The rapid growth of technology in the past couple of decades has paved the way for
development of novel techniques that can solve scientific questions at a rate that
is far beyond the capability of humans. One such example is the field of Artificial
Intelligence (AI) and Machine Learning (ML). AI is a discipline that deals with the
study and design of intelligent agents, that is, devices that intricately perceive
their environment and take actions that maximize the chances of achieving their goals
(1). AI, in a way, mimics the structure and operating methodologies of a human brain
(2). AI has two forms of application: physical and virtual (3). The physical component
is mainly represented by robots. Derived from a Czech word robota, meaning “forced
labor,” the physical robotic forms were conceptualized by inventors such as Leonardo
Da Vinci (3). This component has been widely used in the field of endocrinology, such
as robot-assisted surgery of adrenal or prostate cancer. Examples of virtual applications
of AI are electronic medical records (EMR), where specific algorithms are used to
identify subjects, and harness health related data (3).
ML is a field of AI that deals with the development of models and intricate networks
that enable computer systems to improve their performance on a specific task progressively
(4). ML algorithms can be: (i) unsupervised (spontaneous pattern detection), (ii)
supervised (building algorithms based on prior examples), or (iii) reinforcement learning
(utilization of reward/punishment techniques to obtain the desired result) (3). A
common use of ML in daily life includes flagging spam in an e-mail, autonomous driving
and selecting the best route for daily commute. In the field of medicine, AI/ML technology
can have substantial impact at three levels: physicians, by improving the diagnostic
accuracy and assisting with therapeutic and surgical interventions; health systems,
by enabling improved workflow and reduction in errors; patients, through tailoring
of diagnostic, and treatment modalities based on the unique phenotypic and genetic
features of individual patients (5). In this review, we focus on the virtual components
of AI and ML and provide some examples for the utility of AI/ML in endocrinology and
metabolism.
From early ML tools like logistic regression which found their utility in medicine
several decades ago, AI/ML methods have become far more multifaceted and have revolutionized
the field of medicine through their ability to compute and analyze vast and complex
array of datasets which would not be feasible solely with trained human skillsets
(2). Several AI/ML methods have proven their utility in the diagnosis and management
of various endocrinopathies. Gradient forest analysis, a ML technique, was applied
in a study to identify factors contributing to variation in all-cause mortality among
subjects in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial (6).
This technique detected four risk groups based on hemoglobin glycosylation index (HGI),
BMI, and age. The lowest risk group (with HGI < 0.44, BMI < 30 kg/m2, and age <61
years) experienced reduced absolute mortality risk of 2.3%, while the highest risk
group (HGI > 0.44) experienced a 3.7% increase in absolute mortality risk attributable
to intensive glycemic therapy. These mortality variations in the intensive treatment
group were previously not detected by older, univariate subgroup analyses (6). Another
study developed a prototype support vector regression model that outperformed diabetologists
in predicting blood glucose levels at 30 and 60 min from a given time in patients
with type 1 diabetes, and predicted about one quarter of hypoglycemic events 30 min
ahead of the actual event (7).
AI/ML-based algorithms have been extensively utilized and validated for diagnosis
and classification of diabetic retinopathy (8–11). Deep learning systems and even
purely database-driven AI algorithms have demonstrated the ability to diagnose diabetic
retinopathy and related retinal diseases in large, multiethnic cohorts with high degree
of sensitivity and specificity (8, 10). ML algorithms have also demonstrated the ability
to incorporate associated risk factors such as duration of diabetes and insulin use
into risk-stratification of diabetic retinopathy, which could potentially facilitate
the development of better clinical decision support systems (11). A proprietary system
IDx (Iowa City, IA) that uses ML technology to analyze retinal images in diabetic
retinopathy had a sensitivity of 87% and specificity of 91% for autonomous detection
of disease and received FDA approval in 2018 (12). This example represents the first
prospective assessment of AI/ML in the clinic (5).
