«Building great AI models in healthcare and life sciences requires lots of data that is diverse, well-labeled, and spans across different patient types.»

«However, as AI gains traction, there are still a number of bottlenecks that slow down the process of developing robust AI models such as patient privacy, access to data, and lack of clinical expertise to annotate data for training.»

«In order to overcome these barriers, data scientists and developers have developed new solutions such as federated learning paradigms, AI models that require less labeled data to reach state-of-the-art performance, and AI models that generate synthetic clinical data which can be used to understand disease progression across age, gender, and ethnicity.»

«Europe’s landmark GDPR regulations are templates for healthcare AI, but governments will need to go much further.  Things to consider include educating patients on how their data is deidentified, stored, and used in building AI models. Patients would feel more at ease understanding the security of their data, plus understanding the value that their data plays in better patient care and treatment.»

Article written by Vanessa Braunstein.



Healthcare IT Today