The Spine Journal 21 (2021) 1626−1634
Narrative Review
Focus: Artificial Intelligence and Machine Learning
Artificial intelligence for adult spinal deformity: current
state and future directions
Rushikesh S. Joshi, BSa,*, Darryl Lau, MDb, Christopher P. Ames, MDc
a
Department of Neurological Surgery, University of California San Diego, La Jolla, CA, USA
b
Department of Neurosurgery, New York University, New York, NY, USA
c
Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA
Received 17 February 2021; revised 7 April 2021; accepted 27 April 2021
Abstract
As we experience a technological revolution unlike any other time in history, spinal surgery as a
discipline is poised to undergo a dramatic transformation. As enormous amounts of data become
digitized and more readily available, medical professionals approach a critical juncture with respect
to how advanced computational techniques may be incorporated into clinical practices. Within neurosurgery, spinal disorders in particular, represent a complex and heterogeneous disease entity that
can vary dramatically in its clinical presentation and how it may impact patients’ lives. The spectrum of pathologies is extremely diverse, including many different etiologies such as trauma, oncology, spinal deformity, infection, inflammatory conditions, and degenerative disease among others.
The decision to perform spine surgery, especially complex spine surgery, involves several nuances
due to the interplay of biomechanical forces, bony composition, neurologic deficits, and the
patient’s desired goals. Adult spinal deformity as an example is one of the most complex, given its
involvement of not only the spine, but rather the entirety of the skeleton in order to appreciate
radiographic completeness. With the vast array of variables contributing to spinal disorders, treatment algorithms can vary significantly, and it is very difficult for surgeons to predict how patients
will respond to surgery. As such, it will become imperative for spine surgeons to utilize the burgeoning availability of advanced computational tools to process unprecedented amounts of data
and provide novel insights into spinal disease. These tools range from predictive models built using
machine learning algorithms, to deep learning methods for imaging analysis, to natural language
processing that can mine text from electronic medical records or transcribed patient visits − all to
better treat the intricacies of spinal disorders. The adoption of such techniques will empower
patients and propel spine surgeons into the era of personalized medicine, by allowing clinical plans
to be tailored to address individual patients’ needs. This paper, which exists in the context of a
larger body of literatutre, provides a comprehensive review of the current state and future of artificial intelligence and machine learning with a particular emphasis on Adult spinal deformity surgery. © 2021 The Author(s). Published by Elsevier Inc. This is an open access article under the
CC BY license (http://creativecommons.org/licenses/by/4.0/)
Keywords:
Adult spinal deformity; Artificial intelligence; Machine learning; Predictive analytics; Predictive models; Spine
FDA device/drug status: Not applicable
Author disclosures: RSJ: Nothing to disclose. DL: Nothing to disclose.
CPA: Royalties: Stryker (F), Biomet Zimmer Spine (C), DePuy Synthes
(F), Nuvasive (B), Next Orthosurgical (F), K2M (None), Medicrea (B);
Consulting: DePuy Synthes (B), Medtronic (B), Medicrea (B), K2M (B);
Research Support (Investigator Salary, Staff/Materials)^: Titan Spine (E),
DePuy Synthes (None), ISSG (C); Grants: SRS.
*Corresponding author. Department of Neurological Surgery, University
of California, San Diego, 9300 Campus Point Dr, MC-7893, La Jolla, CA
92037, USA. Tel.: (408) 507-0993.
E-mail address: Rushi.Joshi.MS4@gmail.com (R.S. Joshi).
https://doi.org/10.1016/j.spinee.2021.04.019
1529-9430/© 2021 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license
(http://creativecommons.org/licenses/by/4.0/)
R.S. Joshi et al. / The Spine Journal 21 (2021) 1626−1634
Introduction to artificial intelligence: Applications for
spine surgery
Spine surgery as a specialty is rapidly approaching a critical juncture, as medicine embraces a new era of precision
medicine driven largely by unprecedented amounts of available data and advanced computational techniques. Technology and advancements within medicine are continuing to
develop at a rapid rate as data becomes increasingly more
digitized. Combining the massive amounts of available data
with powerful new computational methods, we now have
the ability to harness the power of artificial intelligence
(AI). At its core, AI seeks to replicate the experience of
human intelligence, or natural intelligence in computers.
