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The success of AI will depend on validated public data sets

por John W. Mitchell, Senior Correspondent | December 09, 2019
Artificial Intelligence Health IT Risk Management

Dr. George Shih, associate vice chair for informatics at Weill Cornell Medicine Radiology, noted there is a shortage of physicians worldwide, and lifesaving medical exams go unread as a result. AI, Shih said, is transforming medicine. He characterized the current AI imaging environment as an academic and industry gold rush measured by AI interest, sessions, and competitions.

Data and privacy concerns related to AI data sharing need to be top of mind, as well as rooting out data bias, which Shih said is prevalent in data sets. He noted that healthcare systems worry about the legal risk of sharing data, and some even consider not sharing to be a competitive advantage. He cited the federated learning model where an algorithm in development is brought to the data set in-house, rather than shipping data to an external AI developer.

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Another panel member, Dr. Laila Poisson, biostatistician at Henry Ford Health System, noted that it’s always best to design a data study with the end in mind. She cited 22 Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) factors. TRIPOD will improve reporting for studies developing, validating, or updating a prediction model. AI developers also need to consider if the algorithm can work under both ideal and unusual conditions.

The final speaker, Dr. Arie Meir, product manager for Google Cloud Healthcare and Life Sciences, reviewed the company’s effort to pioneer de-identification (de-ID) methods to remove patient identifiers. The goal, he maintained, is a meaningful balance between the raw data and full deletion.

For example, if a patient texts a primary care doctor with a complaint of fever and includes her phone number and Social Security Number (SSN), the only number to be de-identified is the SSN — not her phone number and temperature. The algorithm allows the phone number to show so that the clinician can call the patient at their request, but blocks the SSN in case the message gets intercepted or hacked.

Imaging, he said, has similar challenges in what info needs to be de-identified to be useful. He cited Google's work with a large veterinary company that used an AI algorithm to organize a 90-second image sequence for its animal radiologists. The platform saved over 1 million dollars annually in staff time and improved work satisfaction.

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