to the human pathologist—quality control systems that
ensure risky cases are not overlooked.
These and a number of computer-assisted diagnosis
applications are already impacting radiology and pathology,
providing a virtual second opinion, or suggesting areas in
scans or tissue that might require a closer look by a human
expert. On the horizon are new computer-based screening
methods that allow pathologists and radiologists to eliminate
obviously benign or straightforward cases from heavy workloads, enabling pathologists to focus on cases that need attention and possibly further testing. Soon, we may see AI-based
companion diagnostics, which enable a targeted approach to
matching patients with the perfect therapy for the profile of
their tumor—a core theme of precision medicine in cancer.
The black box fallacy
Progress from a field defined largely by academic work into
real commercial applications had happened very rapidly.
Many points along the diagnostic chain are poised for rapid
transformation. But it’s still early days, and nothing happens
overnight in healthcare. Patient data is sensitive, and human
lives hang in the balance, two defining characteristics of AI
in cancer that gate the translation of research to commercial
applications. Increasingly impactful applications and
widespread adoption will require costly and time-consuming
validation and regulatory work.
Much of the regulatory debate in applying AI to diagnostics is the cloudy nature of the underlying processes that drive
decisions in deep learning models, often considered a “black
box.” Historically, mechanisms that drive diagnostics approved
by regulatory bodies are well known and easily explained. Some
question whether deep learning models will be able to pass
through the same level of scrutiny that regulatory bodies require.
But this challenge is already being proven a surmountable one.
In early 2017, the U.S. Food and Drug Administration (FDA)
approved the first AI-based tool for use in a clinical setting.
The Arterys Cardio DL software reviews conventional cardiac
MRI images to produce ventricular segmentations in a matter
of seconds. Perhaps indicating the role future AI tools might
play in clinical applications, Cardio DL is at least as accurate as
and far faster than a human expert and provides editable results
to supplement the analysis. Since then, the FDA has approved
additional AI-based tools for assessing and diagnosing diabetic
retinopathy, wrist fractures and stroke damage.
Adoption on the horizon
Machine learning is opening up possibilities in healthcare
and cancer diagnosis. AI is now being incorporated into
tools for a wide range of clinical applications. We’re already
seeing this transformation unfold, and widespread adoption
is on the horizon.
Proscia AI visualization of skin cancer
regions of interest. Credit: Proscia
While advances in machine learning have
incrementally translated artificial intelligence
(AI) from a matter of science fiction to reality,
breakthroughs in hardware and algorithmic
techniques at the beginning of this decade
sparked an explosion of possibility.