This primer provides an overview of artificial intelligence (AI) for the oncologist and technologist, delineating its relevance, methodologies, and applications. AI, encompassing machine learning (ML) and deep learning (DL), utilizes algorithms to analyze data and make predictions, facilitating early cancer detection and personalized treatment strategies. ML, characterized by supervised and unsupervised learning, allows for pattern recognition and prediction, whereas DL, particularly through convolutional neural networks and transformers, excels in complex data analysis. AI applications span various oncology data types, including electronic health records, medical imaging (radiology and digital pathology), biomarkers (genomics, proteomics, and metabolomics), and digital health sensors. Leveraging AI in oncology enhances diagnostic accuracy, improves treatment selection, and enables early detection through innovative approaches such as wearable devices and mobile health applications. This primer underscores AI's transformative potential in advancing cancer care, exemplified by Food and Drug Administration-approved AI systems and prominent DL architectures. This overview serves as a guide for researchers, clinicians, and policymakers navigating the evolving landscape of AI-driven oncology, emphasizing its pivotal role in augmenting patient outcomes and shaping the future of cancer care.
Get full access to this article
View all access options for this article.
References
1.
JiangF, JiangY, ZhiH, et al.Artificial intelligence in healthcare: Past, present and future. Stroke Vasc Neurol, 2017;2(4):230–243; doi: 10.1136/svn-2017-000101
2.
SudhakarP, MachielsK, VerstocktB, et al.Computational biology and machine learning approaches to understand mechanistic microbiome-host interactions. Front Microbiol, 2021;12:618856; doi: 10.3389/fmicb.2021.618856
3.
HosnyA, ParmarC, QuackenbushJ, et al.Artificial intelligence in radiology. Nat Rev Cancer, 2018;18(8):500–510; doi: 10.1038/s41568-018-0016-5
4.
PanayidesAS, AminiA, FilipovicND, et al.AI in Medical Imaging Informatics: Current Challenges and Future Directions. IEEE J Biomed Health Inform, 2020;24(7):1837–1857; doi: 10.1109/JBHI.2020.2991043
5.
HomeyerA, LotzJ, SchwenLO, et al.Artificial intelligence in pathology: From prototype to product. J Pathol Inform, 2021;12:13; doi: 10.4103/jpi.jpi_84_20
6.
FarinaE, NabhenJJ, DacoregioMI, et al.An overview of artificial intelligence in oncology. Future Sci OA, 2022;8(4):FSO787; doi: 10.2144/fsoa-2021-0074
7.
KhanamN, KumarR.Recent applications of artificial intelligence in early cancer detection. Curr Med Chem, 2022;29(25):4410–4435; doi: 10.2174/0929867329666220222154733
8.
KrizhevskyA, SutskeverI, HintonGE. ImageNet Classification with Deep Convolutional Neural Networks. In: Advances in Neural Information Processing Systems. (Pereira F, Burges CJ, Bottou L, et al. eds.) Curran Associates, Inc.; 2012.
DevlinJ, ChangM, LeeK, et al.[1810.04805] BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding. 2019. Available from: https://arxiv.org/abs/1810.04805 [Last accessed: December28, 2023].
ChenT, GuestrinC.XGBoost: A Scalable Tree Boosting System. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD’16 Association for Computing Machinery: New York, NY, USA; 2016; pp. 785–794; doi: 10.1145/2939672.2939785
14.
GoodfellowIJ, Pouget-AbadieJ, MirzaM, et al.Generative Adversarial Nets. In: Proceedings of the 27th International Conference on Neural Information Processing Systems—Volume 2. NIPS’14 MIT Press: Cambridge, MA, USA; 2014; pp. 2672–2680.
15.
SilvaTC, ZhaoL.Machine Learning. In: Machine Learning in Complex Networks. (Christiano Silva T, Zhao L. eds.) Springer International Publishing: Cham; 2016; pp. 71–91; doi: 10.1007/978-3-319-17290-3_3
ChoudharyK, DeCostB, ChenC, et al.Recent advances and applications of deep learning methods in materials science. Npj Comput Mater, 2022;8(1):59; doi: 10.1038/s41524-022-00734-6
18.
