Explainable AI in Healthcare | Dr. Johnson Thomas MD
- The debate over explainable AI in healthcare continues, sparked by Geoffrey Hinton's hypothetical scenario: choosing between a "black box" AI surgeon with a 90% cure rate and a...
- As a notable example,linear models use variable weights to determine contribution to predictions.
- to address the need for both accuracy and explainability, researchers developed aibx, an artificial intelligence model to aid physicians in selecting thyroid nodules for biopsy. More than 50%...
Explore how explainable AI is revolutionizing healthcare, building trust, and enhancing patient care. This report dives into the critical need for openness in AI models, such as the AIBx model, capable of reducing unneeded thyroid biopsies by over 50%. Learn how physician expertise remains central as AI acts as a powerful diagnostic tool,not a replacement. Discover the latest advancements in AI in medicine, including image similarity and heat maps, that empower doctors with clear, understandable insights. News Directory 3 delivers an in-depth look at the current debate surrounding the balance of AI utility and explainability, offering valuable insights into the future of medical diagnosis and patient outcomes. Understand the pivotal role of explainable AI in fostering confidence among healthcare professionals. Discover what’s next in this rapidly evolving field.
Explainable AI Model Increases Trust in Medical Role, Predictions
Updated June 24, 2025
The debate over explainable AI in healthcare continues, sparked by Geoffrey Hinton’s hypothetical scenario: choosing between a “black box” AI surgeon with a 90% cure rate and a human surgeon with 80%. This ignited discussion on whether explainability should be sacrificed for utility.
Different approaches exist for explainable AI. As a notable example,linear models use variable weights to determine contribution to predictions. In medical image classification,tools like Eli5,LIME,and SHAP can explain predictions,though this adds computational complexity.
to address the need for both accuracy and explainability, researchers developed aibx, an artificial intelligence model to aid physicians in selecting thyroid nodules for biopsy. More than 50% of women over 50 have thyroid nodules,but only 5% to 10% are cancerous. Currently, invasive procedures are needed to determine malignancy.
A study published in *Thyroid* journal showed AIBx could reduce unnecessary biopsies by more than 50%. The negative predictive value of AIBx was 93.2%, meaning when the model predicted a nodule was benign, it was highly likely to be so.
AIBx works by finding similar images to the test image and displaying them alongside their actual diagnoses. Physicians review these images to make the final decision. The model enhances, rather than replaces, physician expertise, a concept termed “Physician in Loop” (PIL). The latest AIBx version also overlays heat maps on the test image, highlighting areas of interest.
By combining image similarity and heat maps, the model becomes more transparent, increasing physician trust. This trust is vital, as physicians are more likely to use AI algorithms they understand.
What’s next
Further research will focus on refining explainable AI models to enhance diagnostic accuracy and build greater confidence among healthcare professionals, ultimately improving patient care and reducing unnecessary procedures.