Artificial intelligence methods available for cancer research

Abstract

Cancer is a heterogeneous and multifaceted disease with a significant global footprint. Despite substantial technological advancements for battling cancer, early diagnosis and selection of effective treatment remains a challenge. With the convenience of large-scale datasets including multiple levels of data, new bioinformatic tools are needed to transform this wealth of information into clinically useful decision-support tools. In this field, artificial intelligence (AI) technologies with their highly diverse applications are rapidly gaining ground. Machine learning methods, such as Bayesian networks, support vector machines, decision trees, random forests, gradient boosting, and K-nearest neighbors, including neural network models like deep learning, have proven valuable in predictive, prognostic, and diagnostic studies. Researchers have recently employed large language models to tackle new dimensions of problems. However, leveraging the opportunity to utilize AI in clinical settings will require surpassing significant obstacles—a major issue is the lack of use of the available reporting guidelines obstructing the reproducibility of published studies. In this review, we discuss the applications of AI methods and explore their benefits and limitations. We summarize the available guidelines for AI in healthcare and highlight the potential role and impact of AI models on future directions in cancer research.

Keywords

machine learning / artificial neural network / deep learning / natural language processing / prediction / guideline / diagnosis

References

[1] Morrow M, Waters J, Morris E. MRI for breast cancer screening, diagnosis, and treatment. Lancet 2011; 378(9805): 1804–1811

[2] Boiselle PM. Computed tomography screening for lung cancer. JAMA 2013; 309(11): 1163–1170

[3] Gøtzsche PC, Jørgensen KJ. Screening for breast cancer with mammography. Cochrane Database Syst Rev 2013; 2013(6): CD001877

[4] Nair M, Sandhu SS, Sharma AK. Cancer molecular markers: a guide to cancer detection and management. Semin Cancer Biol 2018; 52(Pt 1): 39–55

[5] Győrffy B. Discovery and ranking of the most robust prognostic biomarkers in serous ovarian cancer. Geroscience 2023; 45(3): 1889–1898

[6] Kovács SA, Fekete JT, Győrffy B. Predictive biomarkers of immunotherapy response with pharmacological applications in solid tumors. Acta Pharmacol Sin 2023; 44(9): 1879–1889

[7] Seebacher NA, Stacy AE, Porter GM, Merlot AM. Clinical development of targeted and immune based anti-cancer therapies. J Exp Clin Cancer Res 2019; 38(1): 156

[8] Liang G, Fan W, Luo H, Zhu X. The emerging roles of artificial intelligence in cancer drug development and precision therapy. Biomed Pharmacother 2020; 128: 110255

[9] Kumar Y, Gupta S, Singla R, Hu YC. A systematic review of artificial intelligence techniques in cancer prediction and diagnosis. Arch Comput Methods Eng 2022; 29(4): 2043–2070

[10] Pavlopoulou A, Spandidos DA, Michalopoulos I. Human cancer databases (review). Oncol Rep 2015; 33(1): 3–18

[11] Rios Velazquez E, Parmar C, Liu Y, Coroller TP, Cruz G, Stringfield O, Ye Z, Makrigiorgos M, Fennessy F, Mak RH, Gillies R, Quackenbush J, Aerts HJWL. Somatic mutations drive distinct imaging phenotypes in lung cancer. Cancer Res 2017; 77(14): 3922–3930

[12] Hou Q, Bing ZT, Hu C, Li MY, Yang KH, Mo Z, Xie XW, Liao JL, Lu Y, Horie S, Lou MW. RankProd combined with genetic algorithm optimized artificial neural network establishes a diagnostic and prognostic prediction model that revealed C1QTNF3 as a biomarker for prostate cancer. EBioMedicine 2018; 32: 234–244

[13] Carter SM, Rogers W, Win KT, Frazer H, Richards B, Houssami N. The ethical, legal and social implications of using artificial intelligence systems in breast cancer care. Breast 2020; 49: 25–32

[14] Kaul V, Enslin S, Gross SA. History of artificial intelligence in medicine. Gastrointest Endosc 2020; 92(4): 807–812