PaulD, SanapG, ShenoyS, et al.Artificial intelligence in drug discovery and development. Drug Discov Today, 2021;26(1):80–93; doi: 10.1016/j.drudis.2020.10.010
SurianarayananC, LawrenceJJ, ChelliahPR, et al.Convergence of artificial intelligence and neuroscience towards the diagnosis of neurological disorders—A scoping review. Sensors, 2023;23(6):3062; doi: 10.3390/s23063062
22.
SunT, FengB, HuoJ, et al.Artificial intelligence meets flexible sensors: Emerging smart flexible sensing systems driven by machine learning and artificial synapses. Nano-Micro Lett, 2023;16(1):14; doi: 10.1007/s40820-023-01235-x
LongwellJB, GrantRC, HirschI, et al.Large language models encode medical oncology knowledge: Performance on the ASCO and ESMO examination questions. JCO Oncol Pract, 2023;19(11 Suppl.):511–511; doi: 10.1200/OP.2023.19.11_suppl.511
25.
SeoH, Badiei KhuzaniM, VasudevanV, et al.Machine learning techniques for biomedical image segmentation: An overview of technical aspects and introduction to state-of-art applications. Med Phys, 2020;47(5):e148–e167; doi: 10.1002/mp.13649
26.
HunterB, HindochaS, LeeRW. The role of artificial intelligence in early cancer diagnosis. Cancers, 2022;14(6):1524; doi: 10.3390/cancers14061524
27.
WangR, LeiT, CuiR, et al.Medical image segmentation using deep learning: A survey. IET Image Process, 2022;16(5):1243–1267; doi: 10.1049/ipr2.12419
YamashitaR, NishioM, DoRKG, et al.Convolutional neural networks: An overview and application in radiology. Insights Imaging, 2018;9(4):611–629; doi: 10.1007/s13244-018-0639-9
30.
ShenD, WuG, SukH-I. Deep learning in medical image analysis. Annu Rev Biomed Eng, 2017;19(1):221–248; doi: 10.1146/annurev-bioeng-071516-044442
31.
SandersJ, FrankS, LewisG, et al.Multi-tasking neural networks for anatomy segmentation in prostate brachytherapy MRI. Brachytherapy, 2019;18(3):S16; doi: 10.1016/j.brachy.2019.04.039
32.
SandersJW, FrankSJ, KudchadkerRJ, et al.Development and clinical implementation of SeedNet: A sliding-window convolutional neural network for radioactive seed identification in MRI-assisted radiosurgery (MARS). Magn Reson Med, 2019;81(6):3888–3900; doi: 10.1002/mrm.27677
33.
SandersJW, FletcherJR, FrankSJ, et al.Deep learning application engine (DLAE): Development and integration of deep learning algorithms in medical imaging. SoftwareX, 2019;10:100347; doi: 10.1016/j.softx.2019.100347
34.
SandersJ, BrunoT, FrankS, et al.SeedNet for automated detection and localization of radioactive seeds in post-implant MRI: A comparison with and without the use of An Endorectal Coil for Imaging. Brachytherapy, 2018;17(4):S80–S81; doi: 10.1016/j.brachy.2018.04.136
35.
HeK, GanC, LiZ, et al.Transformers in medical image analysis. Intell Med, 2023;3(1):59–78; doi: 10.1016/j.imed.2022.07.002
36.
ChhatwalJ, AlagozO, LindstromMJ, et al.A logistic regression model based on the national mammography database format to aid breast cancer diagnosis. Am J Roentgenol, 2009;192(4):1117–1127; doi: 10.2214/AJR.07.3345
37.
OlatunjiSO, AlotaibiS, AlmutairiE, et al.Early diagnosis of thyroid cancer diseases using computational intelligence techniques: A case study of a Saudi Arabian dataset. Comput Biol Med, 2021;131:104267; doi: 10.1016/j.compbiomed.2021.104267
38.
ZhangF, KaufmanHL, DengY, et al.Recursive SVM biomarker selection for early detection of breast cancer in peripheral blood. BMC Med Genomics, 2013;6(Suppl. 1):S4; doi: 10.1186/1755-8794-6-S1-S4
39.
