{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T15:56:49Z","timestamp":1777651009004,"version":"3.51.4"},"reference-count":240,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,3,4]],"date-time":"2025-03-04T00:00:00Z","timestamp":1741046400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Digit. Health"],"abstract":"<jats:p>Early diagnosis and accurate prognosis play a pivotal role in the clinical management of cancer and in preventing cancer-related mortalities. The burgeoning population of Asia in general and South Asian countries like India in particular pose significant challenges to the healthcare system. Regrettably, the demand for healthcare services in India far exceeds the available resources, resulting in overcrowded hospitals, prolonged wait times, and inadequate facilities. The scarcity of trained manpower in rural settings, lack of awareness and low penetrance of screening programs further compounded the problem. Artificial Intelligence (AI), driven by advancements in machine learning, deep learning, and natural language processing, can profoundly transform the underlying shortcomings in the healthcare industry, more for populous nations like India. With about 1.4 million cancer cases reported annually and 0.9 million deaths, India has a significant cancer burden that surpassed several nations. Further, India's diverse and large ethnic population is a data goldmine for healthcare research. Under these circumstances, AI-assisted technology, coupled with digital health solutions, could support effective oncology care and reduce the economic burden of GDP loss in terms of years of potential productive life lost (YPPLL) due to India's stupendous cancer burden. This review explores different aspects of cancer management, such as prevention, diagnosis, precision treatment, prognosis, and drug discovery, where AI has demonstrated promising clinical results. By harnessing the capabilities of AI in oncology research, healthcare professionals can enhance their ability to diagnose cancers at earlier stages, leading to more effective treatments and improved patient outcomes. With continued research and development, AI and digital health can play a transformative role in mitigating the challenges posed by the growing population and advancing the fight against cancer in India. Moreover, AI-driven technologies can assist in tailoring personalized treatment plans, optimizing therapeutic strategies, and supporting oncologists in making well-informed decisions. However, it is essential to ensure responsible implementation and address potential ethical and privacy concerns associated with using AI in healthcare.<\/jats:p>","DOI":"10.3389\/fdgth.2025.1550407","type":"journal-article","created":{"date-parts":[[2025,3,4]],"date-time":"2025-03-04T07:11:57Z","timestamp":1741072317000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":15,"title":["Role of AI in empowering and redefining the oncology care landscape: perspective from a developing nation"],"prefix":"10.3389","volume":"7","author":[{"given":"Isha","family":"Goel","sequence":"first","affiliation":[]},{"given":"Yogendra","family":"Bhaskar","sequence":"additional","affiliation":[]},{"given":"Nand","family":"Kumar","sequence":"additional","affiliation":[]},{"given":"Sunil","family":"Singh","sequence":"additional","affiliation":[]},{"given":"Mohammed","family":"Amanullah","sequence":"additional","affiliation":[]},{"given":"Ruby","family":"Dhar","sequence":"additional","affiliation":[]},{"given":"Subhradip","family":"Karmakar","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2025,3,4]]},"reference":[{"key":"B1","article-title":"Population (total - India)","year":"2023"},{"key":"B2","doi-asserted-by":"publisher","first-page":"229","DOI":"10.3322\/caac.21834","article-title":"Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"74","author":"Bray","year":"2024","journal-title":"CA Cancer J Clin"},{"key":"B3","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1186\/s12960-021-00575-2","article-title":"Size, composition and distribution of health workforce in India: why, and where to invest?","volume":"19","author":"Karan","year":"2021","journal-title":"Hum Resour Health"},{"key":"B4","article-title":"Medical doctors (per 10000 population)","year":"2023"},{"key":"B5","doi-asserted-by":"publisher","first-page":"1347","DOI":"10.1056\/nejmra1814259","article-title":"Machine learning in medicine","volume":"380","author":"Rajkomar","year":"2019","journal-title":"N Engl J Med"},{"key":"B6","doi-asserted-by":"publisher","first-page":"3713","DOI":"10.1007\/s11042-022-13428-4","article-title":"Natural language processing: state of the art, current trends and challenges","volume":"82","author":"Khurana","year":"2023","journal-title":"Multimed Tools Appl"},{"key":"B7","doi-asserted-by":"crossref","DOI":"10.1109\/ICIS.2017.7960069","article-title":"Application of deep learning in object detection","author":"Zhou","year":"2017"},{"key":"B8","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1016\/j.cogr.2023.04.001","article-title":"Artificial intelligence, machine learning and deep learning in advanced robotics, a review","volume":"3","author":"Soori","year":"2023","journal-title":"Cogn Robot"},{"key":"B9","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1016\/J.COGSYS.2016.09.005","article-title":"Enabling robotic social intelligence by engineering human social-cognitive mechanisms","volume":"43","author":"Wiltshire","year":"2017","journal-title":"Cogn Syst Res"},{"key":"B10","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1093\/cid\/cix731","article-title":"Machine learning for healthcare: on the verge of a major shift in healthcare epidemiology","volume":"66","author":"Wiens","year":"2018","journal-title":"Clin Infect Dis"},{"key":"B11","doi-asserted-by":"crossref","DOI":"10.1007\/978-981-13-7403-6_11","article-title":"Supervised classification algorithms in machine learning: a survey and review","volume-title":"Emerging Technology in Modelling and Graphics. Advances in Intelligent Systems and Computing","author":"Sen","year":"2020"},{"key":"B12","doi-asserted-by":"publisher","first-page":"851","DOI":"10.1093\/BIB\/BBW068","article-title":"Deep learning in bioinformatics","volume":"18","author":"Min","year":"2017","journal-title":"Brief Bioinform"},{"key":"B13","doi-asserted-by":"publisher","first-page":"102888","DOI":"10.1016\/j.inffus.2024.102888","article-title":"A comprehensive survey of large language models and multimodal large language models in medicine","volume":"117","author":"Xiao","year":"2024","journal-title":"Inf Fusion"},{"key":"B14","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1016\/j.eij.2011.04.003","article-title":"Suite of decision tree-based classification algorithms on cancer gene expression data","volume":"12","author":"Snousy","year":"2011","journal-title":"Egypt Inform J"},{"key":"B15","doi-asserted-by":"publisher","first-page":"3744","DOI":"10.1002\/cam4.5060","article-title":"Using machine learning for mortality prediction and risk stratification in atezolizumab? Treated cancer patients: integrative analysis of eight clinical trials","volume":"12","author":"Wu","year":"2022","journal-title":"Cancer Med"},{"key":"B16","doi-asserted-by":"publisher","first-page":"502","DOI":"10.1089\/cmb.2022.0422","article-title":"Identifying biomarkers using support vector machine to understand the racial disparity in triple-negative breast cancer","volume":"30","author":"Sahoo","year":"2023","journal-title":"J Comput Biol"},{"key":"B17","doi-asserted-by":"publisher","first-page":"16444","DOI":"10.1038\/s41598-018-34753-5","article-title":"Machine learning predicts individual cancer patient responses to therapeutic drugs with high accuracy","volume":"8","author":"Huang","year":"2018","journal-title":"Sci Rep"},{"key":"B18","doi-asserted-by":"publisher","first-page":"e29056","DOI":"10.2196\/29056","article-title":"Use of multiple correspondence analysis and k-means to explore associations between risk factors and likelihood of colorectal cancer: cross-sectional study","volume":"24","author":"Florensa","year":"2022","journal-title":"J Med Internet Res"},{"key":"B19","doi-asserted-by":"publisher","first-page":"45602","DOI":"10.1038\/srep45602","article-title":"Clustering by fast search and merge