While the broader goal of a generalized and automated
intelligence remains beyond our scope, we can now use the
tools of AI to develop systems that recreate the characteristics of human intelligence − to learn from immense datasets, make decisions, provide recommendations, and adapt
to new data/circumstances. Through the use of sophisticated concepts such as artificial neural networks and robust
machine learning methods we can now develop systems
that can dynamically learn from data and use that to inform
future behavior and decision making. AI represents a vast
technological goal encapsulating numerous disciplines such
as natural language processing, computer vision, and robotics amongst many others. However, machine learning exists
as a branch of AI that utilizes computer algorithms to learn
from data and prior experiences to build intelligent models.
Machine learning algorithms allow the computer to extract
patterns inherent within datasets without user-defined or
pre-determined rules, to learn relationships from the data
and then make specific predictions or determinations.
As a discipline, spine surgery offers a unique niche to
take advantage of the computational abilities of AI and
machine learning [1]. Over the past several decades, the
collective knowledge of spine disorders has increased
immensely. In addition to the heterogeneity in clinical presentation, spine disorders also span various different etiologies, which can include oncology, trauma, degenerative
disease, and spine deformity amongst others. With adult
spinal deformity (ASD) in particular, our knowledge has
increased significantly as techniques for surgical correction
of ASD have become more refined and widely adopted.
ASD represents an especially intriguing prospect for
machine learning methods due to its nature as one of the
most complex medical problems as it involves not only
evaluation of the entire spinal column, but also the entirety
of the skeleton for appropriate radiographic completeness
[2,3]. We now know that patients can present with drastically different symptoms that cause significant disability
and pain such that extent of deformity correlates with severity of symptoms [4−7]. While the literature has overall
shown that surgical correction of radiographic spinopelvic
measurements can significantly improve patients quality of
life as measured by health-related quality of life (HRQOL)
1627
metrics, there still remains a component of unpredictability
in how individual patients may respond to surgery [8−18].
Additionally, surgical correction often is invasive and
requires osteotomies to achieve desired correction, which
have been associated with a relatively high risk of major
complications [11,19−25]. The ability to better predict
postoperative outcomes for patients based on their independent profiles could greatly enhance our capability to tailor
treatment plans based on individual patients’ needs and
goals.
Other examples showcasing the potential for advanced
analytics to assist spine surgeons is with degenerative disc
disease causing spinal cord and nerve compression − an
extremely common clinical scenario. While the indications
for surgery of intractable pain, weakness, and loss of motor
coordination and strength remain familiar, the ability to predict with a high degree of certainty which patients will
improve symptomatically and which ones won’t, and over
how much time continues to elude us [26]. Many surgeons
have experienced firsthand patients whose symptoms don’t
improve despite adequate decompression, and while we can
postulate that inability to regain function may be due to
duration or degree of compression prior to surgery, this is
impossible to monitor as patients are not serially imaged.
Thus, the potential for AI tools to identify novel insights or
help us determine different criteria for surgery or prognostic
factors is immense. While these are only examples of some
situations where the incorporation of AI can address a clear
need, the opportunities are limitless in what questions may
be answered beyond the scope of our current knowledge
base and human limitations. Spine surgeons have historically relied on clinical judgment and experience, in addition
to retrospective cohort studies based on linear and logistic
regression to educate patients and inform their decision
making. While these statistical methods are important for
identifying associations between variables and outcomes,
they represent generalizations and averages across large
populations, and thus hold little value or granularity for specific cases. Additionally, regression models are far more
effective at determining associations than they are at predicting future outcomes.
In this review, we briefly recap some fundamental principles of machine learning algorithms and illustrate how AI
can be utilized for spinal disorders, by highlighting existing
studies that have begun to employ more powerful machine
learning algorithms for predictive models with a particular
emphasis on applications for ASD management.
Machine learning principles: Strengths and limitations
In order to properly implement machine learning for data
analysis, it is critical that surgeons understand the methods
and fundamental concepts inherent to these techniques.
Proper usage will be imperative for ensuring that models
are appropriately and rigorously developed for clinical
deployment and to contribute to the existing literature.
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R.S. Joshi et al. / The Spine Journal 21 (2021) 1626−1634
One of the core tenets that distinguishes the field of
machine learning from other concepts is the idea of having
an algorithm repeatedly “trained” on existing data and
allowing the mathematical model to learn the relationships
between variables in the dataset. With hypothesis-generated
statistical studies, the onus lies on the user to determine
which variables should be included as dependent and independent variables, limiting our ability to explore relationships within the data that may be non-intuitive. The idea
behind machine learning is that by removing the necessity
of hard-coding algorithmic rules or manually deciding on
relevant variables, instead the algorithm will construct a
model by learning the relationships inherent within the
data, eliminating the user bias of simpler statistical methods. This method allows all aspects of the input data to be
investigated, without ignoring information or variables that
may turn out to be important.
