Giving Compass' Take:

• This Forbes post explores the implementation of artificial intelligence (AI) in medicine, and explains why doctors — along with many drug companies — are often resistant.

• AI is challenging to apply to clinical care or drug discovery, but the takeaway here is not to force the issue. Advocates for such technological advancement need to be cognizant of experienced practitioners.

• Here are ways to build a high-impact medical philanthropy portfolio.

At long last, we seem to be on the threshold of departing the earliest phases of AI, defined by the always tedious “will AI replace doctors/drug developers/occupation X?” discussion, and are poised to enter the more considered conversation of “Where will AI be useful?” and “What are the key barriers to implementation?”

As I’ve watched this evolution in both drug discovery and medicine, I’ve come to appreciate that in addition to the many technical barriers often considered, there’s a critical conceptual barrier as well — the threat some AI-based approaches can pose to our “explanatory models," our need to ground so much of our thinking in models that mechanistically connect tangible observation and outcome. In contrast, AI relates often imperceptible observations to outcome in a fashion that’s unapologetically oblivious to mechanism, which challenges physicians and drug developers by explicitly severing utility from foundational scientific understanding.

Read the full article about the future of AI in healthcare by David Shaywitz at Forbes.