of local density peaks for gene expression microarray data","volume":"7","author":"Mehmood","year":"2017","journal-title":"Sci Rep"},{"key":"B20","doi-asserted-by":"publisher","first-page":"2809","DOI":"10.3390\/s20102809","article-title":"Data-driven cervical cancer prediction model with outlier detection and over-sampling methods","volume":"20","author":"Ijaz","year":"2020","journal-title":"Sensors"},{"key":"B21","doi-asserted-by":"publisher","first-page":"100125","DOI":"10.1016\/j.health.2022.100125","article-title":"A performance analysis of dimensionality reduction algorithms in machine learning models for cancer prediction","volume":"3","author":"Kabir","year":"2022","journal-title":"Healthc Anal"},{"key":"B22","doi-asserted-by":"publisher","first-page":"3294","DOI":"10.1002\/sim.3720","article-title":"Reinforcement learning design for cancer clinical trials","volume":"28","author":"Zhao","year":"2009","journal-title":"Stat Med"},{"key":"B23","doi-asserted-by":"publisher","first-page":"6690","DOI":"10.1002\/mp.12625","article-title":"Deep reinforcement learning for automated radiation adaptation in lung cancer","volume":"44","author":"Tseng","year":"2017","journal-title":"Med Phys"},{"key":"B24","doi-asserted-by":"publisher","first-page":"4034","DOI":"10.1002\/sim.9491","article-title":"Deep reinforcement learning for personalized treatment recommendation","volume":"41","author":"Liu","year":"2022","journal-title":"Stat Med"},{"key":"B25","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis","volume":"42","author":"Litjens","year":"2017","journal-title":"Med Image Anal"},{"key":"B26","doi-asserted-by":"publisher","first-page":"218","DOI":"10.1016\/j.jbi.2017.04.001","article-title":"Predicting healthcare trajectories from medical records: a deep learning approach","volume":"69","author":"Pham","year":"2017","journal-title":"J Biomed Inform"},{"key":"B27","doi-asserted-by":"publisher","first-page":"946329","DOI":"10.3389\/fbioe.2022.946329","article-title":"MLP-based regression prediction model for compound bioactivity","volume":"10","author":"Qin","year":"2022","journal-title":"Front Bioeng Biotechnol"},{"key":"B28","doi-asserted-by":"publisher","first-page":"104751","DOI":"10.1016\/j.jbi.2024.104751","article-title":"Early multi-cancer detection through deep learning: an anomaly detection approach using variational autoencoder","volume":"160","author":"Sado","year":"2024","journal-title":"J Biomed Inform"},{"key":"B29","doi-asserted-by":"publisher","first-page":"1208","DOI":"10.1093\/jamia\/ocac040","article-title":"CancerBERT: a cancer domain-specific language model for extracting breast cancer phenotypes from electronic health records","volume":"29","author":"Zhou","year":"2022","journal-title":"J Am Med Inform Assoc"},{"key":"B30","doi-asserted-by":"publisher","first-page":"877","DOI":"10.3390\/biomedinformatics4020049","article-title":"Exploring the role of CHATGPT in oncology: providing information and support for cancer patients","volume":"4","author":"C\u00e8","year":"2024","journal-title":"BioMedInformatics"},{"key":"B31","doi-asserted-by":"publisher","first-page":"100157","DOI":"10.1016\/j.glmedi.2024.100157","article-title":"Large language models for improving cancer diagnosis and management in primary health care settings","volume":"4","author":"Andrew","year":"2024","journal-title":"J Med Surg Public Health"},{"key":"B32","doi-asserted-by":"publisher","first-page":"7111","DOI":"10.3390\/app13127111","article-title":"T5-Based model for abstractive summarization: a semi-supervised learning approach with consistency loss functions","volume":"13","author":"Wang","year":"2023","journal-title":"Appl Sci"},{"key":"B33","doi-asserted-by":"publisher","first-page":"e0315339","DOI":"10.1371\/journal.pone.0315339","article-title":"CLIP-based multimodal endorectal ultrasound enhances prediction of neoadjuvant chemoradiotherapy response in locally advanced rectal cancer","volume":"19","author":"Zhang","year":"2024","journal-title":"PLoS One"},{"key":"B34","doi-asserted-by":"publisher","first-page":"380","DOI":"10.3390\/bioengineering10030380","article-title":"Vision-language model for visual question answering in medical imagery","volume":"10","author":"Bazi","year":"2023","journal-title":"Bioengineering"},{"key":"B35","doi-asserted-by":"publisher","first-page":"743","DOI":"10.1038\/s41551-023-01045-x","article-title":"A transformer-based representation-learning model with unified processing of multimodal input for clinical diagnostics","volume":"7","author":"Zhou","year":"2023","journal-title":"Nat Biomed Eng"},{"key":"B36","doi-asserted-by":"publisher","first-page":"e42370","DOI":"10.7759\/cureus.42370","article-title":"Robotic surgery: a comprehensive review of the literature and current trends","volume":"15","author":"Rivero-Moreno","year":"2023","journal-title":"Cureus"},{"key":"B37","doi-asserted-by":"publisher","first-page":"A68","DOI":"10.5114\/wo.2014.47136","article-title":"The cancer genome atlas (TCGA): an immeasurable source of knowledge","volume":"19","author":"Tomczak","year":"2015","journal-title":"Contemp Oncol"},{"key":"B38","doi-asserted-by":"publisher","first-page":"470","DOI":"10.1038\/nrg.2016.69","article-title":"Crowdsourcing biomedical research: leveraging communities as innovation engines","volume":"17","author":"Saez-Rodriguez","year":"2016","journal-title":"Nat Rev Genet"},{"key":"B39","article-title":"Digital mammography DREAM challenge - sage bionetworks","year":"2019"},{"key":"B40","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1038\/s41586-019-1799-6","article-title":"International evaluation of an AI system for breast cancer screening","volume":"577","author":"McKinney","year":"2020","journal-title":"Nature"},{"key":"B41","doi-asserted-by":"crossref","DOI":"10.1109\/ICHI.2017.62","article-title":"Breast cancer risk prediction using electronic health records","author":"Wu","year":"2017"},{"key":"B42","doi-asserted-by":"publisher","first-page":"688","DOI":"10.1016\/j.cell.2020.01.021","article-title":"A deep learning approach to antibiotic discovery","volume":"180","author":"Stokes","year":"2020","journal-title":"Cell"},{"key":"B43","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1038\/s41591-019-0724-8","article-title":"Prediction of gestational diabetes based on nationwide electronic health records","volume":"26","author":"Artzi","year":"2020","journal-title":"Nat Med"},{"key":"B44","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/j.inffus.2018.09.012","article-title":"Machine learning for integrating data in biology and medicine: principles, practice, and opportunities","volume":"50","author":"Zitnik","year":"2019","journal-title":"Inf Fusion"},{"key":"B45","doi-asserted-by":"publisher","first-page":"1045","DOI":"10.1007\/s10278-013-9622-7","article-title":"The cancer imaging archive (TCIA): maintaining and operating a public information repository","volume":"26","author":"Clark","year":"2013","journal-title":"J Digit Imaging"},{"key":"B46","doi-asserted-by":"publisher","first-page":"671","DOI":"10.1038\/nmeth.2089","article-title":"NIH Image to ImageJ: 25 years of image analysis","volume":"9","author":"Schneider","year":"2012","journal-title":"Nat Methods"},{"key":"B47","first-page":"265","article-title":"Tensorflow: a system for large-scale machine learning","author":"Abadi","year":"2016"},{"key":"B48","article-title":"Deep learning with python","author":"Chollet","year":"2017"},{"key":"B49","doi-asserted-by":"publisher","first-page":"2825","DOI":"10.5555\/1953048.2078195","article-title":"Scikit-learn: machine learning in python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J Mach Learn Res"},{"key":"B50","article-title":"Very deep convolutional networks for large-scale image recognition. Computer vision and pattern recognition (2014)","author":"Simonyan","year":""},{"key":"B51","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"key":"B52","doi-asserted-by":"publisher","first-page":"8026","DOI":"10.48550\/arXiv.1912.01703","article-title":"Pytorch: an imperative style, high-performance deep learning library","volume":"32","author":"Paszke","year":"2019","journal-title":"Adv Neural Inf Process Syst"},{"key":"B53","doi-asserted-by":"publisher","first-page":"916","DOI":"10.1093\/JNCI\/DJY222","article-title":"Stand-alone