[15] Fekete JT, Győrffy B. ROCplot. org: validating predictive biomarkers of chemotherapy/hormonal therapy/anti-HER2 therapy using transcriptomic data of 3, 104 breast cancer patients. Int J Cancer 2019; 145(11): 3140–3151

[16] Quinlan JR. Induction of decision trees. Mach Learn 1986; 1(1): 81–106

[17] Kingsford C, Salzberg SL. What are decision trees. Nat Biotechnol 2008; 26(9): 1011–1013

[18] Talhouk A, McConechy MK, Leung S, Yang W, Lum A, Senz J, Boyd N, Pike J, Anglesio M, Kwon JS, Karnezis AN, Huntsman DG, Gilks CB, McAlpine JN. Confirmation of ProMisE: a simple, genomics-based clinical classifier for endometrial cancer. Cancer 2017; 123(5): 802–813

[19] Selman TJ, Mann C, Zamora J, Appleyard TL, Khan K. Diagnostic accuracy of tests for lymph node status in primary cervical cancer: a systematic review and meta-analysis. CMAJ 2008; 178(7): 855–862

[20] Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RGPM, Granton P, Zegers CM, Gillies R, Boellard R, Dekker A, Aerts HJ. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer Oxf Engl 2012; 48(4): 441–446

[21] Wu Q, Wang S, Chen X, Wang Y, Dong L, Liu Z, Tian J, Wang M. Radiomics analysis of magnetic resonance imaging improves diagnostic performance of lymph node metastasis in patients with cervical cancer. Radiother Oncol 2019; 138: 141–148

[22] Gevaert O, Echegaray S, Khuong A, Hoang CD, Shrager JB, Jensen KC, Berry GJ, Guo HH, Lau C, Plevritis SK, Rubin DL, Napel S, Leung AN. Predictive radiogenomics modeling of EGFR mutation status in lung cancer. Sci Rep 2017; 7(1): 41674

[23] Hu LS, Ning S, Eschbacher JM, Baxter LC, Gaw N, Ranjbar S, Plasencia J, Dueck AC, Peng S, Smith KA, Nakaji P, Karis JP, Quarles CC, Wu T, Loftus JC, Jenkins RB, Sicotte H, Kollmeyer TM, O’Neill BP, Elmquist W, Hoxworth JM, Frakes D, Sarkaria J, Swanson KR, Tran NL, Li J, Mitchell JR. Radiogenomics to characterize regional genetic heterogeneity in glioblastoma. Neuro-oncol 2017; 19(1): 128–137

[24] Tahmassebi A, Wengert GJ, Helbich TH, Bago-Horvath Z, Alaei S, Bartsch R, Dubsky P, Baltzer P, Clauser P, Kapetas P, Morris EA, Meyer-Baese A, Pinker K. Impact of machine learning with multiparametric magnetic resonance imaging of the breast for early prediction of response to neoadjuvant chemotherapy and survival outcomes in breast cancer patients. Invest Radiol 2019; 54(2): 110–117

[25] Wang Y, Wang D, Geng N, Wang Y, Yin Y, Jin Y. Stacking-based ensemble learning of decision trees for interpretable prostate cancer detection. Appl Soft Comput 2019; 77: 188–204

[26] Ghiasi MM, Zendehboudi S. Application of decision tree-based ensemble learning in the classification of breast cancer. Comput Biol Med 2021; 128: 104089

[27] De Felice F, Crocetti D, Parisi M, Maiuri V, Moscarelli E, Caiazzo R, Bulzonetti N, Musio D, Tombolini V. Decision tree algorithm in locally advanced rectal cancer: an example of over-interpretation and misuse of a machine learning approach. J Cancer Res Clin Oncol 2020; 146(3): 761–765

[28] Breiman L. Random forests. Mach Learn 2001; 45(1): 5–32

[29] Tibor Fekete J, Győrffy B. A unified platform enabling biomarker ranking and validation for 1562 drugs using transcriptomic data of 1250 cancer cell lines. Comput Struct Biotechnol J 2022; 20: 2885–2894

