Artificial intelligence (AI) is a general term that refers to the use of a
machine to imitate intelligent behavior for performing complex tasks with
minimal human intervention, such as machine learning; this technology is
revolutionizing and reshaping medicine. AI has considerable potential to
perfect health-care systems in areas such as diagnostics, risk analysis, health
information administration, lifestyle supervision, and virtual health
assistance. In terms of immunotherapy, AI has been applied to the prediction
of immunotherapy responses based on immune signatures, medical imaging and
histological analysis. These features could also be highly useful in the
management of cancer immunotherapy given their ever-increasing performance in
improving diagnostic accuracy, optimizing treatment planning, predicting outcomes
of care and reducing human resource costs.
Although immunotherapy is a great breakthrough in the field of cancer
treatment, the judgment of whether a particular patient can respond to the
therapy is occasionally confusing. However, the appearance of AI increases the
chance of successful cancer immunotherapy through forecasting the therapeutic
effect based on the establishment of immunotherapy predictive scores, including
immunoscore and immunophenoscore. These two scoring systems were developed
to predict the response to immune checkpoint blockade (ICB) therapy. Meanwhile,
some limitations, such as unknown predictive power of individual biomarkers,
difficulty of integrating diverse biomarkers into one system and lack of ICB
response prediction models that can integrate different biomarkers, are the
main barriers that warrant further study. A previous study showed that the
integration of an AI-based diagnostic algorithm with physicians’
interpretations can be positively related to improving diagnostic accuracy for
indiscernible cancer subtypes. AI technology obtains approximately 91.66%
accuracy when recognizing major histocompatibility complex patterns associated
with immunotherapy response. More importantly, AI can be applied to standardize
assessments across institutions instead of depending on the interpretation of
clinicians that occasionally is inherently subjective. Therefore, the
application of AI in cancer immunotherapy may lead to positive outcomes in
patients.
To date, most notable is the successful
application of AI in immunotherapy in cancer research. Machine
Learning can match the pace with modern medicine regarding generated data and
the detection of phenotypic varieties that sneak through human screening. The range of machine
screening can also be adjusted to detect only interested phenotype changes or
to screen for broader phenotypes. Currently, AI-based methods have shown good
results in the prediction of MHC-II epitopes on the strength of amino acid
sequences and the development of vaccines targeting MHC-II immunopeptidome ,
which demonstrate the increasingly extensive application of AI in
immunotherapy.
Sources: https://www.sciencedirect.com/science/article/pii/S2211383521000459#bib90
https://blog.gnshealthcare.com/immunotherapies-with-ai-changing-cancer-treatment
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