artificial intelligence for breast cancer detection in mammography: comparison with 101 radiologists","volume":"111","author":"Rodriguez-Ruiz","year":"2019","journal-title":"J Natl Cancer Inst"},{"key":"B54","doi-asserted-by":"publisher","first-page":"e222733","DOI":"10.1148\/radiol.222733","article-title":"Comparison of mammography AI algorithms with a clinical risk model for 5-year breast cancer risk prediction: an observational study","volume":"307","author":"Arasu","year":"2023","journal-title":"Radiology"},{"key":"B55","doi-asserted-by":"publisher","first-page":"642","DOI":"10.1007\/s12282-020-01061-8","article-title":"Artificial intelligence for breast cancer detection in mammography: experience of use of the ScreenPoint medical transpara system in 310 Japanese women","volume":"27","author":"Sasaki","year":"2020","journal-title":"Breast Cancer"},{"key":"B56","doi-asserted-by":"publisher","first-page":"625","DOI":"10.1007\/s10278-019-00192-5","article-title":"Improved cancer detection using artificial intelligence: a retrospective evaluation of missed cancers on mammography","volume":"32","author":"Watanabe","year":"2019","journal-title":"J Digit Imaging"},{"key":"B57","doi-asserted-by":"publisher","first-page":"1027","DOI":"10.1016\/j.rcl.2021.07.010","article-title":"Clinical artificial intelligence applications: breast imaging","volume":"59","author":"Hu","year":"2021","journal-title":"Radiol Clin"},{"key":"B58","doi-asserted-by":"publisher","first-page":"e28945","DOI":"10.7759\/cureus.28945","article-title":"Artificial intelligence in breast the emerging future of modern medicine","volume":"14","author":"Mahant","year":"2022","journal-title":"Cureus"},{"key":"B59","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1148\/RADIOL.2020201620","article-title":"Identification of women at high risk of breast cancer who need supplemental screening","volume":"297","author":"Eriksson","year":"2020","journal-title":"Radiology"},{"key":"B60","doi-asserted-by":"publisher","first-page":"eaba4373","DOI":"10.1126\/scitranslmed.aba4373","article-title":"Toward robust mammography-based models for breast cancer risk","volume":"13","author":"Yala","year":"2021","journal-title":"Sci Transl Med"},{"key":"B61","doi-asserted-by":"publisher","first-page":"704","DOI":"10.7326\/M20-1868","article-title":"Deep learning using chest radiographs to identify high-risk smokers for lung cancer screening computed tomography: development and validation of a prediction model","volume":"173","author":"Lu","year":"2020","journal-title":"Ann Intern Med"},{"key":"B62","doi-asserted-by":"publisher","first-page":"djv036","DOI":"10.1093\/jnci\/djv036","article-title":"Prediction of breast cancer risk based on profiling with common genetic variants","volume":"107","author":"Mavaddat","year":"2015","journal-title":"J Natl Cancer Inst"},{"key":"B63","doi-asserted-by":"publisher","first-page":"983","DOI":"10.1038\/nbt.4235","article-title":"A universal SNP and small-indel variant caller using deep neural networks","volume":"36","author":"Poplin","year":"2018","journal-title":"Nat Biotechnol"},{"key":"B64","doi-asserted-by":"publisher","first-page":"1219","DOI":"10.1038\/s41588-018-0183-z","article-title":"Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations","volume":"50","author":"Khera","year":"2018","journal-title":"Nat Genet"},{"key":"B65","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1016\/j.ajhg.2018.11.002","article-title":"Polygenic risk scores for prediction of breast cancer and breast cancer subtypes","volume":"104","author":"Mavaddat","year":"2019","journal-title":"Am J Hum Genet"},{"key":"B66","doi-asserted-by":"publisher","first-page":"17075","DOI":"10.1038\/s41598-020-74172-z","article-title":"Prostate cancer risk prediction using a polygenic risk score","volume":"10","author":"Sipeky","year":"2020","journal-title":"Sci Rep"},{"key":"B67","doi-asserted-by":"publisher","first-page":"e14037","DOI":"10.1111\/eci.14037","article-title":"Hypothesis? Free discovery of novel cancer predictors using machine learning","volume":"53","author":"Madakkatel","year":"2023","journal-title":"Eur J Clin Investig"},{"key":"B68","doi-asserted-by":"publisher","first-page":"mjad023","DOI":"10.1093\/jmcb\/mjad023","article-title":"Accurate prediction of pan-cancer types using machine learning with minimal number of DNA methylation sites","volume":"15","author":"Ning","year":"2023","journal-title":"J Mol Cell Biol"},{"key":"B69","doi-asserted-by":"publisher","first-page":"e0226461","DOI":"10.1371\/journal.pone.0226461","article-title":"Predicting cancer origins with a DNA methylation-based deep neural network model","volume":"15","author":"Zheng","year":"2020","journal-title":"PLoS One"},{"key":"B70","doi-asserted-by":"publisher","first-page":"1855","DOI":"10.1007\/s11760-022-02396-9","article-title":"Cancer prediction with gene expression profiling and differential evolution","volume":"17","author":"Vijaya Lakshmi","year":"2023","journal-title":"Signal Image Video Process"},{"key":"B71","doi-asserted-by":"publisher","first-page":"332","DOI":"10.1186\/s12859-021-04256-8","article-title":"A machine learning framework that integrates multi-omics data predicts cancer-related LncRNAs","volume":"22","author":"Yuan","year":"2021","journal-title":"BMC Bioinformatics"},{"key":"B72","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1186\/s12920-018-0436-9","article-title":"CRlncRC: a machine learning-based method for cancer-related long noncoding RNA identification using integrated features","volume":"11","author":"Zhang","year":"2018","journal-title":"BMC Med Genomics"},{"key":"B73","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1158\/1055-9965.EPI-22-0873","article-title":"Machine learning and real-world data to predict lung cancer risk in routine care","volume":"32","author":"Chandran","year":"2023","journal-title":"Cancer Epidemiol Biomarkers Prev"},{"key":"B74","doi-asserted-by":"publisher","first-page":"e26256","DOI":"10.2196\/26256","article-title":"Artificial intelligence-based prediction of lung cancer risk using nonimaging electronic medical records: deep learning approach","volume":"23","author":"Yeh","year":"2021","journal-title":"J Med Internet Res"},{"key":"B75","doi-asserted-by":"publisher","first-page":"E2248793","DOI":"10.1001\/jamanetworkopen.2022.48793","article-title":"Validation of a deep learning-based model to predict lung cancer risk using chest radiographs and electronic medical record data","volume":"5","author":"Raghu","year":"2022","journal-title":"JAMA Netw Open"},{"key":"B76","doi-asserted-by":"publisher","first-page":"e2400146","DOI":"10.1200\/GO.24.00146","article-title":"Development and clinical validation of visual inspection with acetic acid application-artificial intelligence tool using cervical images in screen-and-treat visual screening for cervical cancer in south India: a pilot study","volume":"10","author":"Poli","year":"2024","journal-title":"JCO Glob Oncol"},{"key":"B77","article-title":"Meghalaya cancer care project.","year":"2023"},{"key":"B78","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1038\/s41746-024-01368-2","article-title":"A real world evaluation of an innovative artificial intelligence tool for population-level breast cancer screening","volume":"8","author":"Adapa","year":"2025","journal-title":"NPJ Digit Med"},{"key":"B79","doi-asserted-by":"publisher","first-page":"954","DOI":"10.1038\/s41591-019-0447-x","article-title":"End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography","volume":"25","author":"Ardila","year":"2019","journal-title":"Nat Med"},{"key":"B80","doi-asserted-by":"publisher","first-page":"607","DOI":"10.1148\/radiol.2019190938","article-title":"Classification of cancer at prostate MRI: deep learning versus clinical PI-RADS assessment","volume":"293","author":"Schelb","year":"2019","journal-title":"Radiology"},{"key":"B81","doi-asserted-by":"publisher","first-page":"1570","DOI":"10.1038\/s41598-018-38381-x","article-title":"Objective risk stratification of prostate cancer using machine learning and radiomics applied to multiparametric magnetic resonance images","volume":"9","author":"Varghese","year":"2019","journal-title":"Sci Rep"},{"key":"B82","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1148\/radiol.2018181371","article-title":"Detection