“Explainability” Is a Poor Band-Aid for Biased AI in Medicine
New study shows docs not great at sniffing out bad models.
OK you’re in the Emergency Room evaluating a patient who comes in with acute shortness of breath. It could be pneumonia, it could be a COPD exacerbation, it could be heart failure. You look at the x-ray to help make your diagnosis — let’s say COPD — and then, before you start ordering the appropriate treatment, you see a pop-up in the electronic health record — a friendly AI assistant that says something like “I’m pretty sure this is heart failure”.
What do you do?
This scenario is closer than you think. In fact, scenarios like this are already happening in health systems around the country, sometimes in pilot programs, sometimes with more full-fledged integration. But the point remains, at some point clinician’s diagnoses are going to be “aided” by AI.
What’s the problem with AI predictions? Well, people often complain it’s a “black box” — sure it may tell me it thinks the diagnosis is heart failure, but I don’t know WHY it thinks that. To make AI work well with clinicians, it needs to explain itself.
But a new study suggests that “explainability” of AI predictions doesn’t make much difference in how doctors use it. In fact, it may make it worse.
We’re talking about this study, appearing in JAMA, which takes a very clever approach to figuring out how AI helps — or hinders — a doctor’s diagnostic ability.
457 hospitalist physicians were presented with multiple clinical vignettes much like the one I opened with — a patient with acute shortness of breath. They had access to all the details, the medical history, and, importantly, the chest x-ray.
The docs went through 8 vignettes under a variety of scenarios. One was without any AI assistance — just to get a baseline diagnostic accuracy. On their own, the docs got about 73% of the cases correct.
Here’s where it gets interesting. The team then had four AI conditions. First, a relatively accurate AI that did not give explanations for its thinking. Second, that same AI but with explanations — in this case highlights on the chest x-ray — added. Third, a biased AI — it would always diagnose people with heart failure if their BMI was over 30, for example — without explanations. And fourth, that same biased AI but providing an explanation.
The hope, the expectation here is that forcing the biased AI to explain itself would help the clinicians realize that it was biased. Explainability becomes a safety feature of the AI model. Did it work?
Let me show you a couple of examples. Here we have the accurate AI, looking at an X-Ray, and determining, correctly in this case, that the patient has heart failure.
Importantly, the model is showing its work — it highlights the part of the chest x-ray that makes it think heart failure is going on, and we clinicians can say — sure that makes sense. Pulmonary edema at the lung bases.
Here’s the biased model predicting, quite strongly, that the patient has pneumonia. But the explanation is way off. It is highlighting part of the heart and chest wall — not the best places to diagnose pneumonia. Clinicians should be able to immediately recognize that the model is screwing up. But do they?
Clinicians’ diagnostic accuracy improved modestly when the accurate AI gave advice, with a bit more improvement seen when the advice was accompanied by an explanation.
Their performance dropped substantially when the biased AI gave advice.
This is the first concerning finding of this study. It reminds us that AI can be helpful when it comes to diagnosis, but the effects of bad or biased AI might be much worse than the benefits of good AI.
Can we mitigate the risk of a biased AI algorithm by forcing it to include those explanations — will clinicians disregard bad advice?
Not really. The diagnostic accuracy of clinicians was still harmed when the biased model explained itself. By and large, the presence of explanations did not act as a safety-valve against a biased AI model.
This should be concerning to all of us. As AI models get put into practice, it’s very reasonable for us all to demand that they are accurate. But the truth is, no model is perfect and there will always be errors. Adding “explainability” as a method to reduce the impact of those errors seems like a great idea — but the empiric data, from this study at least, suggests it is far from a solution.
A version of this commentary first appeared on Medscape.com.
