Artificial Intelligence has been playing a robust and growing role
in the world the past few decades.
One major area AI is growing rapidly is the medical field;
specifically, in diagnostics and treatment management. As there
is a fear of Artificial Intelligence surpassing human tasks and
ability, there is significant research as to how AI can aid in
clinical decisions, support human judgement and increase
treatment efficiency.
There are various magnitudes of AI in healthcare. Many times, AI
utilizes a web database allowing doctors and practitioners to access
thousands of diagnostic resources. As doctors have been deeply
educated in their field and are current with present research, the
use of AI greatly increases a faster outcome that can be matched
with their clinical knowledge. Artificial Intelligence presents many
fears, especially in the clinical setting, of eventually replacing
or reducing the need for human physicians. However, recent research
and data has shown that it is more likely this tool will benefit and
enhance clinical diagnostics and decision making rather than reduce
clinician need.
Many times, a patient can present multiple symptoms that can
correlate with various conditions by both genetic and physical
characteristics, which can delay a diagnosis. So, not only does AI
benefit a practitioner in terms of efficiency, it provides both
quantitative and qualitative data based on input feedback, improving
accuracy in early detection, diagnosis, treatment plan and an
outcome prediction.
The ability for AI to “learn” from the data provides the opportunity
for improved accuracy based on feedback responses. This feedback
includes many back-end database sources, input from practitioners,
doctors, and research institutions. The AI systems in healthcare are
always working in real time, which means the data is always
updating, thus increasing accuracy and relevance. Assembled data is
a compilation of different medical notes, electronic recordings from
medical devices, laboratory images, physical examinations and
various demographics. With this compilation of endlessly updating
information, practitioners have almost unlimited resources to
improve their treatment capabilities.
AI Machine Learning Provides More Targeted Diagnostics
With various amounts of healthcare data out in the field, Artificial
Intelligence must efficiently sort through the presented data in
order to “learn” and build a network. Within the realm of healthcare
data there are two different types of data that can be sorted;
unstructured and structured. Structured learning includes three
different types of techniques including Machine Learning Techniques
(ML), a Neural Network system, and Modern Deep Learning. Whereas,
all unstructured data uses Natural Language Processing (NLP).
Machine Learning techniques use analytical algorithms in order to
pull out specific patient traits, which include all the information
that would be collected in a patient visit with a practitioner.
These traits, such as physical exam results, medications, symptoms,
basic metrics, disease specific data, diagnostic imaging, gene
expressions, and different laboratory testing all contribute to the
collected structured data. Through machine learning, patient
outcomes can then be determined. In one study, Neural Networking was
utilized in a breast cancer diagnostic process sorting from 6,567
genes and paired with texture information inputted from the
subjects’ mammograms. This combination of logged genetic and
physical characteristics allowed for a more specific tumor indicator
outcome.
The most common type of Machine Learning in a clinical setting is
known as supervised learning. Supervised learning uses the physical
traits of the patient, backed with a database of information (in
this case breast cancer genes), to provide a more targeted outcome.
Another type of learning used is Modern Deep Learning, which is
considered to go beyond the surface of Machine Learning. Deep
Learning takes the same inputs as Machine Learning, but feeds it
into a computerized neural network; a hidden layer that further
files the information to a more simplified output. This helps aid
practitioners that may have multiple possible diagnoses narrow down
to one or two outcomes; thus, allowing the practitioner to make a
more definitive and concrete conclusion.
Similar to the structured data processes is Natural Language
Processing, which focuses on all of the unstructured data in a
clinical setting. This type of data is from clinical notes and
documented speech to text processing when a practitioner sees a
patient. This data includes narratives from physical examinations,
laboratory reports, and exam summaries. The Natural Language
Processing uses historical databases that have disease relevant
keywords aiding in the decision-making process for a diagnosis.
Using these processes can provide a more accurate and efficient
diagnosis for a patient, which in turn saves time for the
practitioner, and more importantly can speed up the treatment
process. The faster, more targeted and specific the diagnosis, the
sooner a patient can be on the road to recovery.
Artificial Intelligence Integrated in Major Disease Areas
With cardiovascular, neurological disorders and cancer consistently
being the top causes of death, it is imperative that as many
resources as possible are being utilized to aid in early detection,
diagnosis and treatment. The implementation of artificial
intelligence provides benefits in early detection by being able to
pinpoint any risk alerts a patient may have.
One study involving patients at risk for stroke used AI algorithms
based on their presented symptoms and genetic history to place them
in an early detection stage. This stage was movement based, where
any abnormal physical movement in the patient was documented and
would trigger an alert. This trigger alert allowed for practitioners
to get patients to a MRI/CT scan sooner for a disease evaluation. In
the study, the early detection alert provided 87.6% accuracy in a
diagnosis and prognosis evaluation. That said, the practitioners
were able to implement treatment sooner and predict whether the
patient had a higher possibility of future stroke. Likewise, machine
learning was used in 48-hour post-stroke patients gaining a
perdition accuracy of 70% whether the patient may have another
stroke or not.
Telehealth: Artificial Intelligence on a Smaller Scale
Although Artificial Intelligence is used for high-risk diseases and
on a larger scale, telehealth tools are being implemented into homes
of patients to help treat and prevent high-risk situations while
reducing hospital readmissions. Telehealth tools allow for different
metrics to be taken, documented and processed much like a more
expansive AI machine. This equipment can notify practitioners
immediately when a patient reports a high-risk variable. Early
detection, faster diagnostics, and an updated treatment plan, reduce
time and money for both the patient and hospital, while getting more
immediate care. Artificial Intelligence is allowing practitioners to
make more efficient and logical decisions, bettering the care for
patients as a whole; which in the end, is the ultimate goal.