of breast cancer with mammography: effect of an artificial intelligence support system","volume":"290","author":"Rodr\u00edguez-Ruiz","year":"2019","journal-title":"Radiology"},{"key":"B83","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.compbiomed.2019.01.026","article-title":"Grading of hepatocellular carcinoma using 3D SE-DenseNet in dynamic enhanced MR images","volume":"107","author":"Zhou","year":"2019","journal-title":"Comput Biol Med"},{"key":"B84","doi-asserted-by":"publisher","first-page":"741","DOI":"10.1038\/s41551-018-0301-3","article-title":"Development and validation of a deep-learning algorithm for the detection of polyps during colonoscopy","volume":"2","author":"Wang","year":"2018","journal-title":"Nat Biomed Eng"},{"key":"B85","doi-asserted-by":"publisher","first-page":"954","DOI":"10.1007\/s12664-024-01679-y","article-title":"Imaging colonic polyps in 2024","volume":"43","author":"Nagarajan","year":"2024","journal-title":"Indian J Gastroenterol"},{"key":"B86","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1038\/nature21056","article-title":"Dermatologist-level classification of skin cancer with deep neural networks","volume":"542","author":"Esteva","year":"2017","journal-title":"Nature"},{"key":"B87","doi-asserted-by":"publisher","first-page":"e407","DOI":"10.1016\/S2589-7500(20)30159-X","article-title":"An artificial intelligence algorithm for prostate cancer diagnosis in whole slide images of core needle biopsies: a blinded clinical validation and deployment study","volume":"2","author":"Pantanowitz","year":"2020","journal-title":"Lancet Digit Health"},{"key":"B88","doi-asserted-by":"publisher","first-page":"1901","DOI":"10.3390\/cancers11121901","article-title":"Parallel structure deep neural network using CNN and RNN with an attention mechanism for breast cancer histology image classification","volume":"11","author":"Yao","year":"2019","journal-title":"Cancers"},{"key":"B89","doi-asserted-by":"publisher","first-page":"46450","DOI":"10.1038\/srep46450","article-title":"Accurate and reproducible invasive breast cancer detection in whole-slide images: a deep learning approach for quantifying tumor extent","volume":"7","author":"Cruz-Roa","year":"2017","journal-title":"Sci Rep"},{"key":"B90","doi-asserted-by":"publisher","first-page":"1559","DOI":"10.1038\/s41591-018-0177-5","article-title":"Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning","volume":"24","author":"Coudray","year":"2018","journal-title":"Nat Med"},{"key":"B91","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1016\/j.celrep.2018.03.086","article-title":"Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images","volume":"23","author":"Saltz","year":"2018","journal-title":"Cell Rep"},{"key":"B92","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.ophtha.2019.05.029","article-title":"Development and validation of deep learning models for screening multiple abnormal findings in retinal fundus images","volume":"127","author":"Son","year":"2020","journal-title":"Ophthalmology"},{"key":"B93","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1038\/nature10983","article-title":"The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups","volume":"486","author":"Curtis","year":"2012","journal-title":"Nature"},{"key":"B94","doi-asserted-by":"publisher","first-page":"2906","DOI":"10.1093\/bioinformatics\/btp543","article-title":"Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis","volume":"25","author":"Shen","year":"2009","journal-title":"Bioinformatics"},{"key":"B95","doi-asserted-by":"publisher","first-page":"1476","DOI":"10.1093\/bioinformatics\/btz769","article-title":"Deep-learning approach to identifying cancer subtypes using high-dimensional genomic data","volume":"36","author":"Chen","year":"2020","journal-title":"Bioinformatics"},{"key":"B96","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1186\/s43046-023-00173-4","article-title":"Mutational signatures for breast cancer diagnosis using artificial intelligence","volume":"35","author":"Odhiambo","year":"2023","journal-title":"J Egypt Natl Canc Inst"},{"key":"B97","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1038\/s41389-019-0157-8","article-title":"DeepCC: a novel deep learning-based framework for cancer molecular subtype classification","volume":"8","author":"Gao","year":"2019","journal-title":"Oncogenesis"},{"key":"B98","doi-asserted-by":"publisher","first-page":"1187","DOI":"10.1109\/TCBB.2019.2905553","article-title":"Gene expression classification of lung adenocarcinoma into molecular subtypes","volume":"17","author":"Hu","year":"2020","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"B99","doi-asserted-by":"publisher","first-page":"e0203824","DOI":"10.1371\/journal.pone.0203824","article-title":"Dissecting cancer heterogeneity based on dimension reduction of transcriptomic profiles using extreme learning machines","volume":"13","author":"Wang","year":"2018","journal-title":"PLoS One"},{"key":"B100","doi-asserted-by":"publisher","first-page":"1327","DOI":"10.1214\/11-AOAS533","article-title":"Integrative model-based clustering of microarray methylation and expression data","volume":"6","author":"Kormaksson","year":"2012","journal-title":"Ann Appl Stat"},{"key":"B101","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1038\/NATURE26000","article-title":"DNA methylation-based classification of central nervous system tumours","volume":"555","author":"Capper","year":"2018","journal-title":"Nature"},{"key":"B102","doi-asserted-by":"publisher","first-page":"352","DOI":"10.3390\/cancers14020352","article-title":"Noncoding RNAs and deep learning neural network discriminate multi-cancer types","volume":"14","author":"Wang","year":"2022","journal-title":"Cancers"},{"key":"B103","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.ymeth.2019.06.017","article-title":"Pathway-based deep clustering for molecular subtyping of cancer","volume":"173","author":"Mallavarapu","year":"2020","journal-title":"Methods"},{"key":"B104","doi-asserted-by":"publisher","first-page":"1113","DOI":"10.1038\/ng.2764","article-title":"The cancer genome atlas pan-cancer analysis project","volume":"45","author":"Weinstein","year":"2013","journal-title":"Nat Genet"},{"key":"B105","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1038\/nature11412","article-title":"Comprehensive molecular portraits of human breast tumours","volume":"490","author":"Koboldt","year":"2012","journal-title":"Nature"},{"key":"B106","doi-asserted-by":"publisher","first-page":"11263","DOI":"10.1038\/ncomms11263","article-title":"Integrated multi-omics analysis of oligodendroglial tumours identifies three subgroups of 1p\/19q co-deleted gliomas","volume":"7","author":"Kamoun","year":"2016","journal-title":"Nat Commun"},{"key":"B107","doi-asserted-by":"publisher","first-page":"2013","DOI":"10.3390\/cancers13092013","article-title":"Performance comparison of deep learning autoencoders for cancer subtype detection using multi-omics data","volume":"13","author":"Franco","year":"2021","journal-title":"Cancers"},{"key":"B108","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1038\/s41586-020-1969-6","article-title":"Pan-cancer analysis of whole genomes","volume":"578","author":"Campbell","year":"2020","journal-title":"Nature"},{"key":"B109","doi-asserted-by":"publisher","first-page":"728","DOI":"10.1038\/S41467-019-13825-8","article-title":"A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns","volume":"11","author":"Jiao","year":"2020","journal-title":"Nat Commun"},{"key":"B110","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1007\/s10916-018-0975-9","article-title":"Profiling lung cancer patients using electronic health records","volume":"42","author":"Menasalvas Ruiz","year":"2018","journal-title":"J Med Syst"},{"key":"B111","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1097\/PTS.0000000000000127","article-title":"Using natural language processing to extract abnormal results from cancer screening reports","volume":"13","author":"Moore","year":"2017","journal-title":"J Patient Saf"},{"key":"B112","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1200\/cci.17.00128","article-title":"Automated extraction of grade, stage, and quality information from transurethral resection