Sheba’s ARC Innovation Center Joins TRAIN to Elevate AI Standards in Healthcare

In recent years, we have seen a rise in AI that has birthed into society a new era of innovation, efficiency, and precision. While it has prevalence in every sector, it has particularly impacted healthcare and has given patients a second chance at a quality life. Sheba’s answer to innovation has been ARC Innovation Center.

Since its inception in 2019, ARC has been a force behind numerous groundbreaking innovations and startups, including MDClone, AISAP, and TytoCare, amongst others. With AI being instrumental to ARC’s innovations, they have taken steps to ensure AI is leveraged to its highest standards of efficiency, safety, and dependability and have joined the Trustworthy & Responsible AI Network (TRAIN).

TRAIN: Pioneers in Ethical AI in Healthcare

As of today, the legitimacy of AI use in healthcare goes hand in hand with TRAIN. This unique consortium of healthcare organizations and their partners in technology are dedicated to responsible AI principles, striving to enhance the quality, safety, and trustworthiness of AI within the medical care sector.

Key organizations that are partnering with TRAIN include Adventhealth, Cleveland Clinic, Duke Health, Johns Hopkins Medicine, Mass General Brigham, Microsoft, and most recently, Sheba’s ARC Innovation Center. Why TRAIN is essential to the healthcare industry comes down to its sheer determination to make its implementation as ethically sound as possible and also dependable, secure, and fair. This is done by utilizing the following:

Quality and Safety – TRAIN is dedicated to maintaining high standards of care by rigorously testing and monitoring AI systems. Through the creation of thorough evaluation protocols, TRAIN aids healthcare providers in ensuring that AI technologies enhance patient safety rather than jeopardize it.

Building Trust – For AI applications to be trusted, transparency and accountability are crucial. TRAIN’s collaborative approach promotes a culture of openness, where best practices are shared and AI implementations are carefully documented and monitored. This transparency fosters trust among both healthcare providers and patients.

Collaborative Efforts – TRAIN’s collective expertise allows the network to tackle the complex challenges that come with AI in healthcare. By collaborating, TRAIN members can create and implement practical, scalable best practices, ensuring AI technologies benefit the entire healthcare community.

What TRAIN Strives to Accomplish

Moving forward, TRAIN will continuously strive to expand its reach and continuously improve the standards and practices for AI in healthcare. This initiative will involve ongoing collaborations, innovations, and a steadfast commitment to ethical principles.

• Expanding the Consortium – It is expected that as time goes by, more healthcare organizations will take part in the TRAIN network, which will broaden the scope of its collaborative efforts and enhance the diversity of perspectives and expertise when it comes to AI use in the healthcare sector.
• Developing Advanced Tools – TRAIN will continue to lead in the development and refining of tools for AI outcome measurement and push for healthcare providers to have the resources to implement AI technologies effectively.
• Enhancing Training and Resources – TRAIN is dedicated to providing comprehensive training and resources for healthcare professionals, helping them stay abreast of the latest developments in AI and ensuring they are equipped to manage AI implementations responsibly.

The Future of AI in Healthcare: A Call for Responsible Innovation

Prof. Eyal Zimlichman, MD, Chief Innovation and Transformation Officer at Sheba Medical Center and Founder and Director of ARC Innovation has stated the following in regard to joining TRAIN. “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.”