Cátedra UNESCO de Bioética



"Artificial intelligence, bias, and patients' perspectives"

The Lancet

Some of the most exciting applications of machine learning to medicine involve the kinds of data that cannot be analysed with traditional statistical models: medical imaging, waveforms, and videos. Researchers are training algorithms to take in these complex signals, and output a doctor's interpretation—eg, given a particular retinal fundus photograph, would an ophthalmologist identify diabetic retinopathy? Algorithms based on datasets that pair images or waveforms with “labels” assigned by a doctor have the potential to drive improvements in efficiency and diagnostic accuracy. However, the strength of this approach can also be its weakness: by matching the performance of doctors, algorithms will also incorporate their inherent limitations.