AI/ML technologies has also been used in the analysis of large datasets generated
from genomic technology. Deep-coverage whole-genome sequencing performed in 8,392
individuals of European and African descent to identify single-nucleotide variants
and copy-number variations in Lipoprotein (a) revealed that LPA risk genotypes conferred
greater relative risk for incident cardiovascular disease than direct measurements
of Lipoprotein (a) levels. These risk genotypes were also associated with increased
sub-clinical atherosclerotic disease in individuals of African ancestry (13). ML techniques
were utilized in developing a novel mRNA based molecular test to detect BRAF V600E
mutations in thyroid fine needle aspirate samples, which demonstrated sensitivity
equal to that of established DNA-based assay and had lower non-diagnostic rates (14).
By utilizing functional enrichment analysis followed by module analysis performed
on protein-protein interaction network, the differential gene expression in anaplastic
thyroid carcinoma was assessed (15). There were 247 up-regulated genes which were
predominantly involved in cell cycle and 275 down-regulated genes that were mostly
involved in thyroid hormone synthesis, insulin resistance, and cancer pathways, thus
expanding on the current knowledge of genetics of thyroid carcinomas (15).
AI/ML methods can accurately interpret medical images and provide a computer-aided
diagnosis. ML algorithms like convolutional neural network and support vector machine
(SVM) demonstrated higher sensitivity, specificity, and positive and negative predictive
values with early detection of facial changes in acromegaly when compared with doctors'
evaluation, thus allowing for earlier clinical diagnosis and evaluation (16). A simple
diagrammatic representation of utilization of facial landmark recognition for diagnosis
of acromegaly is provided in Figure 1. ML has enabled individuals to make optimal
decisions in real time by enabling organizational performance (3). For example, utilization
of ML techniques such as principal component analysis and SVM, and matrix-assisted
laser desorption/ionization mass spectrometry (MS) imaging demonstrated the potential
to differentiate hormone-secreting from non-secreting pituitary adenomas, and demarcate
tumor from normal gland at a molecular level under 30 minutes, therefore potentially
enabling real-time, intra-operative tumor delineation, and improve patient outcomes
(17).
Figure 1
A simplified schematic representation of one of the several machine learning (ML)
technologies that can be utilized for diagnosis and management of endocrine disorders.
The example demonstrated in the above diagram deals with early recognition of acromegaly
based on facial features. Photographs of several individuals with normal facial features
are utilized to obtain data on facial patterns through processes such as facial feature
extraction (represented by blue dotted lines), facial detection, normalization, and
frontalization (also displayed in the figure). This data is then fed into ML algorithms
for recognizing normal facial features. These ML tools then perform complex analyses
and generate output data that is used to determine whether the face presented in the
test photograph is consistent with features of acromegaly (Image courtesy: Sriram
Gubbi, NIDDK, NIH).
Variants of SVM, neural networks, and other ML techniques, with immunohistochemical
methods were utilized in categorizing Cushing syndrome with adrenocortical lesions
(18). Based on the gene expression profiling, the highest expressions of Ki-67 and
PCNA were found in adrenocortical carcinoma while highest FHIT expression was found
in adrenocortical hyperplasia, and adrenal adenomas had intermediate expression of
all three antigens. These techniques diagnosed the adrenocortical disease type with
92.6% accuracy. In another study, urinary steroid profiling using gas chromatography/MS
and subsequent ML analysis generated a pattern of immature, early stage steroid metabolites
in adrenocortical cancer (19). This enabled differentiation of cancer from an adenoma
with a sensitivity and specificity of 90%, a diagnostic value that was higher than
CT, MRI, or PET scans.
