Some AI services are already working in healthcare. They evaluate thousands of radiologic studies every day. It seems clear that radiologists should review some images to monitor the quality of AI services work. Please share your thoughts on how to calculate the minimum sufficient number of images for revision?
@Lev Pestrenin
Monitoring the quality of AI services in radiology is crucial for ensuring patient safety and accurate diagnoses. Here are some key strategies to consider:
Data-driven Monitoring:
Performance Metrics: Track key performance indicators (KPIs) specific to the AI service being used. These could include accuracy (sensitivity, specificity), turnaround time, agreement with radiologist findings, and identification of false positives/negatives.
Ground Truth: Compare AI outputs with interpretations from qualified radiologists on a subset of cases. This "ground truth" data helps assess the AI's accuracy and identify potential biases.
Benchmarking: Compare the AI's performance against established benchmarks or reference standards for specific radiological tasks.
Clinical Integration Monitoring:
Radiologist Feedback: Gather feedback from radiologists regarding the AI's usefulness, ease of integration into workflow, and impact on diagnostic confidence.
Clinical Outcomes: Monitor the impact of AI on patient outcomes, such as accuracy of diagnoses, treatment decisions, and patient morbidity/mortality (when ethically feasible).
Technical Monitoring:
Data Quality: Monitor the quality of data used to train and run the AI model. Look for biases, inconsistencies, or missing data that could affect performance.
Model Drift: Track the AI model's performance over time to detect any degradation in accuracy (drift) that might necessitate retraining.
System Uptime and Security: Monitor the AI system's uptime and ensure proper security measures are in place to protect patient data.
Additional Considerations:
Transparency and Explainability: Choose AI models that provide some level of transparency in their decision-making process. This allows radiologists to understand why the AI suggests a particular finding, fostering trust and collaboration.
Regulatory Compliance: Ensure the AI service is compliant with relevant regulations regarding data privacy, safety, and algorithmic bias.
Continuous Improvement: Establish a feedback loop to continuously improve the AI service based on data, clinical experience, and user feedback.
By employing a combination of these strategies, healthcare providers can effectively monitor the quality of AI services in radiology, promoting trust in the technology and ultimately improving patient care.
For the Quality of AI we need :
FDA theoretical framework to implement & evaluate human centered AI.
To monitor the AI in Radiology we should have an (AI specialist ) ,a Radiologist,a Medical physicist and a Radiation health physicist , radiation technologist .
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