for radiologists.

Can AI Replace the Radiologist?

The more clinically useful question is not whether AI will replace radiologists.

It is:

How will radiologists' responsibilities change as AI becomes embedded in imaging workflows?

AI may increasingly reduce repetitive visual search and quantitative tasks while increasing the importance of:

Complex interpretation
Clinical integration
Multimodality correlation
Uncertainty management
AI supervision
Quality assurance
Error detection
Communication
Governance
Validation

This does not eliminate the need for expertise.

It changes where expertise is applied.

A radiologist working with AI must understand not only anatomy and disease but also the operating characteristics and failure modes of the systems being used.

Why Is Trustworthy AI a Systems Problem?

Trustworthy AI cannot be reduced to a single model characteristic.

A system may have excellent sensitivity but poor calibration.

It may have strong external validation but weak workflow integration.

It may perform well initially but deteriorate after deployment.

It may provide accurate results but create excessive alert burden.

It may be technically secure but lack clear clinical accountability.

A systems-based approach connects medical imaging AI with clinical workflow integration, trustworthy AI pipelines, continuous model monitoring, enterprise orchestration, and clinical governance.

This perspective shifts the focus from:

"How accurate is the algorithm?"

to:

"How reliably does the complete clinical system perform?"

That distinction is central to the future of medical AI.

What Does Trustworthy AI Mean in Healthcare?

Trustworthy AI is broader than accuracy.

The NIST AI Risk Management Framework identifies characteristics including validity and reliability, safety, security and resilience, accountability and transparency, explainability and interpretability, privacy enhancement, and fairness with harmful bias managed.

For medical imaging, these concepts can be translated into practical clinical questions.

Validity

Does the system perform the intended clinical task?

Reliability

Does performance remain sufficiently stable under relevant conditions?

Safety

Can foreseeable failures be detected and controlled?

Robustness

Can the system tolerate reasonable variation in imaging and clinical conditions?

Transparency

Can users understand what the system is intended to do?

Explainability

Can the output be interpreted sufficiently to support clinical review?

Fairness

Does performance vary substantially across clinically relevant patient populations?

Security

Can the AI system and its data be protected from unauthorized manipulation?

Accountability

Is there a clear process for monitoring and responding to failures?

These principles reinforce the idea that clinical trust emerges from multiple controls rather than from algorithmic accuracy alone.

Medical Imaging AI Safety: A Practical Decision Framework

Table 3. From AI Performance to Clinical Trust

Question	Technical Perspective	Clinical Safety Perspective
Does the model work?	Accuracy and performance metrics	Is performance adequate for the intended use?
Was it validated?	Internal testing	Was external and local validation performed?
Is it available?	System uptime	Does it reliably support clinical workflow?
Is the output accurate?	Model prediction	Does it improve or appropriately support decisions?
Does it generalize?	Dataset testing	Does it work in our patients and imaging environment?
Is it explainable?	Heatmaps or features	Can clinicians meaningfully evaluate the output?
Is it monitored?	Technical dashboard	Are clinical performance and workflow impact monitored?
Is it updated?	Software release	Is every update controlled and revalidated?
Who is responsible?	Vendor / IT	Is clinical accountability clearly defined?
What happens after failure?	Error logging	Is there an escalation and recovery pathway?

This table captures the central argument:

Clinical trust is broader than algorithmic performance.

Why Should Trust Be Earned Rather Than Assumed?

There is a temptation to equate technological sophistication with reliability.

A deep neural network can be extraordinarily complex.

A multimodal model can process enormous amounts of information.

A medical imaging platform can generate results in seconds.

None of these characteristics independently establishes clinical safety.

Trust must be supported by evidence.

That evidence should include:

Appropriate testing
Transparent reporting
Clinically relevant validation
Local implementation assessment
Post-deployment monitoring
Failure detection
Governance
Human oversight

The most important question may therefore be the simplest:

What happens when the AI is wrong?

A mature AI program should already have an answer.

The Real Meaning of AI Safety in Radiology

Medical imaging AI has already moved beyond the laboratory.

AI is being incorporated into real clinical technologies and imaging workflows.

The challenge is no longer simply to build models that recognize patterns.

It is to build clinical systems that remain safe when conditions are imperfect.

The distinction is fundamental:

Algorithmic accuracy is a property of a model under specified evaluation conditions.

Clinical safety is a property of the entire healthcare system in which that model operates.

Those are not the same thing.

A model may be accurate but poorly integrated.

A workflow may be efficient but vulnerable to automation bias.

A system may perform well initially but deteriorate as the clinical environment changes.

A technically available AI service may still be clinically ineffective if its outputs are ignored or delivered too late.

For these reasons, trustworthy medical AI requires continuous attention to evidence, context, human oversight, and lifecycle governance.

Conclusion: AI Can Transform Radiology Without Becoming the Final Authority

AI is already changing radiology.

