The Food and Drug Administration’s (FDA) five-part artificial intelligence (AI) / machine learning (ML) based Software as a Medical Device (SaMD) action plan is set to accelerate the integration of AI and ML into healthcare systems. As a result, the industry will witness wider adoption of these digital health tools and technologies, says GlobalData, a leading data and analytics company.

Kamilla Kan, Medical Analyst at GlobalData, comments: “While AI and ML has been incredibly helpful throughout the pandemic, providing new ways to detect, analyse and trace COVID-19, the lack of regulations has been a cause for concern and slowed the pace of adoption of the emerging technologies. For instance, previously the issue of biased results was raised due to the fact that AI/ML powered algorithms could be trained on one demographic and used on a different one. Following the public’s concerns, the FDA released the action plan for regulating AI in medical devices.

“With this action plan, FDA officials will be able to evaluate and monitor a software product from its premarket development through post-market performance. While many healthcare manufacturer companies such as Phillips, Medtronic and GE Healthcare are already rapidly investing and incorporating AI and ML, this approach will enable faster integration and acceptance of these technologies into healthcare systems by reassuring healthcare professionals and the general public that the AI/ML-powered technologies has been properly tested. As a result of this, current methods of diagnosis, data analysis, image recognition and assessment will benefit greatly.”

Due to rapid implementation of AI/ML-powered technologies, the AI/ML market has been developing a fast growth over the past years. According to GlobalData forecasts, the market for AI/ML platforms will reach $52bn in 2024, up from $29bn in 2019. Integration of AI and ML into the healthcare industry has increased over the years, as previously there was little to no regulation, which caused concern and distrust towards adaptive algorithms. However, the growing burden on healthcare systems has resulted in higher adoption of digital health tools and technologies.