Radiologists may misidentify AI images - Scorecard - MDSpire
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Radiologists may misidentify AI images

CT and MRI images were harder to distinguish than radiographs and ultrasound, while subspecialty expertise was associated with greater accuracy.

  • By

  • Andrea Surnit

  • August 26, 2026

  • 4 min

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Clinical Scorecard: Radiologists may misidentify AI images

At a Glance

CategoryDetail
ConditionArtificial Intelligence in Radiology
Key MechanismsIdentification of AI-generated vs. real radiological images
Target PopulationRadiologists
Care SettingClinical Radiology

Key Highlights

  • Radiologists correctly identified 75% of AI-generated images.
  • Accuracy varied by imaging modality: CT (70%), MRI (77%), Ultrasound (88%), Radiographs (91%).
  • Relevant subspecialty expertise improved classification accuracy.
  • Confidence in classification correlated with correct identification.
  • Study limitations included online presentation of images and potential selection bias.

Guideline-Based Recommendations

Diagnosis

  • Assess classification accuracy of AI-generated images across modalities.

Management

  • Consider subspecialty expertise when evaluating AI-generated images.

Monitoring & Follow-up

  • Monitor radiologist confidence levels in image classification.

Risks

  • Potential misidentification of AI-generated images may impact diagnostic accuracy.

Patient & Prescribing Data

Not specified; study focused on radiologists.

N/A

Clinical Best Practices

  • Incorporate training on AI-generated images in radiology education.
  • Encourage radiologists to report confidence levels during image classification.

Related Resources & Content

Original Source(s)

Radiologists may misidentify AI images

  • by Andrea Surnit

  • August 26, 2026

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