The state of AI in healthcare: Five key findings enterprises should know
Artificial Intelligence (AI) is revolutionizing enterprise and consumer technology as we know it, but few industries have made strides as significant as healthcare and life sciences. With its growing applications in the field, NLP is empowering hospitals, health systems, and businesses alike to accelerate research and care. From accelerating clinical trial recruitment and vaccine development, to detecting potentially life-threatening adverse drug events and forecasting hospital gridlock/staffing demands, AI is making an impact beyond the headlines.
Even in its infancy, the global AI in healthcare market size and investments in AI technologies, like Natural Language Processing (NLP), have experienced significant growth, even in the wake of pandemic-driven budget cuts. This trajectory is exciting to watch and expected to continue over the next several years, as we’ve only scratched the surface of AI’s potential. But understanding how organizations are applying these technologies, who is using them, and the challenges and breakthroughs they’re seeing in practice is vital to continuing progress in the field.
A new Gradient Flow survey, in collaboration with John Snow Labs, seeks to answer these questions and shed light on the state of AI in healthcare today. The global survey, queried users from nearly 50 countries worldwide, with more than a quarter of respondents holding technical leadership roles. From this research emerged 5 key findings that enterprise organizations should keep in mind as they plan to work with customers in the healthcare/life sciences space or embark on healthcare AI journeys of their own.
When asked what technologies they plan to have in place by the end of 2021, close to half of respondents cited data integration, while one-third cited NLP and business intelligence, respectively among the technologies they are currently using or plan to use by the end of the year. Additionally, more than one third of Technical Leaders indicated that their organizations are using—or will soon be using—data annotation tools and data science platforms. It’s clear that healthcare organizations are getting serious about putting their data to use, made easier by the advent of electronic medical records and technologies like NLP that serve as a connective tissue to make sense of siloed data sources to paint a complete picture. For example, not only can NLP compile information from EHRs, but also free-text and images such as X-Rays or other diagnostic tests that would otherwise be hard to piece together.
As AI moves from research to production, survey results demonstrate a shift from use by data scientists and technical personnel to clinicians and patients. When asked who the intended users are for AI tools and technologies, more than half of all respondents signaled clinicians among their target users. Of mature organizations, those classified as having extensive AI experience, 59 percent indicated that patients were also users of AI technologies. This further exemplifies the democratization of AI technology in healthcare and the many applications it can provide in a clinical setting. Chatbots and other interactive and automation technologies will only grow as AI matures.
