The AI Revolution in Diagnosis Report

Breaking through the triple bind of clinical trust

The human and financial costs of diagnostic error are more than evident, while the in-principle improvements of AI-DDS on quality of patient care – including accuracy, consistency, equity and resource efficiency – are immense.

Nevertheless, three widespread clinician concerns inhibit the trust that is fundamental, not only to a willingness to consider and trial new AI-DDS solutions, but to acceptance and adoption of the significant long-term changes in both day-to-day workflow and after-market monitoring that these very different models require.

• Regulatory complexities;
• Clinical evidence;
• The “black box” of AI/ML.

In this unfamiliar context, how can developers best engage with and support clinicians – and vice versa – to bring these critical innovations through development and approval to application?

The report has been researched and written by GT LifeScience CSO Michael Bayler, with invaluable expert commentary from Jan Wolber of GE Healthcare, Scott Patterson of Baycare Health, Allison Morgan of Metis Clinical, Enes Taylan, and GT LifeScience CTO John Harris.

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