The use of artificial intelligence in healthcare is potentially revolutionary. But the field also has its share of challenges. A theme that was discussed during the conference “Digital, artificial intelligence, doctors and patients” of the Global Health conference in Strasbourg.
Digital in the hospital is making great strides. Some algorithms are already able to diagnose lung cancer or analyze a mammogram better than a doctor. This technology promises huge advancements in the future, provided certain challenges are met. Several players and analysts from the field gathered during the round table”Digital, artificial intelligence, doctors and patientsof the Global Health conference in Strasbourg, analyze the future challenges that the world of technology and the world of health will have to face together.
Data, yes, but quality
“To have a good artificial intelligence, it must work with a good algorithm. And a good algorithm is built with good dataexplains Bernard Nordlinger, a member of the National Academy of Medicine and a specialist in artificial intelligence in health. Health data, collected by hospitals and health structures, must be calibrated, relevant and “cleansed” of any parasitic details that would distort their interpretation.”We remember the artificial intelligence Watson, created by IBM, which had disappointed many, especially since the health data used was not ideal.”
Scientists working with this program explained that they had great difficulty understanding patients’ medical records: acronyms to be detailed, errors to correct, abbreviated sentences. Each piece of information must first be formatted in order to be properly analyzed by the system. “The problem was the same during the Covid-19 crisis. Artificial intelligence could not find its place because the data generated at the time was not of high quality“, regrets the specialist. On the other hand, if the data are accurate and well-formulated, they can even get their place in a clinical trial. Today there are control groups, that is, the arm of a clinical trial that does not take […]
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