To harness the best parts of AI — and avoid its worst possible outcomes — you need an AI strategy that not only includes technical implementation tactics but also prioritizes efforts to minimize bias, address ethical considerations, and build consumer trust and confidence.
Medical algorithms will continue to preserve the racial bias that has been historically baked into the U.S. healthcare system unless the medical field stops operationalizing race in its machine learning models, according to panelists at a recent panel hosted by the Philadelphia Alliance for Capital and Technologies. They argued algorithms should use social determinants of health as a predictor of health outcomes instead of race.
Hear executives from Quantum Health, Surescripts, EY, Clinical Architecture and Personify Health share their views on digital transformation in healthcare.
Algorithms are everywhere and so are the biases behind them. As algorithms find greater use in healthcare, a panel at MedCity News’ INVEST Digital Health virtual conference addressed how and why algorithms are biased, how that bias affects clinical care, and some possible solutions to bias.
Data scientists who develop ML algorithms may not consider legal ramifications of algorithmic bias, so both developers and users should partner with legal teams to mitigate potential legal challenges arising from developing and/or using ML algorithms,