LiewXY, HameedN, ClosJ.An investigation of XGBoost-based algorithm for breast cancer classification. Mach Learn Appl, 2021;6:100154; doi: 10.1016/j.mlwa.2021.100154
40.
XiaoL-H, ChenP-R, GouZ-P, et al.Prostate cancer prediction using the random forest algorithm that takes into account transrectal ultrasound findings, age, and serum levels of prostate-specific antigen. Asian J Androl, 2017;19(5):586–590; doi: 10.4103/1008-682X.186884
41.
MuhammadW, HartGR, NartowtB, et al.Pancreatic cancer prediction through an artificial neural network. Front Artif Intell, 2019;2:2; doi: 10.3389/frai.2019.00002
42.
SuhYJ, JungJ, ChoB-J. Automated breast cancer detection in digital mammograms of various densities via deep learning. J Pers Med, 2020;10(4):211; doi: 10.3390/jpm10040211
43.
CirilloD, Núñez-CarpinteroI, ValenciaA.Artificial intelligence in cancer research: Learning at different levels of data granularity. Mol Oncol, 2021;15(4):817–829; doi: 10.1002/1878-0261.12920
44.
KohD-M, PapanikolaouN, BickU, et al.Artificial intelligence and machine learning in cancer imaging. Commun Med, 2022;2(1):1–14; doi: 10.1038/s43856-022-00199-0
45.
LeeLIT, KanthasamyS, AyyalarajuRS, et al.The current state of artificial intelligence in medical imaging and nuclear medicine. BJR Open, 2019;1(1):20190037; doi: 10.1259/bjro.20190037
46.
SandersJW, LewisGD, ThamesHD, et al.Machine segmentation of pelvic anatomy in MRI-assisted radiosurgery (MARS) for prostate cancer brachytherapy. Int J Radiat Oncol Biol Phys, 2020;108(5):1292–1303; doi: 10.1016/j.ijrobp.2020.06.076
47.
SongAH, JaumeG, WilliamsonDFK, et al.Artificial intelligence for digital and computational pathology. Nat Rev Bioeng, 2023;1:930–949; doi: 10.1038/s44222-023-00096-8
48.
ProvostF, FawcettT.Data Science for Business. O'Reilly Media, Inc.; 2013.
49.
BeraK, SchalperKA, RimmDL, et al.Artificial intelligence in digital pathology—New tools for diagnosis and precision oncology. Nat Rev Clin Oncol, 2019;16(11):703–715; doi: 10.1038/s41571-019-0252-y
50.
EstevaA, KuprelB, NovoaRA, et al.Dermatologist-level classification of skin cancer with deep neural networks. Nature, 2017;542(7639):115–118; doi: 10.1038/nature21056
ManiDR, KrugK, ZhangB, et al.Cancer proteogenomics: Current impact and future prospects. Nat Rev Cancer, 2022;22(5):298–313; doi: 10.1038/s41568-022-00446-5
53.
SpratlinJL, SerkovaNJ, EckhardtSG. Clinical applications of metabolomics in oncology: A review. Clin Cancer Res, 2009;15(2):431–440; doi: 10.1158/1078-0432.CCR-08-1059
54.
JunaidSB, ImamAA, BalogunAO, et al.Recent advancements in emerging technologies for healthcare management systems: A survey. Healthcare, 2022;10(10):1940; doi: 10.3390/healthcare10101940
55.
BegS, HandaM, ShuklaR, et al.Wearable smart devices in cancer diagnosis and remote clinical trial monitoring: Transforming the healthcare applications. Drug Discov Today, 2022;27(10):103314; doi: 10.1016/j.drudis.2022.06.014
56.
BhattMW, SharmaS.An IoMT-based approach for real-time monitoring using wearable neuro-sensors. J Healthc Eng, 2023;2023:1066547; doi: 10.1155/2023/1066547
57.
LiaoY, ThompsonC, PetersonS, et al.The future of wearable technologies and remote monitoring in health care. Am Soc Clin Oncol Educ Book Am Soc Clin Oncol Annu Meet, 2019;39:115–121; doi: 10.1200/EDBK_238919
58.
McGregorBA, VidalGA, ShahSA, et al.Remote oncology care: Review of current technology and future directions. Cureus n.d.;12(8):e10156; doi: 10.7759/cureus.10156
