Explainable artificial intelligence in healthcare by Dr. Johnson Thomas, MD, FACE, @JohnsonThomasMD

Debates are raging on social media concerning explainable AI in healthcare. Geoffrey Hinton, one of many ‘godfathers of AI’ not too long ago tweeted – “Suppose you may have most cancers and it’s important to select between a black field AI surgeon that can’t clarify the way it works however has a 90% remedy charge and a human surgeon with an 80% remedy charge.

Would you like the AI surgeon to be unlawful?”. [JT1] As you’ll be able to think about twitterverse took sides. One camp argued that healthcare AI must be explainable and different camp argued that we must always not sacrifice usefulness for explainability.   There are totally different approaches to explainability. In linear fashions we might use the burden for every variable to find out how a lot it contributes to the prediction. In medical picture classification we might use Eli5, LIME, and SHAP to elucidate the predictions. This provides one other layer of computing complexity and in turns requires extra computing assets and time.  

Why can’t we mix good accuracy and explainability in the identical software program? When confronted with a medical downside, we determined to mix the most effective of each worlds. Greater than 50 p.c of ladies over the age of fifty have thyroid nodules. Due to elevated use of imaging modalities, we’re detecting extra of those thyroid nodules. However, solely about 5 to 10 p.c of those nodules are cancerous. At current, the one solution to establish whether or not there’s most cancers in these nodules are by invasive procedures like surgical procedure and needle biopsy. We employed synthetic intelligence to create a mannequin that can assist physicians to decide on the suitable nodule for biopsy. In our research printed in Thyroid journal[JT2] , we confirmed that by using this mannequin (AIBx), pointless biopsies could possibly be lowered by greater than 50 p.c. The likelihood {that a} nodule is definitely benign when the mannequin predicts it to be benign, particularly the adverse predictive worth of AIBx was 93.2 p.c.   AIBx  finds related photos to the take a look at picture and shows these photos together with their precise prognosis. Doctor opinions these related photos and the corresponding prognosis to make the ultimate resolution. Our mannequin was created to boost the doctor’s means to decide on the suitable nodules for biopsies reasonably than exchange the doctor. Each step of this course of wants doctor’s enter and therefore we used the time period Doctor in Loop (PIL). Newest model of AIBx additionally overlays warmth maps over the take a look at picture to point out areas of curiosity that resulted within the prediction.

By combining picture similarity and warmth maps (class activation maps) we made it an explainable mannequin. This in flip will increase doctor’s belief within the mannequin.   In conclusion, utilizing an explainable synthetic intelligence mannequin helps to extend the belief within the mannequin’s predictions. A doctor refers a affected person to a surgeon based mostly on his or her belief within the surgeon. Equally, AI algorithms that enhance belief of their prediction will probably be preferentially used than black field algorithms, that is my reply to Geoffrey Hinton’s query.

For extra details about our analysis, please go to  

To learn our medical analysis article in Thyroid journal, please go to


 [JT1]  [JT2]


<strong>Johnson Thomas, MD, FACE</strong>
Johnson Thomas, MD, FACE

embody thyroid nodule, thyroid most cancers and diabetes. Dr. Thomas has printed within the fields of molecular markers, tumor induced hypoglycemia, machine studying and synthetic intelligence. He’s additionally an avid coder, his GitHub repo. Dr. Thomas obtained researcher of the yr award, 2019 from Mercy ministry and Kenneth Simcic Award for Educational Excellence in Endocrinology in 2012.

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