of bladder tumor pathology reports using natural language processing","volume":"2","author":"Glaser","year":"2018","journal-title":"JCO Clin Cancer Inform"},{"key":"B113","doi-asserted-by":"publisher","first-page":"102811","DOI":"10.1016\/j.ebiom.2020.102811","article-title":"Machine learning-based genome-wide interrogation of somatic copy number aberrations in circulating tumor DNA for early detection of hepatocellular carcinoma","volume":"56","author":"Tao","year":"2020","journal-title":"EBioMedicine"},{"key":"B114","doi-asserted-by":"publisher","first-page":"593831","DOI":"10.3389\/fonc.2020.593831","article-title":"Integrating liquid biopsy and radiomics to monitor clonal heterogeneity of EGFR-positive non-small cell lung cancer","volume":"10","author":"Cucchiara","year":"2020","journal-title":"Front Oncol"},{"key":"B115","doi-asserted-by":"publisher","first-page":"2166","DOI":"10.1039\/d0lc00096e","article-title":"A web-based automated machine learning platform to analyze liquid biopsy data","volume":"20","author":"Shen","year":"2020","journal-title":"Lab Chip"},{"key":"B116","doi-asserted-by":"publisher","first-page":"449","DOI":"10.1111\/j.1471-4159.2011.07307.x","article-title":"A specific miRNA signature in the peripheral blood of glioblastoma patients","volume":"118","author":"Roth","year":"2011","journal-title":"J Neurochem"},{"key":"B117","doi-asserted-by":"publisher","first-page":"1206","DOI":"10.1038\/s41596-019-0139-5","article-title":"RNA sequencing and swarm intelligence-enhanced classification algorithm development for blood-based disease diagnostics using spliced blood platelet RNA","volume":"14","author":"Best","year":"2019","journal-title":"Nat Protoc"},{"key":"B118","doi-asserted-by":"publisher","first-page":"5435","DOI":"10.1021\/acsnano.9b09119","article-title":"Early-stage lung cancer diagnosis by deep learning-based spectroscopic analysis of circulating exosomes","volume":"14","author":"Shin","year":"2020","journal-title":"ACS Nano"},{"key":"B119","doi-asserted-by":"publisher","first-page":"926","DOI":"10.1126\/science.aar3247","article-title":"Detection and localization of surgically resectable cancers with a multi-analyte blood test","volume":"359","author":"Cohen","year":"2018","journal-title":"Science"},{"key":"B120","article-title":"AIIMS unveils indigenously developed technology for early detection of cancer","year":"2024"},{"key":"B121","article-title":"Apollo cancer centres has successfully launched India\u2019s first AI-precision oncology centre","year":"2024"},{"key":"B122","doi-asserted-by":"publisher","first-page":"1202","DOI":"10.1038\/nbt.2877","article-title":"A community effort to assess and improve drug sensitivity prediction algorithms","volume":"32","author":"Costello","year":"2014","journal-title":"Nat Biotechnol"},{"key":"B123","doi-asserted-by":"publisher","first-page":"4784","DOI":"10.1158\/1078-0432.CCR-14-1096","article-title":"A three-microRNA signature predicts responses to platinum-based doublet chemotherapy in patients with lung adenocarcinoma","volume":"20","author":"Saito","year":"2014","journal-title":"Clin Cancer Res"},{"key":"B124","doi-asserted-by":"publisher","first-page":"912","DOI":"10.1038\/s41588-019-0390-2","article-title":"Detecting the mutational signature of homologous recombination deficiency in clinical samples","volume":"51","author":"Gulhan","year":"2019","journal-title":"Nat Genet"},{"key":"B125","doi-asserted-by":"publisher","first-page":"1743","DOI":"10.1101\/gr.221077.117","article-title":"Discovering novel pharmacogenomic biomarkers by imputing drug response in cancer patients from large genomics studies","volume":"27","author":"Geeleher","year":"2017","journal-title":"Genome Res"},{"key":"B126","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1016\/j.semcancer.2022.12.009","article-title":"Artificial intelligence-based multi-omics analysis fuels cancer precision medicine","volume":"88","author":"He","year":"2023","journal-title":"Semin Cancer Biol"},{"key":"B127","doi-asserted-by":"publisher","first-page":"5792","DOI":"10.1038\/s41598-021-84973-5","article-title":"A meta-analysis of watson for oncology in clinical application","volume":"11","author":"Jie","year":"2021","journal-title":"Sci Rep"},{"key":"B128","doi-asserted-by":"publisher","first-page":"585364","DOI":"10.3389\/FENDO.2021.585364","article-title":"Adequacy and effectiveness of watson for oncology in the treatment of thyroid carcinoma","volume":"12","author":"Yun","year":"2021","journal-title":"Front Endocrinol"},{"key":"B129","doi-asserted-by":"publisher","first-page":"2875","DOI":"10.1002\/mp.12930","article-title":"Knowledge-based automated planning for oropharyngeal cancer","volume":"45","author":"Babier","year":"2018","journal-title":"Med Phys"},{"key":"B130","doi-asserted-by":"publisher","first-page":"905","DOI":"10.1002\/cncr.30655","article-title":"Artificial intelligence platform for oncology could assist in treatment decisions","volume":"123","author":"Printz","year":"2017","journal-title":"Cancer"},{"key":"B131","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1200\/po.17.00011","article-title":"OncoKB: a precision oncology knowledge base","volume":"1","author":"Chakravarty","year":"2017","journal-title":"JCO Precis Oncol"},{"key":"B132","doi-asserted-by":"publisher","first-page":"1800104","DOI":"10.1002\/adtp.201800104","article-title":"Modulating BET bromodomain inhibitor ZEN-3694 and enzalutamide combination dosing in a metastatic prostate cancer patient using CURATE.AI, an artificial intelligence platform","volume":"1","author":"Pantuck","year":"2018","journal-title":"Adv Ther"},{"key":"B133","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.molonc.2015.07.006","article-title":"Genomic signatures for paclitaxel and gemcitabine resistance in breast cancer derived by machine learning","volume":"10","author":"Dorman","year":"2016","journal-title":"Mol Oncol"},{"key":"B134","doi-asserted-by":"publisher","first-page":"1180","DOI":"10.1016\/S1470-2045(18)30413-3","article-title":"A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study","volume":"19","author":"Sun","year":"2018","journal-title":"Lancet Oncol"},{"key":"B135","doi-asserted-by":"publisher","first-page":"689","DOI":"10.1080\/1744666X.2019.1623670","article-title":"Artificial intelligence and immunotherapy","volume":"15","author":"Jabbari","year":"2019","journal-title":"Expert Rev Clin Immunol"},{"key":"B136","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1038\/nbt.4313","article-title":"Deep learning using tumor HLA peptide mass spectrometry datasets improves neoantigen identification","volume":"37","author":"Bulik-Sullivan","year":"2019","journal-title":"Nat Biotechnol"},{"key":"B137","doi-asserted-by":"publisher","first-page":"998","DOI":"10.1093\/annonc\/mdz108","article-title":"Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers","volume":"30","author":"Trebeschi","year":"2019","journal-title":"Ann Oncol"},{"key":"B138","doi-asserted-by":"publisher","first-page":"4034404","DOI":"10.1155\/2022\/4034404","article-title":"A deep learning radiomics analysis for survival prediction in esophageal cancer","volume":"2022","author":"Wang","year":"2022","journal-title":"J Healthc Eng"},{"key":"B139","doi-asserted-by":"publisher","first-page":"8673","DOI":"10.1038\/s41598-023-35556-z","article-title":"An artificial neural network-based radiomics model for predicting the radiotherapy response of advanced esophageal squamous cell carcinoma patients: a multicenter study","volume":"13","author":"Xie","year":"2023","journal-title":"Sci Rep"},{"key":"B140","doi-asserted-by":"publisher","first-page":"8738","DOI":"10.1038\/s41598-017-09315-w","article-title":"Bladder cancer treatment response assessment in CT using radiomics with deep-learning","volume":"7","author":"Cha","year":"2017","journal-title":"Sci Rep"},{"key":"B141","doi-asserted-by":"publisher","first-page":"315","DOI":"10.3389\/fonc.2017.00315","article-title":"Deep deconvolutional neural network for target segmentation of nasopharyngeal cancer in planning computed tomography images","volume":"7","author":"Men","year":"2017","journal-title":"Front Oncol"},{"key":"B142","doi-asserted-by":"publisher","first-page":"677","DOI":"10.1148\/radiol.2019182012","article-title":"Deep learning for