Globally, the healthcare industry is expected to increase AI investments by 44 percent between 2017 and 2020 (Tata Consultancy Services survey). Healthcare-focused AI tools are expected to bring in more than $20 billion by the middle of the 2020s, exhibiting a CAGR from 40 to nearly 50 percent over the next five to seven years. And, in the first nine months of 2017 alone, global healthcare Big Data analytics companies received more than $1 billion in venture capital investment, representing a 31 percent increase over funding levels at the same time in 2016 (Mercom Capital).
In this frenetic environment, life science leaders must balance their need to apply these technologies to improve patient care and bottom lines — without getting caught up in the Al hype machine. Ask yourself if it is worth investing in AI tools and infrastructure before seeing proof of their potential. You will need to balance caution with the reality that machine learning could be the next transformative force in healthcare.
In general healthcare, Al is beginning to be used for clinical decision support (CDS), offering a wide variety of clinicians and staff (and patients) access to personalized information at specified times. All of this has been done to help enhance patient outcomes. Support devices such as reminders, alerts, and evidence links to electronic medical records can guide a clinician's decision making. AI software can also assist physicians with disease diagnosis and treatment algorithms. The development and use of CDS systems should be driven by a core framework that articulates appropriate conditions for their use. These include processes for monitoring data quality, developing and validating algorithms, and sufficiently protecting patient data.
In the biopharma industry, researchers can use Al and machine-learning technologies to drive decision-making processes for new drug development and for expanding existing drugs to treatments for other conditions. Pharma leaders can also employ AI tools to expedite the clinical trials process by more quickly and accurately identifying appropriate patients from various data sources. Finally, pharma companies are using Al to predict the time, location, and likelihood of epidemic outbreaks.
Currently, a third of all healthcare AI Software as a Service (SaaS) companies are focusing partly or exclusively on diagnostics, making it one of the largest focus areas for burgeoning startups in the field. The Institute of Medicine at the National Academies of Science, Engineering, and Medicine reports that “diagnostic errors contribute to approximately 10 percent of patient deaths,” and also account for 6 to 17 percent of hospital complications.
To avoid mistakes and limit risks when making healthcare-related AI purchasing decisions, you should:
(a) confirm that your solution is clinically validated and has appropriate data privacy and security functions, and (b) plan an incremental (step by step) implementation on-site to ensure all users use the tool in a controlled manner. As time goes by and as experience grows, usage will be broader, but best practices will need to have strict usage rules initially. The Al solution may help accelerate some tasks, but human expertise may be required for the broad scope of what is needed.
Currently, Al in healthcare is in the second stage of the Gartner Hype Cycle: “the peak of inflated expectation. "However, if we don’t allow it to catch up to the hype, it may fall back into what Gartner calls the “trough of disillusionment.” To avoid this, we need to properly understand AI and its limitations so that we may use it more effectively and in areas where it can significantly improve existing processes.