Within machine learning, there are three primary paradigms; these are supervised learning, unsupervised learning, and reinforcement learning. In supervised learning, the
data has specified “labels” or outputs that have been previously designated. As such, the algorithm will learn a function that maps input variables to specific outputs based on
input-output examples, so that the model can then be
deployed on novel data using the inferred function. Supervised learning problems generally fall under the categories
of classification or regression problems, and utilize several
different algorithms including support vector machines,
artificial neural networks, decision trees, and random forest
models among several others. The majority of studies discussed in this review incorporate supervised learning methods to develop models for predicting various outcomes
following surgical correction of ASD. In unsupervised
learning, the algorithm is free to learn the intrinsic patterns
within the data, as there are no labeled inputs or outputs.
These methods seek to identify the natural structure of the
data without any user-dictated labels. One such example of
unsupervised learning is hierarchical clustering analysis,
which was used by Ames et al. to identify a novel classification scheme for ASD patients, and also by Kim et al. for
classification of cervical deformity patients [27,28]. Reinforcement learning is the third concept, with slightly different applications. In reinforcement learning, the machine
learning model is trained in an environment to make a
series of decisions based on rewards and penalties, as it
seeks to maximize the total reward for the situation. Examples of reinforcement learning include developing automated systems to play games like Chess or Go, and also
autonomous vehicles.
The advantage of machine learning over earlier statistical methods and hypothesis-generated studies as mentioned
earlier lies in its function of being able to process and analyze large amounts of heterogeneous variables to make
highly accurate and reproducible predictions. While statistical models are essential for their ability to determine causative relationships and associations between variables, they
lack the same predictive capabilities as machine learning
models. The tradeoff, however, is that while more powerful,
machine learning models can also be more difficult to interpret, and thus have a higher barrier of entry for widespread
use. Additionally, machine learning generally requires large
amounts of high-quality data, and models tend to perform
better the more data is available for training as sparsely
populated data can lead to overfitting − statistical models
still retain their associative utility with relatively small
amounts of data. Techniques such as ensemble learning,
which is when predictions from several different models
are combined either by “bagging” (train multiple complex
models in parallel and average their responses for final
model) or “boosting” (train simpler models in sequence to
build on each other and create a final model) help mitigate
some of these issues. Despite the potential pitfalls, the
immense power of machine learning poses a unique opportunity for spine surgeons to capitalize on these emerging
technologies to better serve the patient population.
Applications for ASD surgery
Early ASD predictive models
ASD represents a unique niche within spine surgery that
is well poised to take advantage of the immense potential
offered by machine learning and AI. Given how heterogeneous the clinical presentation for ASD can be and the vast
array of surgical techniques available in the surgeon’s
armamentarium, the treatment algorithm can be complicated with many different possibilities. In addition, due to
the high complication rates and invasiveness of surgical
techniques, ASD surgery can benefit from predictive models that may offer patients additional granularity and risk
stratification catered specifically towards their individual
health profiles. By learning from historical data, predictive
models can then be deployed prospectively to account for
an individual’s specific profile and empower patients in a
shared decision-making process to tailor treatment plans
according to their own goals and needs. Recently, the International Spine Study Group (ISSG) and European Spine
Study Group (ESSG) have been at the forefront of developing predictive models for ASD patients, using their prospectively collected, multi-institutional database. Early efforts
in predictive modeling for ASD patients utilized supervised
learning where the data used to build the models has a specified target outcome or variable labeled, which machine
learning algorithms then attempt to predict by learning a
function based on input-output examples in the data. The
input variables in these models can be diverse, including
patient information such as radiographic parameters, demographic information, comorbidities, complex metrics such
as frailty index and Charlson Comorbidity index, surgical
characteristics, intraoperative information, and HRQOL
scores.
R.S. Joshi et al. / The Spine Journal 21 (2021) 1626−1634
Most predictive analytics in ASD pertain to the assessment of postoperative outcomes, however, some earlier
studies explored the feasibility of developing predictive
models for perioperative outcomes. Durand et al. used decision tree and random forest models in a cohort of over
1,000 ASD patients to predict intra- and postoperative
blood transfusion requirements with AUCs of 0.79 and
0.85, respectively [29]. Influential variables for predicting
transfusion requirements included operative duration, invasiveness, hematocrit, and patient weight and age. Looking
at additional perioperative outcomes, Safaee et al. and
Scheer et al. similarly built models to predict length of stay
(LOS) and early complication risk in ASD patients [30,31].