automated contouring of primary tumor volumes by MRI for nasopharyngeal carcinoma","volume":"291","author":"Lin","year":"2019","journal-title":"Radiology"},{"key":"B143","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1186\/s40644-020-00297-z","article-title":"A CT-based radiomics nomogram for differentiation of focal nodular hyperplasia from hepatocellular carcinoma in the non-cirrhotic liver","volume":"20","author":"Nie","year":"2020","journal-title":"Cancer Imaging"},{"key":"B144","doi-asserted-by":"publisher","first-page":"2497","DOI":"10.1007\/s11517-020-02229-2","article-title":"Classification of hepatocellular carcinoma and intrahepatic cholangiocarcinoma based on multi-phase CT scans","volume":"58","author":"Ponnoprat","year":"2020","journal-title":"Med Biol Eng Comput"},{"key":"B145","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1007\/s00330-020-07119-7","article-title":"Can machine learning radiomics provide pre-operative differentiation of combined hepatocellular cholangiocarcinoma from hepatocellular carcinoma and cholangiocarcinoma to inform optimal treatment planning?","volume":"31","author":"Liu","year":"2021","journal-title":"Eur Radiol"},{"key":"B146","doi-asserted-by":"publisher","first-page":"6924","DOI":"10.1007\/s00330-020-07056-5","article-title":"Preoperative prediction for pathological grade of hepatocellular carcinoma via machine learning-based radiomics","volume":"30","author":"Mao","year":"2020","journal-title":"Eur Radiol"},{"key":"B147","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.media.2016.05.004","article-title":"Brain tumor segmentation with deep neural networks","volume":"35","author":"Havaei","year":"2017","journal-title":"Med Image Anal"},{"key":"B148","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1038\/s41591-019-0715-9","article-title":"Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks","volume":"26","author":"Hollon","year":"2020","journal-title":"Nat Med"},{"key":"B149","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1097\/SLA.0000000000003262","article-title":"Artificial intelligence and the future of surgical robotics","volume":"270","author":"Panesar","year":"2019","journal-title":"Ann Surg"},{"key":"B150","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1016\/j.lungcan.2016.09.004","article-title":"Robot-assisted surgery for lung cancer: state of the art and perspectives","volume":"101","author":"Veronesi","year":"2016","journal-title":"Lung Cancer"},{"key":"B151","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1007\/s11684-020-0770-0","article-title":"Application of artificial intelligence in surgery","volume":"14","author":"Zhou","year":"2020","journal-title":"Front Med"},{"key":"B152","doi-asserted-by":"publisher","first-page":"337ra64","DOI":"10.1126\/scitranslmed.aad9398","article-title":"Supervised autonomous robotic soft tissue surgery","volume":"8","author":"Shademan","year":"2016","journal-title":"Sci Transl Med"},{"key":"B153","doi-asserted-by":"publisher","first-page":"6497","DOI":"10.1016\/j.asoc.2020.106612","article-title":"Application of artificial intelligence methods in vital signs analysis of hospitalized patients: a systematic literature review","volume":"96","author":"Kaieski","year":"2020","journal-title":"Appl Soft Comput J"},{"key":"B154","doi-asserted-by":"publisher","first-page":"104481","DOI":"10.1016\/j.compbiomed.2021.104481","article-title":"Integrating multi-omics data through deep learning for accurate cancer prognosis prediction","volume":"134","author":"Chai","year":"2021","journal-title":"Comput Biol Med"},{"key":"B155","doi-asserted-by":"publisher","first-page":"e1006076","DOI":"10.1371\/journal.pcbi.1006076","article-title":"Cox-nnet: an artificial neural network method for prognosis prediction of high-throughput omics data","volume":"14","author":"Ching","year":"2018","journal-title":"PLoS Comput Biol"},{"key":"B156","doi-asserted-by":"publisher","first-page":"3261","DOI":"10.7150\/jca.21261","article-title":"Improvement in prediction of prostate cancer prognosis with somatic mutational signatures","volume":"8","author":"Zhang","year":"2017","journal-title":"J Cancer"},{"key":"B157","doi-asserted-by":"publisher","first-page":"841","DOI":"10.1109\/TCBB.2018.2806438","article-title":"A multimodal deep neural network for human breast cancer prognosis prediction by integrating multi-dimensional data","volume":"16","author":"Sun","year":"2019","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"B158","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1177\/117693510600200030","article-title":"Applications of machine learning in cancer prediction and prognosis","volume":"2","author":"Cruz","year":"2006","journal-title":"Cancer Inform"},{"key":"B159","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1093\/bioinformatics\/btl543","article-title":"Improved breast cancer prognosis through the combination of clinical and genetic markers","volume":"23","author":"Sun","year":"2007","journal-title":"Bioinformatics"},{"key":"B160","doi-asserted-by":"publisher","first-page":"1248","DOI":"10.1158\/1078-0432.CCR-17-0853","article-title":"Deep learning-based multi-omics integration robustly predicts survival in liver cancer","volume":"24","author":"Chaudhary","year":"2018","journal-title":"Clin Cancer Res"},{"key":"B161","doi-asserted-by":"publisher","first-page":"4976","DOI":"10.3390\/cancers13194976","article-title":"Predicting overall survival time in glioblastoma patients using gradient boosting machines algorithm and recursive feature elimination technique","volume":"13","author":"Karami","year":"2021","journal-title":"Cancers"},{"key":"B162","doi-asserted-by":"publisher","first-page":"2065","DOI":"10.7150\/ijbs.28608","article-title":"Comparison of prognostic indices in NSCLC patients with brain metastases after radiosurgery","volume":"14","author":"Gao","year":"2018","journal-title":"Int J Biol Sci"},{"key":"B163","doi-asserted-by":"publisher","first-page":"212","DOI":"10.1186\/s13014-022-02186-0","article-title":"Machine learning models predict overall survival and progression free survival of non-surgical esophageal cancer patients with chemoradiotherapy based on CT image radiomics signatures","volume":"17","author":"Cui","year":"2022","journal-title":"Radiat Oncol"},{"key":"B164","doi-asserted-by":"publisher","first-page":"903372","DOI":"10.3389\/fonc.2022.903372","article-title":"Improved prediction of survival outcomes using residual cancer burden in combination with ki-67 in breast cancer patients underwent neoadjuvant chemotherapy","volume":"12","author":"Kim","year":"2022","journal-title":"Front Oncol"},{"key":"B165","doi-asserted-by":"publisher","first-page":"e0280148","DOI":"10.1371\/journal.pone.0280148","article-title":"Deep learning prediction of pathological complete response, residual cancer burden, and progression-free survival in breast cancer patients","volume":"18","author":"Dammu","year":"2023","journal-title":"PLoS One"},{"key":"B166","doi-asserted-by":"publisher","first-page":"e43725","DOI":"10.2196\/43725","article-title":"Electronic health record-based absolute risk prediction model for esophageal cancer in the Chinese population: model development and external validation","volume":"9","author":"Han","year":"2023","journal-title":"JMIR Public Health Surveill"},{"key":"B167","doi-asserted-by":"publisher","first-page":"1117420","DOI":"10.3389\/fonc.2023.1117420","article-title":"The innovative model based on artificial intelligence algorithms to predict recurrence risk of patients with postoperative breast cancer","volume":"13","author":"Zeng","year":"2023","journal-title":"Front Oncol"},{"key":"B168","doi-asserted-by":"publisher","first-page":"100127","DOI":"10.1016\/j.apjon.2022.100127","article-title":"Artificial intelligence empowered digital health technologies in cancer survivorship care: a scoping review","volume":"9","author":"Pan","year":"2022","journal-title":"Asia Pac J Oncol Nurs"},{"key":"B169","doi-asserted-by":"publisher","first-page":"1027808","DOI":"10.3389\/fphar.2022.1027808","article-title":"Artificial intelligence-based internet hospital pharmacy services in China: perspective based on a case study","volume":"13","author":"Bu","year":"2022","journal-title":"Front