The 10 Most Innovative Companies Bringing AI to Healthcare The 10 Most Innovative Companies Bringing AI to He | Page 52

analysis, detection, diagnosis, and prognosis, which goes
way beyond predicting labels of normal and abnormal
tissues.
Several research works, including the works we have done
at Stanford; recently demonstrate that Deep Learning can
replace conventional iterative algorithms for more accurate
medical imaging reconstruction from 10x faster MRI scan
or even 200x lower-dose PET scans. In addition, a lot
clinical research works in neuroradiology; showed AI
algorithms can not only categorize current disease, but also
predict how the disease will progress in the future. From
faster exams to accurate prognosis prediction, the entire
radiology workflow can be improved using AI.
Different from other AI+radiology companies and startups
focus on automating radiology diagnosis using AI, we at
Subtle Medical chose a different route to integrate AI into
clinical radiology. We provide hospitals and imaging
centers software infrastructure for AI-empowered imaging
workflow enabling at least 4x faster and safer MRI and PET
exams. We believe these AI tools will help hospitals and
imaging centers to improve their productivity, quality of the
service and patient satisfaction.
No Workflow Disruption from Disruptive AI Technology
It is no doubt that AI is a disruptive technology that will
reform the radiology practice and workflow. However, the
innovation in radiology practice, as well as in healthcare in
general, should never disrupt clinicians’ operation. There is
a saying that if the change of workflow requires radiologists
to move from their chairs, it is not going to works. AI is
supposed to free clinicians from repetitive tasks, not to add
more tasks to them. Therefore it is fundamental for an AI
product to seamlessly integrate into the entire workflow.
For example, at Subtle Medical, all the products are
designed in a way to (semi-) automatically function
between scanners and PACS, staying almost silent and
invisible to technicians and radiologists.
In general, we believe the best radiology tool should
simplify, accelerate, and prioritize tasks for radiologists,
making exams more accurate, efficient and personalized,
and improve the productivity and satisfaction for both
patients and clinicians. AI products have potentials to
achieve all of these requirements.
Super-human Capability of AI
such as Deep Learning, have great potentials in tasks
human professionals are not good at as well. For example,
in identifying diagnosis disease subtypes, similar to fine-
grained image classification of identifying two similar dog
breeds in ImageNet challenge, AI algorithms can do better
than human and resolve inter-reader variability. In addition,
for all the repetitive and quantitative measurements in
radiology, such as contouring, segmentations, measuring
brain thickness, and recording tumor size changes, AI
algorithm can not only free clinician from these time-
consuming mundane tasks but also get more accurate
results. In addition, quantitative biomarkers and quantitative
parameter mapping is a new trend in medical imaging as it
offers better objective criteria for clinical decision, where
Deep Learning is shown to improve for MRI.
Last but not least, new algorithms lead ways to correlate
radiology with large scale genetic datasets, which is too
complicated that human can never be good at but AI. All of
these new advances from AI can contribute to personalized
and precision medicine in future radiology.
Still a Long Way to Go
Although AI in radiology has achieved a lot progresses,
there are still a long way to go until it is routinely used in
clinics. In 2016, Dr. Geoffrey Hilton mentioned “it is quite
obvious that we should stop training radiologists.” Later,
several AI papers published on achieving “expert-level
performance” in diagnosis add-up the concerns that
radiologists should be worried about losing jobs. However
nowadays we know there are definitely some hypes in
“replacing” radiologists. As the developers of radiology AI,
we also understand and appreciate more that radiologists
are doing much more than image categorizing. Most
AI/Computer-Assisted-Diagnosis products are still in their
early stage for a closed-set of a few disease categories.
AI, for a foreseeable future, will not be generalized. No
human is better than a small calculator for accurate
arithmetic, but that is all that the calculator can do. Given
the long-tail effects and the lack of datasets for rare disease,
it is unlikely AI can do everything for radiologists. But, as
put by Dr. Langlotz from Stanford, “radiologists who use
AI will replace radiologists who don’t.” AI in radiology,
probably similar to AI in other popular areas such as
manufacturing and autonomous driving, will keep improve
its capability to change the entire industry. As quoted in a
recent Forbes article on robotics: AI will replace tasks, not
jobs.
Several research works show state-of-the-art AI algorithms,
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