Artificial intelligence (AI) adoption in the global healthcare industry is growing as the technology is increasingly able to tackle complex problems across diverse areas of the industry.
For example, researchers at the Mayo Clinic have developed an AI capable of diagnosing hypertrophic cardiomyopathy, a thickening of the heart that can lead to improper blood pumping. Another group, comprising researchers from Regenstrief Institute, Georgia State, Albert Einstein College of Medicine and Solid Research Group, developed an AI capable of predicting Alzheimer’s disease and dementia risk through scanning electronic health record data. IBM’s Watson Health cognitive computing platform is capable of aiding clinicians in making clearer diagnoses based on patient data and published scientific literature.
In healthcare settings, AI must be highly trained, transparent and accurate in order to successfully enhance health service offerings for both patients and clinicians. However, this can be difficult to achieve with AI working across varied sectors, such as diagnoses, predictive health and clinician decision-support.
Healthcare data sets are becoming increasingly rich and complex. Along with this trend, AI must also become increasingly sophisticated in order to diagnose diseases or predict negative health outcomes.
In February, the Consumer Technology Association (CTA), in conjunction with a panel of leading industry players, released a set of standard definitions for key concepts underpinning AI and specifically AI in healthcare settings. These definitions will serve as a powerful standard starting point for all players looking to develop AI-based solutions in the healthcare space.
As these standards become adopted by the industry, they will enable industry players to tackle emerging AI issues, including bias and ethics in a unified fashion. Healthcare AI-based standards, such as those released by the CTA, are expected to contribute to the sustained uptake of AI-based solutions in the healthcare space and to further bolster future trust between clinicians, AI and positive patient outcomes.