Another emerging field based on AI technology that could potentially have a wider
scope in the future is “pre-emptive medicine” (20). Pre-emptive medicine is a novel
concept proposed in Japan, which aims at delaying the onset, or even preventing the
occurrence of chronic diseases, such as diabetes, hypertension, cancer, or dementia
by using a combination of AI techniques, genomic analysis and environmental interaction
data (20). The above examples reinforce the promising role of AI/ML in diagnosis and
management of endocrine disorders which, in several instances, can outperform skilled
physicians, minimize resource use and allocation, and yield tangible benefits by supporting
physicians and accelerating clinical decision-making (7, 16). Despite the substantial
evidence for the ability of AI/ML to deliver cost-effective healthcare and improve
patient outcomes, medicine has trailed behind other scientific fields in implementing
these techniques into practice (21). Potential hurdles include the longitudinal nature
of variations in human disease, inadequacies in the quality and reliability, heterogeneity
of healthcare data, personal data confidentiality, need for informed consent from
patients, requirement of supportive policies and efficient business models, unpredictable
reimbursement, and increasing necessity for data sharing (21, 22). The so-called “digital
biomarkers” that are obtained through big data analyses performed using AI/ML techniques
are not readily interpretable clinically, in the sense, even if a certain newer AI/ML
algorithm has been shown to be superior to older techniques in certain population
cohorts; its implementation in clinical practice across more diverse populations might
not necessarily result in better diagnosis or outcome; and could potentially even
lead to over-diagnosis and over-treatment in certain patient cohorts (23). Ethical
issues, including misuse of AI/ML to manipulate quality metrics to make unscrupulous
profit, potential in-built discriminatory biases toward under-represented populations,
and physician over-dependence on AI/ML pose some of the foreseeable challenges in
this field (24). Although AI/ML can theoretically “replace” physicians with regards
to performing certain diagnostic, therapeutic, or surgical tasks, these technologies,
almost certainly, will never be able to provide the emotional, social, and ethical
support that a physician can offer the patient, and will never replace the unique
bond of a doctor-patient relationship (25, 26).
Then the question arises: What aspects of patient care can physicians focus on in
the era of AI/ML technology? With the ever-growing complexity and quantity of knowledge
in the medical field, it is almost impossible for physicians to mentally organize
and retain all of this data (27). Therefore, the medical fraternity should focus on
strengthening the following aspects in order to re-define the role of physicians in
the era of AI/ML: (1) Medical school curricula must shift their focus from information
acquisition to knowledge management and communication skills, (2) Physicians must
be trained to manage and collaborate with AI/ML applications, (3) Emphasis must be
placed on training physicians to interpret AI/ML output data and to effectively utilize
these results in clinical decision making, and (4) Reinforcing cultivation of empathy
and compassion among physicians (27). Eventually, physicians and AI technology need
to develop a mutually supportive relationship than a competitive one. While physicians
can provide appropriate feedback for AI/ML techniques and tools to improve, these
tools in turn can facilitate physicians in solving uncertain clinical scenarios (28).
This can result in mutual identification of key clinical or algorithmic biases, which
can be then tackled by the combined efforts of physicians and AI/ML through better
data collection and through model improvements (28).
In conclusion, utilization of AI/ML will enable diagnosing endocrine disorders with
higher accuracy, potentially avoid unnecessary investigations, and reduce healthcare
expenditures and facilitate better digital storage of vast patient data, be it individual
profiles or aggregated data for epidemiological research and planning, and these benefits
may one day transform clinical endocrine practice. Today, AI technologies like artificial
pancreas for management of diabetes have already become a reality (29). Major efforts
are required from academia and the information technology industry to push for further
development of AI/ML technology in endocrinology. Endocrinologists are well suited
to play a vital role in the advancement of AI/ML. However, this has not been the focus
of training programs for endocrine subspecialties, which do not provide the necessary
education for trainees to feel confident in the use of these technologies for diagnosis
or research. There is certainly a need to spread awareness, acquire funding, introduce
these concepts into training programs, and encourage further research in this new,
exciting branch of endocrinology and metabolism—a deep future awaits!
Author Contributions
SG prepared the manuscript and the figure. FH-S, PH, JT, and CK wrote parts of the
manuscript and critically reviewed the manuscript.
Conflict of Interest Statement
The authors declare that the research was conducted in the absence of any commercial
or financial relationships that could be construed as a potential conflict of interest.