Copyright © 2020 pubrica. All rights reserved 1

Uses of Artificial Intelligence (AI) in Measuring the Impact of Research

Dr. Nancy Agens, Head,

Technical Operations, Pubrica

sales@pubrica.com

In Brief

Asked 20 years ago whether self-driving

cars or identification by retinal scanning

would be feasible, there likely would have

been a collective “Dream on!”. And yet,

these are not only our present day reality,

they represent only the icing on the cake.

From Siri to Alexa and Tesla, interactions

with machine-based artificial intelligence

or AI permeate in our daily lives. Netflix

and Amazon serve as our most loyal

personal shoppers, always knowing just

what else we may wish to view or purchase.

Could machines come to serve as our

personal doctors, where life and death

hang on the line?

To get to an answer, we can assess

how AI has impacted medical research,

what strides have been made in converting

research & development into

commercialized AI-based technology. We

can then begin to truly evaluate whether in

another 20 years, AI might displace

physicians or more benignly, serve as their

personal assistants.

I. BASICS of AI

AI is essentially a branch of

computer science whereby data are collected

and algorithms created based on patterns

found across the data using deep machine

learning or neural networks. The output may

be a diagnostic, prognostic or disease

prediction that appears as if a human had

analyzed the data and determined the output,

all at a fraction of the time it would take a

human to complete. For AI to be reliable, it

is absolutely critical that the data numbers

be high and of sufficient breath and quality

to avoid skewed, biased results that are not

generalizable (Sinz, Pitkow, Reimer, Bethge,

& Tolias, 2019). Otherwise, we’re left with

garbage in, garbage out. The AI field has

matured wherein the scope and quality of

training data, augmentation of data and

enhanced computational power have

resulted in ever more precise output,

enabling processes that are particularly

repetitive, ripe for AI (Gardezi, Elazab, Lei,

& Wang, 2019).

II. AI IN MEDICAL RESEARCH

Several areas of medicine have been

particularly amenable to AI based on the

sheer volume of data readily available:

radiology, ophthalmology and pathology

(Ahuja, 2019; Gardezi et al., 2019). The

data are derived from the vast numbers of

patient-derived images and recordings that

these medical segments collect to make

diagnoses: from X-rays to CT scans, MRI

imaging, retinal imaging and tissue

histology images.

The field of opthalmology has

pioneered the way with the first-ever FDA

approved AI-medical device that was

granted to IDx based on the autonomous

analysis of 900 patient retinal images. Their

diagnostic, the IDx-DR retinal imaging

device, can detect higher than mild levels of

diabetic retinopathy (DR) in diabetic

patients with an accuracy of 87.4%

(Abràmoff, Lavin, Birch, Shah, & Folk,

2018). It is the first authorized medical

device screening tool that does not require a

clinician to interpret retinal images for (DR)

(FDA, 2018).

Copyright © 2020 pubrica. All rights reserved 2

Another compelling example is AI

applications in radiology, specifically breast

mammography diagnostics. Based on

100,000 breast mammogram images,

Google’s health research arm very recently

announced that their AI-trained software

resulted in 5.7% fewer false positive and 9.4%

fewer false negative rates than trained

radiologists (Collins, 2020). While their AI-

software has not been approved yet by the

FDA for diagnostic purposes, the results are

proving out the revolutionary impact of AI

in medical research.

AI in pathology has also made

strides on the research and development

front, particularly in cancer diagnostics

given its extensive dependence on (digitized)

tissue morphology. The push for some form

of automatized assistance comes partially

from interobserver (aka pathologist)

variability in the analysis of H&E stained

tissue and the sheer volume of images

(Harbias, Salmo, & Crump, 2017)Advances

in AI-research by Philips led the FDA to

grant approval of their IntelliSite Pathology

Solution, the first ever whole slide review

imaging system to be marketed (FDA, 2017).

While pathologists are still required to

review and interpret the images, they can do

so from digitized images rather than tissue

samples.

III. WHAT’S ON THE HORIZON

The market has been bullish on AI

medical R&D being translated into

commercial products. In 2016, the lion’s

share of AI-based investments went to the

healthcare sector over other sectors (CB

Insights Research, 2017). The appetite for

AI-based medicine continues to increase at a

rate of 40% and is expected to top $6.6

billion by 2021 (Frost & Sullivan, 2020).

With funding supporting AI R&D and a

marketplace appearing ready to adopt,

discussions abound over the implications of

AI for physicians in the workforce. The

doomsday scenario that they would be

replaced by machines is a fair concern. Just

take a look at the IDX-DR case:

opthalmologist are no longer required to

screen for diabetic retinopathy in instances

where the IDX-DR screening tool is used.

Other the other hand, AI-based tools can be

relegated to high volume repetitive

workloads and facilitation of clinical

workflows without impacting the billable

reimburseables.

There may likely be some shifts in

the physician workforce, but the optimist in

me believes that AI can be leveraged to

create new opportunities for physicians. By

relegating more of the routine, repetitive

workload to AI, it could importantly provide

precious time back to physicians staving off

physician burnout, a true modern day

symptom afflicting many overworked

providers. This could ultimately translate

into more face time with patients -- “yes, the

doctor is in.”

REFERENCES

[1] Abràmoff, M. D., Lavin, P. T., Birch, M., Shah, N., &

Folk, J. C. (2018). Pivotal trial of an autonomous AI-

based diagnostic system for detection of diabetic

retinopathy in primary care offices. Npj Digital

Medicine, 1(1), 39. https://doi.org/10.1038/s41746-

018-0040-6

[2] Ahuja, A. S. (2019). The impact of artificial intelligence

in medicine on the future role of the physician.

PeerJ, 7, e7702. https://doi.org/10.7717/peerj.7702

[3] CB Insights Research. (2017). Healthcare Remains The

Hottest AI Category For Deals. Retrieved February

14, 2020, from

https://www.cbinsights.com/research/artificial-

intelligence-healthcare-startups-investors/

[4] Collins, K. (2020). Google Health’s AI can spot breast

cancer missed by human eyes. Retrieved February

14, 2020, from https://www.cnet.com/news/google-

healths-ai-can-spot-breast-cancer-missed-by-human-

eyes/

[5] Frost, & Sullivan. (2020). From $600 M to $6 Billion,

Artificial Intelligence Systems Poised for Dramatic

Market Expansion in Healthcare. Retrieved February

14, 2020, from https://ww2.frost.com/news/press-

releases/600-m-6-billion-artificial-intelligence-

systems-poised-dramatic-market-expansion-

healthcare/

[6] Gardezi, S. J. S., Elazab, A., Lei, B., & Wang, T.

Copyright © 2020 pubrica. All rights reserved 2

(2019). Breast Cancer Detection and Diagnosis

Using Mammographic Data: Systematic Review.

Journal of Medical Internet Research, 21(7),

e14464. https://doi.org/10.2196/14464

[7] Harbias, A., Salmo, E., & Crump, A. (2017).

Implications of Observer Variation in Gleason

Scoring of Prostate Cancer on Clinical Management:

A Collaborative Audit. The Gulf Journal of

Oncology, 1(25), 41–45. Retrieved from

http://www.ncbi.nlm.nih.gov/pubmed/29019329

[8] Sinz, F. H., Pitkow, X., Reimer, J., Bethge, M., &

Tolias, A. S. (2019). Engineering a Less Artificial

Intelligence. Neuron, 103(6), 967–979.

https://doi.org/10.1016/j.neuron.2019.08.034