AI may be increasingly ready for healthcare, but is healthcare anywhere near ready for AI?

In broad terms, that’s the question Lee Schlenker, Professor of Business Analytics and Community Management, took up in a recent column at the tech community site Towardsdatascience.

As he sums the not-so-distant future of the relationship between AI and healthcare, AI “is at the core of a major evolutionary transition that is progressively blurring the distinctions between biology, technology, and society. This interdependence challenges the traditional boundaries of mortality, morbidity, and healthcare.”

Challenges, indeed, as Schlenker proceeds to lay out the myriad categories of healthcare that are inevitably going to change, as well as the array of questions those changes are going to bring with them.

For example, he asks, How will health analytics condition how the medical profession defines well-being?

After all, he points out, “Health analytics modifies the systemic relationships between patients, physicians, and health institutions. The Quantified Self movement, or ‘self-knowledge through numbers’, suggests that individuals have the primary responsibility for improving their own physical, mental and emotional well-being. . . Recently, the widespread adoption of wearable (the Fitbit, the Apply Watch, UV Sense…) and ambient technologies (Smart Pills, Virtual Voice Assistants, Olfactory technologies…) has fueled this trend.”

Consequently, he asks, Should the medical profession focus solely on the patients they treat or be incentivized for the well-being of the populations they serve?

Moving more specifically into the impacts of AI, Schlenker argues that “although technology’s impact on medical practice has never been neutral, the progressive introduction of artificial intelligence will test the qualifications of the medical profession. The variety and sophistication of algorithms available for descriptive and prescriptive analytics grow exponentially as machine learning applications in the life sciences mature. Both hospitals and physicians can now leverage AI diagnoses without having to bear the costs and time constraints of retaining more trained professionals.”

There is, however, a downside to this development, he says.  Specifically, “in normalizing the use of machine learning, the practitioner may never take the time to study the model, the code, nor the training data used to establish diagnostic thresholds.”

So, he asks, To what extent does the medical profession need to understand how AI changes medical practice?

For Schlenker, the upshot is that the medical profession needs to understand how Data Science is changing the nature of healthcare. “Future innovation will depend upon mixing and matching human and machine expertise to recognize the contributions of multiple forms of intelligence. The medical profession must look beyond the limits of AI to encourage practitioners to do more, rather than less, in developing our future well-being.”