The Ledger
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Turakhia: Fully autonomous medical AI faces societal, not technical, barriers
“That's a societal issue. That's not a technical hurdle at this point.”
Turakhia: Continuous learning in medical AI risks patient harm from biased data
“Bad data could heavily bias the system and cause harm, right? So if you start learning from bad inputs that come into the system for whatever reason, you could intentionally or unintentionally, you know, cause harm.”
Turakhia: Missing wearable data is the strongest predictor of illness
“In fact, the biggest predictor, Of someone getting ill with a lot of wearable studies is missing data because they were too sick to wear the sensor.”
Turakhia: AI cannot predict acute heart attacks days in advance
“You can predict a cumulative probability, like a probability of getting condition X or diagnosis X over a time horizon of five or 10 years. But we are nowhere near saying, you know, you're going to have a heart attack in the next three days.”
Turakhia: Neural networks replicate human error patterns in EKG and imaging studies
“Some of the most promising aspects of the imaging studies and the EKG studies are that the confusion matrices, the way humans misclassify things is recapitulated by the convolutional neural networks.”
Turakhia: FDA's Digital Health Office effectively mitigates regulatory risk
“The regulatory risk thing is being largely addressed by this new Office of Digital Health and the FDA, and they're really doing, seem much more forward thinking about it.”
Turakhia: No standardized quality improvement metrics exist for EKG interpretation
“There's actually no standardized metrics for QI in any of this.”
Turakhia: Clinical documentation terminology varies between individual hospitals
“In natural language processing that's embedded in AI, the lexicon that people use, how doctors and clinicians write what it is that they're seeing with their patient is different from not even specialty to specialty, but hospital to hospital, sort of mini subc…”