Safaee et al. used generalized linear regression models with
bootstrapping in a cohort of 653 patients and were able to
estimate LOS within 2 days with predictive accuracy of
75.4%, with major predictors being staged surgery, C7 sagittal vertical axis (SVA) and number of levels fused [30].
Scheer et al. utilized an ensemble of decision trees and
bootstrapping methods to predict major complications at 6
weeks postoperatively with an AUC of 0.89. Being able to
predict early complication risk or LOS can help inform discharge plans and workflow, as well as identify high-risk
surgical candidates at the preoperative stage [31].
The ability to predict postoperative outcomes following
ASD surgery was further explored in several subsequent
studies examining proximal junctional failure (PJF)/clinically relevant proximal junctional kyphosis (PJK) [32],
pseudarthrosis [33], risk stratification for major complications and reoperation [34], and cervical malalignment following thoracolumbar surgery [35]. Building on the success
of prior deployment of predictive models, Scheer et al.
were among the first to implement a machine learningbased model to predict rates of PJF and clinically significant
PJK in a cohort of 510 ASD patients [32]. Using similar
methods of decision trees and bootstrapping, their final
model yielded 86% accuracy with AUC of 0.89, and demonstrated the top predictors to be age, lower instrumented
vertebrae (LIV) and preoperative SVA. This same analytical method was then employed for pseudarthrosis as well
with equally successful results of 91% accuracy and AUC
of 0.94 [33]. The model for pseudarthrosis assessed a total
of 82 input variables, ultimately identifying the top contributing variables to be LIV, use of bone morphogenic protein
and max coronal cobb angle, in contrast to those identified
as predictors for PJK/PJF − this variance in influential input
variables highlights a key advantage of machine learning to
identify relationships between variables in the data that
may not be readily apparent. Passias et al. in a more unique
approach sought to predict cervical malalignment following
thoracolumbar ASD surgery, and developed a model with
AUC of 0.89 demonstrating C2-T3 cobb angle at baseline
and increased number of Smith-Peterson osteotomies to be
highly predictive of poor cervical compensation [35].
The most rigorous work to date assessing postoperative
risk of major complications, reoperation and hospital
1629
readmission was conducted by Pellise et al. and the ISSG/
ESSG [34]. In their study, random forest models incorporating over 100 input features from a cohort of 1,612 ASD
patients were built to assess risk for all three major outcomes of major complication, reoperation and hospital
readmission with AUC ranging from 0.67 to 0.92. The novelty of this study lies in its development of two predictive
models for each outcome: one consisting of only preoperative variables, and another including perioperative information as well. Significant predictors of hospital readmission
included pelvic tilt (PT), LIV, age, and ODI walking
response, while top predictors of reoperation were walking
ability and site/surgeon, which accounted for much greater
predictive ability for reoperation than for either readmission
or major complication. The top predictors for risk of major
complication in this model were LIV (with pelvic extension), age, walking ability, and sagittal imbalance radiographic parameters (most prominently SVA). Given the
relatively high rates of complications in ASD surgery, having robust and accurate models to predict individual
patients’ risk profiles based on their specific parameters is
crucial for promoting more informed and collaborative
decision-making that can take into account the patient’s
own goals. To help incorporate this risk stratification model
into clinical practice, the ISSG/ESSG have developed a calculator that is currently in alpha testing, to make such tools
more widely accessible. In Fig. 1, the user-interface for the
risk calculator is shown along with some of the input
parameters that can be entered specifically for a patient to
help generate their corresponding postoperative predictions.
