In recent years, the integration of artificial intelligence (AI) in the healthcare and life sciences (HLS) industry has surged, promising transformative impacts on patient care, research, and operational efficiency. At the recently held 2024 Healthcare Information and Management Systems Society Global Health Conference and Exhibition, Microsoft announced that it will be further integrating AI in its applications through new collaborations to facilitate innovation within the medical device space. The initiative is expected to contribute to the forecasted 31% compound annual growth rate (CAGR) in the AI healthcare space between 2022 and 2027, according to GlobalData, a leading data and analytics company.
GlobalData’s thematic report on “Artificial Intelligence in Medical,” reveals that the AI healthcare space was worth $4.8 billion in 2022 and is forecast to reach $18.8 billion in 2027.
Joselia Carlos, Medical Device Analyst at GlobalData, comments: “The medical devices industry is highly regulated and, as a result, can be slow to adopt new technologies. However, leaders in the medical devices industry are realizing the benefits AI can bring, such as aiding physicians in making faster and more reliable diagnostic decisions, data management, and the potential of remote surgery. Seeing how AI brings several benefits, many physicians are eager to incorporate AI into their practices.”
Despite the immense potential AI has in the HLS sector, several challenges and considerations must be addressed to ensure its responsible and effective adoption. These include concerns regarding data privacy and security and the need for robust regulatory frameworks to govern AI applications in healthcare. That is why Microsoft joined a group of healthcare leaders to form the Trustworthy and Responsible AI Network—one of the first health AI networks that implements principles aimed at improving the quality and safety of AI in the healthcare space.
Carlos continues: “AI systems collect personal data to train AI models to personalize the patient experience. In essence, the more data an AI model has, the better it will learn and perform for a patient. However, this creates a huge privacy concern, especially since these AI models will be accumulating personal information. Data that is not adequately safeguarded could result in a series of phishing attacks and data breaches.”
Carlos concludes: “Looking ahead, the integration of AI in the HLS industry is poised to continue its upward trajectory. Collaborations between HLS leaders, such as the network formed by Microsoft, are necessary for overcoming barriers, ensuring ethical and responsible AI implementations, and harnessing medical innovation. Despite the potential challenges associated with AI integration, AI will certainly be a key driver of healthcare innovation.”
