عنوان انگلیسی مقاله:
Artificial intelligence (AI) and big data in cancer and precision oncology
ترجمه فارسی عنوان مقاله:
هوش مصنوعی و داده های بزرگ در سرطان و انکولوژی دقیق
Sciencedirect - Elsevier - Computational and Structural Biotechnology Journal, 18 (2020) 2300-2311. doi:10.1016/j.csbj.2020.08.019
Zodwa Dlamini ⇑, Flavia Zita Francies, Rodney Hull, Rahaba Marima
Artificial intelligence (AI) and machine learning have significantly influenced many facets of the healthcare
sector. Advancement in technology has paved the way for analysis of big datasets in a cost- and
time-effective manner. Clinical oncology and research are reaping the benefits of AI. The burden of cancer
is a global phenomenon. Efforts to reduce mortality rates requires early diagnosis for effective therapeutic
interventions. However, metastatic and recurrent cancers evolve and acquire drug resistance. It is imperative
to detect novel biomarkers that induce drug resistance and identify therapeutic targets to enhance
treatment regimes. The introduction of the next generation sequencing (NGS) platforms address these
demands, has revolutionised the future of precision oncology. NGS offers several clinical applications that
are important for risk predictor, early detection of disease, diagnosis by sequencing and medical imaging,
accurate prognosis, biomarker identification and identification of therapeutic targets for novel drug discovery.
NGS generates large datasets that demand specialised bioinformatics resources to analyse the
data that is relevant and clinically significant. Through these applications of AI, cancer diagnostics and
prognostic prediction are enhanced with NGS and medical imaging that delivers high resolution images.
Regardless of the improvements in technology, AI has some challenges and limitations, and the clinical
application of NGS remains to be validated. By continuing to enhance the progression of innovation
and technology, the future of AI and precision oncology show great promise.
Keywords: Artificial intelligence | Machine learning | Deep learning | Big datasets | Precision oncology | NGS and bioinformatics | Medical imaging | Digital pathology | Diagnosis | Treatment | Prognosis and drug discovery