Fig. 2 demonstrates an example of some of the output that is
produced by the calculator, including major complication,
reintervention and readmission rates, in addition to functional outcomes such as MCID across three different
HRQOL metrics. In an effort to further promote the utility
of predictive models for patient education, Ames et al. used
similar techniques to also investigate the cost implications
of ASD surgery given the enormous financial burden it can
place on both patients and hospital systems [36]. The aim
of this study was to preoperatively identify which patients
may be at risk of suffering catastrophic costs (defined as >
$100,000) at 90-days and 2-years following surgical correction of ASD. Using a combination of random forest models
and regression trees, the group was able to achieve goodness of fit R2 scores ranging from 56% to 57% when predicting 90-day direct cost and 29% to 35% for predicting 2year direct cost. While these accuracy metrics may reflect
relatively lower performance when compared to other predictive models developed for ASD, the key result was the
design which allowed the authors to interpret the model
results and identify variables that were highly predictive of
incurring catastrophic costs. Top predictors of direct cost
and catastrophic cost were determined to be number of levels fused, surgical approach, use of interbody fusion, LOS
and attending surgeon. By increasing awareness of at-risk
patients in the preoperative stage, patients and healthcare
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R.S. Joshi et al. / The Spine Journal 21 (2021) 1626−1634
Fig. 1. Web-based risk calculator for ASD surgery outcomes predictions (input). The calculator developed by the ISSG/ESSG allows for myriad patientrelated information including demographics, radiographic parameters and surgical plan among several others, to be input for individual patients. The calculator is then able to make predictions for several different postoperative outcomes.
R.S. Joshi et al. / The Spine Journal 21 (2021) 1626−1634
1631
Fig. 2. Web-based risk calculator for ASD surgery outcomes predictions (output). The ISSG/ESSG risk calculator is able to predict several different outcomes for individual patients based on the input parameters entered. These outcomes include rates for major complications, reinterventions and readmission,
in addition to PROs such as MCID based on three major HRQOL surveys (ODI, SRS22, and SF36).
systems could both benefit tremendously from cost-sharing
and bundled payments to reduce the immense economic
burden of these scenarios.
Predictive analytics for HRQOL scores following ASD
surgery
In addition to predicting postoperative outcomes and
complications, machine learning also offers the potential
to better prognosticate how patients will respond to surgery based on PROs and HRQOL metrics such as the
Scoliosis Research Society-22 (SRS-22), Short Form-36
(SF-36) and ODI surveys. It is critical that predictive analytics move beyond solely assessing outcomes such as
complication risk or MCID, because for patients, it is often
far more informative to understand what their functional
outcome may look like following surgical intervention.
With this goal in mind, Oh et al. were among the first
groups to develop a machine learning model to predict
PROs following ASD surgery [37]. Using an ensemble of
decision trees and bootstrapping, their predictive model
was able to successfully predict MCID in 2-year ODI
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scores with 85.5% accuracy and AUC of 0.96. Scheer et al.
followed up on this work by looking at only ASD patients
with preoperative ODI>30 and developed a predictive
model with AUC of 0.94 and 86% accuracy. Notably, the
variables identified as top predictors in these two studies
were remarkably different: Oh et al. identified patient
comorbidities (preoperative depression, arthritis and osteoporosis) as well as number of levels fused as the most significant predictors of MCID in 2-year ODI scores, while
Scheer et al. found radiographic parameters such as SVA
and pelvic incidence-lumbar lordosis (PI-LL) mismatch in
addition to gender and SRS-22 scores as having greater
predictive influence in their model.
Similar to the efforts for developing robust predictive analytics for ASD risk stratification and outcomes, the ISSG/ESSG
also led several revolutionary studies with respect to predicting
PROs following ASD surgery using their large, prospectively
collected database. A few key differences exist between the
studies conducted by the ISSG/ESSG and the earlier described
models. Most importantly, their ASD database contains highquality data that has been carefully curated and collected
through collaborations across multiple countries, spine centers
and surgeons. The more data that is available for a model to be
trained on, the better its generalizability is for future prospective
applications. In two rigorous studies, Ames et al. developed
robust predictive models utilizing several different algorithmic
approaches to predict probability of achieving MCID across all
three major HRQOL domains (ODI, SRS-22 and SF-36) and a
separate model to predict individual patient responses to each
of the SRS-22 survey questions [38,39]. To predict MCID
across all three different HRQOL metrics, Ames et al. in a
cohort of 570 patients used a total of 75 input features and 8 different machine learning algorithms assessed for accuracy using
MAE to build their predictive models [38]. Final model selection was determined by performance as assessed by MAE
(ranging from 8%−15% indicating high accuracy) and R2
goodness of fit scores. Baseline PROs were found to be the
most important variable in predicting postoperative PROs, and
with respect to patient-level data, age followed by comorbidities
were the most important variables. Taking this study even further, Ames et al. next explored the feasibility of predicting
patient responses to each of the individual SRS-22 questions to
provide more granular insight into possible functional outcomes
following surgery [39]. Analyzing six different machine learning algorithms and 150 input features, Ames et al. were able to
develop predictive models with AUC ranging from 0.57 −
0.87. Interestingly, the models differed in their predictive ability
based on which domains individual questions belonged to;
SRS-22 domains of pain, disability, and social/labor function
had the highest predictive accuracies while subjective responses
in the domains of satisfaction, depression/anxiety and selfappearance were less accurately predicted. These efforts highlight both a paradigm shift in the ability of spine surgeons to
embrace personalized/individualized medicine for ASD
patients, but also underscore the difficulty of gauging functional
outcomes in more subjective domains such as mental health,
patient attitudes, and emotional well-being. By incorporating
predictive analytics into clinical practice, spine surgeons can
begin to substantiate their clinical recommendations with individualized data for patients to be more well-informed and partake in their own treatment plans.