[30] Cohen JD, Li L, Wang Y, Thoburn C, Afsari B, Danilova L, Douville C, Javed AA, Wong F, Mattox A, Hruban RH, Wolfgang CL, Goggins MG, Dal Molin M, Wang TL, Roden R, Klein AP, Ptak J, Dobbyn L, Schaefer J, Silliman N, Popoli M, Vogelstein JT, Browne JD, Schoen RE, Brand RE, Tie J, Gibbs P, Wong HL, Mansfield AS, Jen J, Hanash SM, Falconi M, Allen PJ, Zhou S, Bettegowda C, Diaz LA Jr, Tomasetti C, Kinzler KW, Vogelstein B, Lennon AM, Papadopoulos N. Detection and localization of surgically resectable cancers with a multi-analyte blood test. Science 2018; 359(6378): 926–930

[31] Toth R, Schiffmann H, Hube-Magg C, Büscheck F, Höflmayer D, Weidemann S, Lebok P, Fraune C, Minner S, Schlomm T, Sauter G, Plass C, Assenov Y, Simon R, Meiners J, Gerhäuser C. Random forest-based modelling to detect biomarkers for prostate cancer progression. Clin Epigenetics 2019; 11(1): 148

[32] Kawakami E, Tabata J, Yanaihara N, Ishikawa T, Koseki K, Iida Y, Saito M, Komazaki H, Shapiro JS, Goto C, Akiyama Y, Saito R, Saito M, Takano H, Yamada K, Okamoto A. Application of artificial intelligence for preoperative diagnostic and prognostic prediction in epithelial ovarian cancer based on blood biomarkers. Clin Cancer Res 2019; 25(10): 3006–3015

[33] Zhang Z, Huang L, Li J, Wang P. Bioinformatics analysis reveals immune prognostic markers for overall survival of colorectal cancer patients: a novel machine learning survival predictive system. BMC Bioinformatics 2022; 23(1): 124

[34] Lin J, Yin M, Liu L, Gao J, Yu C, Liu X, Xu C, Zhu J. The development of a prediction model based on random survival forest for the postoperative prognosis of pancreatic cancer: a SEER-based study. Cancers (Basel) 2022; 14(19): 4667

[35] Zhang H, Chi M, Su D, Xiong Y, Wei H, Yu Y, Zuo Y, Yang L. A random forest-based metabolic risk model to assess the prognosis and metabolism-related drug targets in ovarian cancer. Comput Biol Med 2023; 153: 106432

[36] Lee JY, Lee KS, Seo BK, Cho KR, Woo OH, Song SE, Kim EK, Lee HY, Kim JS, Cha J. Radiomic machine learning for predicting prognostic biomarkers and molecular subtypes of breast cancer using tumor heterogeneity and angiogenesis properties on MRI. Eur Radiol 2022; 32(1): 650–660

[37] Horvat N, Veeraraghavan H, Khan M, Blazic I, Zheng J, Capanu M, Sala E, Garcia-Aguilar J, Gollub MJ, Petkovska I. MR imaging of rectal cancer: radiomics analysis to assess treatment response after neoadjuvant therapy. Radiology 2018; 287(3): 833–843

[38] Deist TM, Dankers FJWM, Valdes G, Wijsman R, Hsu IC, Oberije C, Lustberg T, van Soest J, Hoebers F, Jochems A, El Naqa I, Wee L, Morin O, Raleigh DR, Bots W, Kaanders JH, Belderbos J, Kwint M, Solberg T, Monshouwer R, Bussink J, Dekker A, Lambin P. Machine learning algorithms for outcome prediction in (chemo)radiotherapy: an empirical comparison of classifiers. Med Phys 2018; 45(7): 3449–3459

[39] Paul D, Su R, Romain M, Sébastien V, Pierre V, Isabelle G. Feature selection for outcome prediction in oesophageal cancer using genetic algorithm and random forest classifier. Comput Med Imaging Graph 2017; 60: 42–49

