Artificial Intelligence in Medicine: Today and Tomorrow
Artificial intelligence-powered
medical technologies are rapidly evolving into applicable solutions for
clinical practice. Deep learning algorithms can deal with increasing amounts of
data provided by wearables, smartphones, and other mobile monitoring sensors in
different areas of medicine.
AI has revolutionized medical
technologies and can be commonly understood as the part of computer science
that is able to deal with complex problems with many applications in areas with
huge amount of data but little theory.
Currently, only very specific
settings in clinical practice benefit from the application of artificial
intelligence, such as the detection of atrial fibrillation, epilepsy seizures,
and hypoglycemia, or the diagnosis of disease based on histopathological
examination or medical imaging.
The implementation of artificial intelligence in clinical practice is a promising area of development that rapidly evolves together with the other modern fields of precision medicine, genomics and teleconsultation.
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1. Cardiology
Atrial Fibrillation
The early detection of atrial
fibrillation was one of the first application of AI in medicine.
Cardiovascular Risk
Applied to electronic patient
records, AI has been used to predict the risk of cardiovascular disease, for
instance acute coronary syndrome and heart failure better than traditional
scales.
Pulmonary Medicine
The interpretation of pulmonary
function tests has been reported as a promising field for the development of AI
applications in pulmonary medicine.
Endocrinology
Continuous glucose monitoring
enables patients with diabetes to view real-time interstitial glucose readings
and provides information on the direction and rate of change of blood glucose levels.
Nephrology
Artificial intelligence has been
applied in several settings in clinical nephrology. For instance, it has been
proven useful for the prediction of the decline of glomerular filtration rate
in patients with polycystic kidney disease, and for establishing risk for
progressive IgA nephropathy
Gastroenterology
Artificial neural networks have
been used to diagnose gastroesophageal reflux disease and atrophic gastritis,
as well as to predict outcomes in gastrointestinal bleeding, survival of
esophageal cancer, inflammatory bowel disease, and metastasis in colorectal
cancer and esophageal squamous cell carcinoma.
2. Neurology
Epilepsy
Intelligent seizure detection
devices are promising technologies that have the potential to improve seizure
management through permanent ambulatory monitoring.
Gait, Posture, and Tremor
Assessment
Wearable sensors have proven useful
to quantitatively assess gait, posture, and tremor in patients with multiple
sclerosis, Parkinson disease, Parkinsonism, and Huntington disease
3. Computational Diagnosis of
Cancer in Histopathology
Paige.ai has received breakthrough
status from FDA for an AI-based algorithm that is capable of diagnose cancer in
computational histopathology with great accuracy, allowing pathologist to gain
time to focus on important slides.
4. Medical Imaging and Validation
of AI-Based Technologies
A long-awaited meta-analysis
compared performances of deep learning software and radiologists in the field
of imaging-based diagnosis.
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