Sheba’s ARC Innovation Joins TRAIN: Advancing Trustworthy and Responsible AI in Healthcare
It has been recently announced that Sheba’s innovation arm, ARC Innovation, is participating in the Trustworthy & Responsible AI Network (TRAIN). Given the impact that AI is having in transforming our world, as well as in the healthcare sector, TRAIN, a consortium which groups together healthcare entities with a goal of responsibly integrating AI into the medical field, is essential.
Setting a Standard for AI Use in Healthcare
As AI begins playing a more prominent role in precision medicine, clinical decision-making, drug development and patient education, it is clear that a standard needs to be set and that strict guidelines need to be put in place. Such measures will ensure AI technology is used to its highest potential in a responsible manner.
It was only fairly recently at the HIMSS Global Health Conference that TRAIN was introduced to the masses. Some of the leading healthcare institutions, health systems and technology providers, which include Microsoft, Duke Health and Vanderbilt University Medical Center have also joined the consortium.
Professor Eyal Zimlichman, MD, Chief Innovation and Transformation Officer at Sheba Medical Center and Founder and Director of ARC Innovation has stated the following, “AI is a transforming force within healthcare, with new solutions developed daily, bringing considerable benefits to every aspect of health. In order to effectively implement AI technologies, innovation must be balanced with responsibility and trust. AI cannot be implemented without safeguards, and only through rigorous quality and safety controls can health systems adopt these new solutions.”
Changing the Landscape of the Medical Field
Ultimately, TRAIN members will work together to improve the quality and reliability of AI in healthcare by sharing best practices and facilitating the registration of clinical AI applications through a secure online portal. They will also offer resources for evaluating AI implementation outcomes, maintaining privacy at all stages. Additionally, the consortium will aid in creating a federated national AI outcomes registry to document real-world data on algorithm effectiveness, safety, and ongoing improvement.
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