(APRIL 11, 2018) – The potential of using machine learning techniques in medicine is immense. As electronic health records have become widely available, there is hope that machine learning will improve diagnosis and care. However integrating these new methodologies into medical practice is challenging. New methods need to meet healthcare standards, for example around doctor accountability and patient privacy, and must be smoothly integrated into clinical decision making practices.
We had the pleasure of speaking to Arthur Papier who has been working on problems like these for decades. A dermatologist by training, he started working with electronic health records in the 1980s and launched a clinical decision support tool called VisualDx at the turn of the millennium. VisualDx aids physicians in exploring all diagnostic possibilities through visual clues. The tool combines a search through a database of which symptoms and findings convey which diagnoses with images of how the disease in question looks on skin, eyes, mouth and in radiography.
Click here to read the full interview with PLOS ONE’s Leonie Mueck.
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