Advanced analytics for ASD surgery using unsupervised
learning
While predictive models that help prognosticate patient
outcomes with ASD surgery are critical for empowering
patients when discussing treatment plans and goals of surgery, these methods all constitute supervised learning as there
is a desired output that is being predicted. In the first use of
unsupervised learning for spine research, Ames et al. implemented an AI system to develop a novel classification scheme
for ASD patients [27]. As described earlier, unsupervised
learning represents a subset of machine learning where there
are no labeled input/output pairings in the data; rather the
goal is to use a mathematical model to identify inherent patterns in the natural structure of the data. Used in this context,
Ames et al. wanted to employ unsupervised learning across
their cohort of 570 ASD patients to identify unique patient
clusters based on the available input features. The final model
presented three distinct clusters of patients based on clinical
information, and four distinct clusters when considering surgical characteristics. The patient-level clusters comprised of
young patients with coronal deformity, older patients with
higher incidence of prior spine surgery, and older patients
with lower incidence of prior spine surgery. Surgery-level
clusters were defined as patients with high number of levels
fused and three-column osteotomies, patients with high number of levels fused and Smith-Peterson osteotomies, patients
with no interbody fusions or osteotomies and patients with
the highest number of interbody fusions. The hierarchical
clustering algorithm determined that patients within these
specific groupings shared the most similar characteristics, and
represented a novel approach compared to prior classification
schemes that were solely reliant on radiographic parameters.
When the three patient-level clusters and four surgery-level
clusters were combined into 12 sub-groups, PRO, complication and risk profiles for each sub-group could then be determined, with major complication risk ranging from 0% to
51.8%. This study presented a major stride forward in the
realm of machine learning and AI, and showcased how powerful unsupervised learning techniques could help create new
AI-based classification systems to facilitate treatment optimization for surgeons by highlighting treatment patterns predicted to yield optimal improvement with lowest risk. The
added benefit of having more granular risk-benefit profiles
during the preoperative evaluation of patients will help drive
ASD surgery towards personalized medicine.
Conclusion: Future directions and next steps
In summary, while spine surgeons have begun to make significant strides in the realm of predictive analytics, we are just
R.S. Joshi et al. / The Spine Journal 21 (2021) 1626−1634
barely glimpsing the power of machine learning and AI techniques, and how they can be widely incorporated to augment our
ability to treat and care for spine patients. As we embrace the
era of genomic medicine, combined with the digitization and
widespread availability of enormous amounts of data, advanced
computational techniques will become critical to help physicians analyze vast amounts of data that would be impossible
without computer assistance. Oncology in particular has seen a
huge advancement recently with molecular and genomic information about various tumors, and dramatically altered how
patients are treated based on their individual profiles and new
molecularly targeted therapies. AI will especially help with
ASD as our knowledge of biomarkers increases, and as we
acquire additional information including with the use of wearable technology pertaining to various aspects of a person’s biometrics and biophysical health profile. A strong foundation of
computer science tools will be imperative to analyze this disparate data. The age of physicians making decision based on clinical judgment in isolation will slowly fade away as technology
continues to advance, providing physicians with novel insights
and data to supplement and augment decades of clinical experience. The information we are poised to gain through the implementation of machine learning and AI into clinical practice will
empower patients in a shared decision-making and push spine
surgeons confidently into the era of individualized medicine.
Acknowledgment
[9]
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No funding was provided for this article.
Declarations of Competing Interests
The authors declare that they have no known competing
financial interests or personal relationships that could have
appeared to influence the work reported in this paper.
[17]
[18]
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