Pharmacol"},{"key":"B170","doi-asserted-by":"publisher","first-page":"711","DOI":"10.21037\/atm.2019.11.108","article-title":"Using artificial intelligence to improve medical services in China","volume":"8","author":"Li","year":"2020","journal-title":"Ann Transl Med"},{"key":"B171","article-title":"Cancer care at your doorstep with Hospido\u2019s new AI-enabled chatbot cancer dost","year":"2021"},{"key":"B172","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1038\/s41746-023-00831-w","article-title":"An artificial intelligence based app for skin cancer detection evaluated in a population based setting","volume":"6","author":"Smak Gregoor","year":"2023","journal-title":"NPJ Digit Med"},{"key":"B173","article-title":"Improved cancer care and support","year":"2023"},{"key":"B174","article-title":"CancerBase","year":"2023"},{"key":"B175","first-page":"261","article-title":"Bringing wearable devices into oncology practice: fitting smart technology in the clinic","volume":"26","author":"Menta","year":"2018","journal-title":"Discov Med"},{"key":"B176","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1002\/cai2.37","article-title":"Applications of digital medicine in oncology: prospects and challenges","volume":"1","author":"Ge","year":"2022","journal-title":"Cancer Innov"},{"key":"B177","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1038\/s41746-020-00351-x","article-title":"Harnessing consumer smartphone and wearable sensors for clinical cancer research","volume":"3","author":"Low","year":"2020","journal-title":"NPJ Digit Med"},{"key":"B178","doi-asserted-by":"publisher","first-page":"103314","DOI":"10.1016\/j.drudis.2022.06.014","article-title":"Wearable smart devices in cancer diagnosis and remote clinical trial monitoring: transforming the healthcare applications","volume":"27","author":"Beg","year":"2022","journal-title":"Drug Discov Today"},{"key":"B179","doi-asserted-by":"publisher","DOI":"10.1002\/widm.1485","article-title":"Remote patient monitoring using artificial intelligence: current state, applications, and challenges","volume":"13","author":"Shaik","year":"2023","journal-title":"Wiley Interdiscip Rev Data Min Knowl Discov"},{"key":"B180","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1038\/s41416-021-01633-1","article-title":"Artificial intelligence in oncology: current applications and future perspectives","volume":"126","author":"Luchini","year":"2022","journal-title":"Br J Cancer"},{"key":"B181","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1186\/s12957-016-1022-2","article-title":"A cost-effective handheld breast scanner for use in low-resource environments: a validation study","volume":"14","author":"Broach","year":"2016","journal-title":"World J Surg Oncol"},{"key":"B182","article-title":"Breast exam for better breast health","year":"2023"},{"key":"B183","doi-asserted-by":"publisher","first-page":"e555","DOI":"10.1016\/S2214-109X(22)00030-4","article-title":"The iBreastExam versus clinical breast examination for breast evaluation in high risk and symptomatic Nigerian women: a prospective study","volume":"10","author":"Mango","year":"2022","journal-title":"Lancet Glob Health"},{"key":"B184","doi-asserted-by":"publisher","first-page":"10442","DOI":"10.1038\/s41598-019-46540-x","article-title":"Identifying potential drug targets in hepatocellular carcinoma based on network analysis and one-class support vector machine","volume":"9","author":"Tong","year":"2019","journal-title":"Sci Rep"},{"key":"B185","doi-asserted-by":"publisher","first-page":"8515","DOI":"10.1038\/s41598-020-65584-y","article-title":"Prediction of breast cancer proteins involved in immunotherapy, metastasis, and RNA-binding using molecular descriptors and artificial neural networks","volume":"10","author":"L\u00f3pez-Cort\u00e9s","year":"2020","journal-title":"Sci Rep"},{"key":"B186","first-page":"692277","article-title":"A machine learning approach predicts essential genes and pharmacological targets in cancer. Icahn School of Medicine at Mount","author":"Gilvary","year":"2019"},{"key":"B187","doi-asserted-by":"publisher","first-page":"564","DOI":"10.1016\/j.cell.2017.06.010","article-title":"Defining a cancer dependency map","volume":"170","author":"Tsherniak","year":"2017","journal-title":"Cell"},{"key":"B188","doi-asserted-by":"publisher","first-page":"999","DOI":"10.1038\/s41419-022-05437-w","article-title":"High-confidence cancer patient stratification through multiomics investigation of DNA repair disorders","volume":"13","author":"Mkrtchyan","year":"2022","journal-title":"Cell Death Dis"},{"key":"B189","doi-asserted-by":"publisher","first-page":"5462","DOI":"10.1073\/pnas.1718338115","article-title":"Pan-cancer transcriptional signatures predictive of oncogenic mutations reveal that Fbw7 regulates cancer cell oxidative metabolism","volume":"115","author":"Davis","year":"2018","journal-title":"Proc Natl Acad Sci U S A"},{"key":"B190","doi-asserted-by":"publisher","first-page":"1443","DOI":"10.1039\/d2sc05709c","article-title":"Alphafold accelerates artificial intelligence powered drug discovery: efficient discovery of a novel CDK20 small molecule inhibitor","volume":"14","author":"Ren","year":"2023","journal-title":"Chem Sci"},{"key":"B191","doi-asserted-by":"publisher","first-page":"1038","DOI":"10.1038\/s41587-019-0224-x","article-title":"Deep learning enables rapid identification of potent DDR1 kinase inhibitors","volume":"37","author":"Zhavoronkov","year":"2019","journal-title":"Nat Biotechnol"},{"key":"B192","doi-asserted-by":"publisher","first-page":"661","DOI":"10.2174\/1568026621666210119112845","article-title":"Multi-target drug discovery via PTML modeling: applications to the design of virtual dual inhibitors of CDK4 and HER2","volume":"21","author":"Kleandrova","year":"2021","journal-title":"Curr Top Med Chem"},{"key":"B193","doi-asserted-by":"publisher","first-page":"1294","DOI":"10.1016\/j.chembiol.2016.07.023","article-title":"A data-driven approach to predicting successes and failures of clinical trials","volume":"23","author":"Gayvert","year":"2016","journal-title":"Cell Chem Biol"},{"key":"B194","doi-asserted-by":"publisher","first-page":"1686","DOI":"10.1021\/ci300124c","article-title":"A Bayesian approach to in silico blood-brain barrier penetration modeling","volume":"52","author":"Martins","year":"2012","journal-title":"J Chem Inf Model"},{"key":"B195","doi-asserted-by":"publisher","first-page":"1034","DOI":"10.1021\/ci100104j","article-title":"Estimation of ADME properties with substructure pattern recognition","volume":"50","author":"Shen","year":"2010","journal-title":"J Chem Inf Model"},{"key":"B196","doi-asserted-by":"publisher","first-page":"bbab523","DOI":"10.1093\/bib\/bbab523","article-title":"Artificial intelligence in clinical research of cancers","volume":"23","author":"Shao","year":"2022","journal-title":"Brief Bioinform"},{"key":"B197","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/cancers12123519","article-title":"A novel pipeline for drug repurposing for bladder cancer based on patients\u2019 omics signatures","volume":"12","author":"Mokou","year":"2020","journal-title":"Cancers"},{"key":"B198","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1007\/s12553-023-00738-2","article-title":"Artificial intelligence applied to clinical trials: opportunities and challenges","volume":"13","author":"Askin","year":"2023","journal-title":"Health Technol"},{"key":"B199","doi-asserted-by":"publisher","first-page":"103406","DOI":"10.1016\/j.jbi.2020.103406","article-title":"Matching patients to clinical trials using semantically enriched document representation","volume":"105","author":"Hassanzadeh","year":"2020","journal-title":"J Biomed Inform"},{"key":"B200","doi-asserted-by":"publisher","first-page":"1195","DOI":"10.1093\/jamia\/ocz064","article-title":"Optimizing clinical trials recruitment via deep learning","volume":"26","author":"Gligorijevic","year":"2019","journal-title":"J Am Med Inform Assoc"},{"key":"B201","doi-asserted-by":"publisher","first-page":"e40895","DOI":"10.7759\/cureus.40895","article-title":"Chatdoctor: a medical chat model fine-tuned on a large language model meta-AI (LLaMA) using medical domain knowledge","volume":"15","author":"Li","year":"2023","journal-title":"Cureus"},{"key":"B202","doi-asserted-by":"publisher","DOI":"10.1056\/aioa2300138","article-title":"Towards