The Rise and Fall and Rise of AI
A few years ago, we witnessed the heralding of the imminent rise of artificial intelligence (AI) to support medical information services provided through industry-based contact centers. And, in some cases, the message Lonnie Corant Jaman Shuka Rashid Lynn (better known as Common) shares in recent Microsoft commercials is our reality: we do have more power at our fingertips than generations before us. But witnessing the rise of AI and actually experiencing it are two different realities. In retrospect, for medical information services these emerging technologies were overhyped when first introduced to the market in terms of how feasibly they could be applied in practical applications capable of creating a positive return on investment. Many larger companies invested in Machine Learning ‘pilot’ programs that appeared to produce less than positive outcomes, and the term “over innovating” entered the corporate lexicon.
The gold rush towards AI has been fueled by exaggerating its capabilities and overgeneralizing the ways the technology could be applied in real world settings. Well-meaning experts talked about the amazing potential of AI – from improving patient outcomes, providing better engagement between healthcare providers and patients, and increasing compliance and efficiency – without clarifying the current limitations. Simply, expectations were created that could not possibly be met by the nascent technologies available at the time, and collectively as an industry we got caught up in this frenzied environment – missing the real opportunities inherent in what the technology could actually accomplish.
And yet, regardless of these setbacks, steady progress in the field of AI has been made over the past several years. The technology has matured, slowly but surely, and through incremental improvements has emerged from the ashes of numerous failed initiatives with impressive gains over its overhyped predecessors. There are numerous successes in sectors within the healthcare industry. For example, AI has taken wearables to the next level. You don’t need to look any further than your wrist: smart watches and fitness trackers. A recent report by Global Market Insights, covered this past July by WDTNN, shares that the AI empowered wearable market is poised to hit $180B by 2025. Viz.ai is using AI to detect stroke. They are using AI to synchronize stroke care and envision using AI to improve access to life-saving therapies.
At the same time, many manufacturers are cautiously returning to the table to sponsor new projects based on AI. The FDA is considering a total product lifecycle-based regulatory framework for AI and machine learning technologies. To review their proposed framework, download the report here: https://www.fda.gov/media/122535/download.
Challenges to AI in Provision of Medical Information Services
Regulated companies, who tend to be slow adopters to begin with, waited and watched as other industries gambled with AI investments. In truth, even if these emerging technologies had proved to be extremely successful, it is likely that regulated industries would still have hesitated to adopt them simply due to the regulatory and perceptual hurdles involved in implementing new technologies in this space. One of the main challenges with AI powered technologies for FDA regulated companies is a process known as computer system validation (CSV), a combination of risk assessments and software testing documents designed to prove that the software poses no risk to patient safety or quality of care, is fit for use in a regulated setting, and produces information or data that meet a set of predefined requirements.
Computer system validation is centered on the concept of predictability in the behavior of the system being validated. When creating validation test scripts (which are based on the authorized system requirements) the two key columns are the expected results and the actual results.
