As Deep Learning Comes For Medicine How Do We Work Around Its Brittleness?

Deep learning is revolutionizing medicine. Algorithms are increasingly doing everything from triaging medical imagery to predicting treatment outcomes. Yet as hospitals undergo the same AI revolution affecting other fields, the dangers of AI bias and errors and the life-or-death consequences of medicine lends unique risk to these experiments, suggesting caution.

One of the fastest-growing uses of AI in medicine today is the analysis of medical imagery. Human analysis of imagery is slow, difficult to scale and error-prone. Replacing or augmenting human analysis with algorithmic analysis could even eventually allow medical imaging devices to diagnose patients in real-time as they are being imaged and direct technicians to collect additional imagery to narrow the diagnosis while the patient is still lying the imaging system.

The problem is that today’s correlative deep learning systems require vast amounts of extremely diverse training imagery, which can be hard to acquire in hospital settings where there may be more uniformity in patient conditions, demographics and imaging systems. Most dangerously, AI algorithms can easily learn characteristics unrelated to the actual disease itself, lending to false positives and negatives that can cause adverse patient outcomes or even death.

The full Forbes article can be viewed at this link.