generalist biomedical AI","volume":"1","author":"Tu","year":"2024","journal-title":"NEJM AI"},{"key":"B203","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3571730","article-title":"Survey of hallucination in natural language generation","volume":"55","author":"Ji","year":"2022","journal-title":"ACM Comput Surv"},{"key":"B204","article-title":"RLAIF vs RLHF: scaling reinforcement learning from Human feedback with AI feedback. arXiv.org (2023)","author":"Lee","year":""},{"key":"B205","doi-asserted-by":"publisher","first-page":"e0301738","DOI":"10.1371\/journal.pone.0301738","article-title":"Comparison between parameter-efficient techniques and full fine-tuning: a case study on multilingual news article classification","volume":"19","author":"Razuvayevskaya","year":"2024","journal-title":"PLoS One"},{"key":"B206","article-title":"Artificial intelligence: development, risks and regulation. House of lords library, UK parliament","author":"Tobin","year":"2023"},{"key":"B207","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1016\/j.bushor.2022.03.002","article-title":"Artificial intelligence and knowledge management: a partnership between human and AI","volume":"66","author":"Jarrahi","year":"2023","journal-title":"Bus Horiz"},{"key":"B208","doi-asserted-by":"publisher","first-page":"731","DOI":"10.1007\/s44174-023-00063-2","article-title":"Drawbacks of artificial intelligence and their potential solutions in the healthcare sector","volume":"1","author":"Khan","year":"2023","journal-title":"Biomed Mater Devices"},{"key":"B209","doi-asserted-by":"publisher","first-page":"1656","DOI":"10.1080\/00140139.2023.2243404","article-title":"Ironies of artificial intelligence","volume":"66","author":"Endsley","year":"2023","journal-title":"Ergonomics"},{"key":"B210","volume-title":"Global Strategy on Digital Health 2020-2025","year":"2021"},{"key":"B211","article-title":"National digital health blueprint","year":"2019"},{"key":"B212","article-title":"Ethical guidelines for application of artificial intelligence in biomedical research and healthcare","year":"2023"},{"key":"B213","article-title":"Artificial intelligence for health","year":"2023"},{"key":"B214","doi-asserted-by":"publisher","first-page":"100191","DOI":"10.1016\/j.mcpdig.2024.100191","article-title":"Global harmonisation of AI-enabled software as a medical device regulation: addressing challenges and unifying standards","volume":"3","author":"Reddy","year":"2024","journal-title":"Mayo Clin Proc Digit Health"},{"key":"B215","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1038\/s41587-019-0055-9","article-title":"The international cancer genome consortium data portal","volume":"37","author":"Zhang","year":"2019","journal-title":"Nat Biotechnol"},{"key":"B216","doi-asserted-by":"publisher","first-page":"100029","DOI":"10.1016\/j.xgen.2021.100029","article-title":"GA4GH: international policies and standards for data sharing across genomic research and healthcare","volume":"1","author":"Rehm","year":"2021","journal-title":"Cell Genom"},{"key":"B217","doi-asserted-by":"publisher","DOI":"10.1038\/d44151-024-00019-5","article-title":"A genome atlas mapping cancers in India","author":"Dhup","year":"2024","journal-title":"Nat India"},{"key":"B218","doi-asserted-by":"publisher","first-page":"D1225","DOI":"10.1093\/nar\/gkaa923","article-title":"Indigenomes: a comprehensive resource of genetic variants from over 1000 Indian genomes","volume":"49","author":"Jain","year":"2021","journal-title":"Nucleic Acids Res"},{"key":"B219","article-title":"GenomeIndia","year":"2024"},{"key":"B220","doi-asserted-by":"publisher","first-page":"S2","DOI":"10.4103\/0973-1075.76229","article-title":"Computerized clinical database development in oncology","volume":"17","author":"Deo","year":"2011","journal-title":"Indian J Palliat Care"},{"key":"B221","doi-asserted-by":"publisher","first-page":"998222","DOI":"10.3389\/fonc.2022.998222","article-title":"Artificial intelligence assists precision medicine in cancer treatment","volume":"12","author":"Liao","year":"2023","journal-title":"Front Oncol"},{"key":"B222","doi-asserted-by":"publisher","first-page":"2392290","DOI":"10.1080\/23288604.2024.2392290","article-title":"The Ayushman Bharat digital mission of India: an assessment","volume":"10","author":"Mishra","year":"2024","journal-title":"Health Syst Reform"},{"key":"B223","article-title":"NCG guidelines manual","year":"2021"},{"key":"B224","doi-asserted-by":"publisher","first-page":"1629","DOI":"10.3390\/healthcare10091629","article-title":"European health data space - an opportunity now to grasp the future of data-driven healthcare","volume":"10","author":"Horgan","year":"2022","journal-title":"Healthcare"},{"key":"B225","doi-asserted-by":"publisher","first-page":"e22","DOI":"10.2196\/medinform.3447","article-title":"Making big data useful for health care: a summary of the inaugural mit critical data conference","volume":"2","author":"Badawi","year":"2014","journal-title":"JMIR Med Inform"},{"key":"B226","article-title":"IndiaAI mission to boost artificial intelligence","year":"2024"},{"key":"B227","article-title":"Cabinet approves over Rs 10,300 crore for IndiaAI mission, will empower AI startups and expand compute infrastructure access.","year":"2024"},{"key":"B228","article-title":"Status of the action plan for cancer screening in rural areas.","year":"2024"},{"key":"B229","article-title":"Responsible AI for social empowerment (RAISE).","year":"2020"},{"key":"B230","doi-asserted-by":"publisher","first-page":"1147210","DOI":"10.3389\/fpubh.2023.1147210","article-title":"Strengthening and promoting digital health practice: results from a global digital health partnership\u2019s survey","volume":"11","author":"Cascini","year":"2023","journal-title":"Front Public Health"},{"key":"B231","doi-asserted-by":"publisher","first-page":"105334","DOI":"10.1016\/j.compbiomed.2022.105334","article-title":"Artificial intelligence techniques for prediction of drug synergy in malignant diseases: past, present, and future","volume":"144","author":"Rani","year":"2022","journal-title":"Comput Biol Med"},{"key":"B232","doi-asserted-by":"publisher","first-page":"100005","DOI":"10.1016\/j.jrt.2020.100005","article-title":"Legal and human rights issues of AI: gaps, challenges and vulnerabilities","volume":"4","author":"Rodrigues","year":"2020","journal-title":"J Responsible Technol"},{"key":"B233","article-title":"Solving the AI accountability gap - towards data science. Medium","author":"Bartlett","year":"2021"},{"key":"B234","article-title":"Transparency for machine learning-enabled medical devices: guiding principles","year":"2024"},{"key":"B235","article-title":"Good machine learning practice for medical device development: guiding principles","year":"2021"},{"key":"B236","article-title":"Artificial intelligence and machine learning in software","year":"2024"},{"key":"B237","article-title":"The digital personal data protection act","year":"2023"},{"key":"B238","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1038\/s41591-018-0272-7","article-title":"Privacy in the age of medical big data","volume":"25","author":"Price","year":"2019","journal-title":"Nat Med"},{"key":"B239","doi-asserted-by":"publisher","first-page":"598","DOI":"10.4103\/ijmr.ijmr_1821_22","article-title":"Cancer incidence estimates for 2022 & projection for 2025: result from national cancer registry programme, India","volume":"156","author":"Sathishkumar","year":"2022","journal-title":"Indian J Med Res"},{"key":"B240","doi-asserted-by":"publisher","DOI":"10.3390\/cancers14143442","volume":"14","author":"Basurto-Hurtado","year":"2022"}],"container-title":["Frontiers in Digital Health"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fdgth.2025.1550407\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,4]],"date-time":"2025-03-04T07:12:05Z","timestamp":1741072325000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fdgth.2025.1550407\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,4]]},"references-count":240,"alternative-id":["10.3389\/fdgth.2025.1550407"],"URL":"https:\/\/doi.org\/10.3389\/fdgth.2025.1550407","relation":{},"ISSN":["2673-253X"],"issn-type":[{"value":"2673-253X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,4]]},"article-number":"1550407"}}