It can accelerate image analysis, support detection, automate measurements, assist reconstruction, prioritize examinations, and contribute to increasingly sophisticated clinical workflows.

But the most important question is not whether AI is impressive.

It is whether AI can be trusted for a specific clinical purpose, in a specific environment, with a specific population, under appropriate human oversight.

That is a much more demanding standard.

The future of medical imaging will therefore not be determined simply by who develops the most powerful model.

It will depend on who can build the most reliable human-AI clinical system.

For radiologists, physicians, hospital executives, and healthcare technology leaders, the strategic priority is to evaluate AI not only by how it performs when everything goes right, but also by how safely the clinical system responds when the AI is:

Uncertain
Wrong
Unavailable
Out of distribution
Poorly integrated
Affected by model drift

That is where AI safety becomes more than a technical concept.

It becomes a patient-safety requirement.

Key Takeaways

Medical imaging AI is increasingly becoming part of real clinical workflows.
High diagnostic accuracy does not automatically establish clinical safety.
AI performance can change when scanners, protocols, patient populations, or workflows change.
External and local validation are essential for understanding generalizability.
Automation bias can influence clinical reasoning even when a physician remains the final decision-maker.
Model drift makes post-deployment monitoring essential.
Workflow integration is part of patient safety, not merely an IT concern.
Generative AI introduces additional risks because fluent output can appear more reliable than it actually is.
Human oversight must involve active verification rather than nominal review.
Trustworthy AI requires validity, reliability, safety, robustness, transparency, fairness, security, and accountability.
The future of radiology AI depends on building reliable human-AI clinical systems rather than simply more powerful algorithms.

Frequently Asked Questions

Is medical imaging AI safe?

Medical imaging AI can be used safely when its intended use, evidence, validation, clinical workflow, human oversight, and post-deployment monitoring are appropriately managed. Safety cannot be inferred from a single accuracy metric or from performance in a controlled research dataset alone.

Can AI replace radiologists?

Current clinical imaging AI systems are generally designed for specific functions rather than complete autonomous radiological practice. AI can assist with detection, segmentation, quantification, reconstruction, triage, and decision support while radiologists integrate those outputs with patient-specific clinical information.

Why is external validation important?

External validation evaluates an AI system on data meaningfully independent from its development environment. It can reveal changes in performance related to different institutions, scanners, patient populations, protocols, and disease distributions.

What is model drift?

Model drift refers to changes in AI behavior or performance as the clinical environment changes. Scanner upgrades, imaging protocols, patient populations, software modifications, and clinical practice changes can all contribute.

What is automation bias?

Automation bias occurs when users place excessive reliance on an automated recommendation. In radiology, this can influence both interpretation and the distribution of visual attention.

Does FDA authorization guarantee identical performance everywhere?

No. Regulatory authorization applies to a defined medical-device context and intended use. Healthcare organizations must still consider local populations, imaging environments, workflows, and implementation conditions.

Why is AI safety different from AI accuracy?

Accuracy describes performance for a particular task under defined evaluation conditions. Safety encompasses the larger system, including data, workflow, human users, monitoring, governance, cybersecurity, and downstream clinical consequences.

References

U.S. Food and Drug Administration. Artificial Intelligence-Enabled Medical Devices. FDA.
Tabassi E. Artificial Intelligence Risk Management Framework (AI RMF 1.0). National Institute of Standards and Technology; 2023. doi:10.6028/NIST.AI.100-1.
Autio C, Schwartz R, Dunietz J, et al. Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile. NIST AI 600-1. National Institute of Standards and Technology; 2024. doi:10.6028/NIST.AI.600-1.
Tejani AS, Klontzas ME, Gatti AA, et al. Checklist for Artificial Intelligence in Medical Imaging (CLAIM): 2024 Update. Radiology: Artificial Intelligence. 2024;6(4). doi:10.1148/ryai.240300.
Mongan J, Moy L, Kahn CE Jr. Checklist for Artificial Intelligence in Medical Imaging (CLAIM): A Guide for Authors and Reviewers. Radiology: Artificial Intelligence. 2020;2(2). doi:10.1148/ryai.2020200029.
Lekadir K, Frangi AF, Porras AR, et al. FUTURE-AI: International Consensus Guideline for Trustworthy and Deployable Artificial Intelligence in Healthcare. BMJ. 2025;388. doi:10.1136/bmj-2024-081554.
World Health Organization. Ethics and Governance of Artificial Intelligence for Health. Geneva: WHO; 2021.
World Health Organization. Ethics and Governance of Artificial Intelligence for Health: Guidance on Large Multi-Modal Models. Geneva: WHO; 2025.

Medical Disclaimer

This article is intended for educational and professional information purposes only. It does not constitute medical advice, diagnosis, treatment recommendations, or regulatory advice. The performance and clinical applicability of any artificial intelligence system depend on its intended use, validation evidence, implementation environment, patient population, and applicable regulatory requirements. Clinical decisions should remain under the responsibility of appropriately qualified healthcare professionals.

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