A study evaluates generative AI's performance in interpreting chest X-rays for tuberculosis screening in low-resource settings.
Key Details
- 1Researchers trained a generative AI model using two public TB chest radiograph datasets.
- 2The AI generated free-text reports and labeled images as showing presence/absence and laterality of TB-related abnormalities.
- 3Radiologists compared the model's reports to their own interpretations and judged report acceptability.
- 4Two additional radiologists established the reference standard for evaluation.
- 5Generative AI shows promise but still needs significant oversight before clinical deployment.
Why It Matters
Automating TB screening with generative AI could address radiologist shortages, especially in high-prevalence, low-resource settings. However, the need for oversight highlights ongoing challenges around accuracy, reliability, and safe adoption in clinical workflows.
Source
Health Imaging
Related News
•AuntMinnie
AI for Breast Cancer Screening Not Cost-Effective, Study Finds
AI-assisted breast cancer screening showed minor clinical benefits over DBT alone but was not cost-effective at standard willingness-to-pay thresholds.
•AuntMinnie
AI Model Produces Clinically Acceptable Chest X-Ray Reports
A South Korean study finds that AI-generated chest x-ray reports are nearly as clinically acceptable as radiologist-written reports under standard criteria.
•Radiology Business
AI-Generated Reports Cut Radiology Reading Times and Gain Acceptance
AI-generated reporting significantly reduces radiologists' reading times and increases report acceptability over time.