[40] Wang S, Wang Y, Wang D, Yin Y, Wang Y, Jin Y. An improved random forest-based rule extraction method for breast cancer diagnosis. Appl Soft Comput 2020; 86: 105941

[41] Singh H, Singh S, Singla D, Agarwal SM, Raghava GPS. QSAR based model for discriminating EGFR inhibitors and non-inhibitors using Random forest. Biol Direct 2015; 10: 10

[42] Li X, Xu Y, Cui H, Huang T, Wang D, Lian B, Li W, Qin G, Chen L, Xie L. Prediction of synergistic anti-cancer drug combinations based on drug target network and drug induced gene expression profiles. Artif Intell Med 2017; 83: 35–43

[43] Sidorov P, Naulaerts S, Ariey-Bonnet J, Pasquier E, Ballester PJ. Predicting synergism of cancer drug combinations using NCI-ALMANAC data. Front Chem 2019; 7: 509

[44] Yang C, Huang X, Li Y, Chen J, Lv Y, Dai S. Prognosis and personalized treatment prediction in TP53-mutant hepatocellular carcinoma: an in silico strategy towards precision oncology. Brief Bioinform 2021; 22(3): bbaa164

[45] Hu C, Steingrimsson JA. Personalized risk prediction in clinical oncology research: applications and practical issues using survival trees and random forests. J Biopharm Stat 2018; 28(2): 333–349

[46] Natekin A, Knoll A. Gradient boosting machines, a tutorial. Front Neurorobot 2013; 7: 21

[47] Friedman JH. Greedy function approximation: a gradient boosting machine. Ann Stat 2001; 29(5): 1189–1232

[48] ChenTGuestrin C. XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Pages 785–794). 2016; arXiv: 1603.02754

[49] Mallett S, Royston P, Waters R, Dutton S, Altman DG. Reporting performance of prognostic models in cancer: a review. BMC Med 2010; 8(1): 21

[50] Moncada-Torres A, van Maaren MC, Hendriks MP, Siesling S, Geleijnse G. Explainable machine learning can outperform Cox regression predictions and provide insights in breast cancer survival. Sci Rep 2021; 11(1): 6968

[51] Nazari M, Shiri I, Zaidi H. Radiomics-based machine learning model to predict risk of death within 5-years in clear cell renal cell carcinoma patients. Comput Biol Med 2021; 129: 104135

[52] Arrieta O, Cardona AF, Martín C, Más-López L, Corrales-Rodríguez L, Bramuglia G, Castillo-Fernandez O, Meyerson M, Amieva-Rivera E, Campos-Parra AD, Carranza H, Gómez de la Torre JC, Powazniak Y, Aldaco-Sarvide F, Vargas C, Trigo M, Magallanes-Maciel M, Otero J, Sánchez-Reyes R, Cuello M. Updated frequency of EGFR and KRAS mutations in non-small-cell lung cancer in Latin America: the Latin-American Consortium for the Investigation of Lung Cancer (CLICaP). J Thorac Oncol 2015; 10(5): 838–843

[53] Le NQK, Kha QH, Nguyen VH, Chen YC, Cheng SJ, Chen CY. Machine learning-based radiomics signatures for EGFR and KRAS mutations prediction in non-small-cell lung cancer. Int J Mol Sci 2021; 22(17): 9254

[54] Li Q, Yang H, Wang P, Liu X, Lv K, Ye M. XGBoost-based and tumor-immune characterized gene signature for the prediction of metastatic status in breast cancer. J Transl Med 2022; 20(1): 177

[55] Liu X, Yuan P, Li R, Zhang D, An J, Ju J, Liu C, Ren F, Hou R, Li Y, Yang J. Predicting breast cancer recurrence and metastasis risk by integrating color and texture features of histopathological images and machine learning technologies. Comput Biol Med 2022; 146: 105569

[56] Bomane A, Gonçalves A, Ballester PJ. Paclitaxel response can be predicted with interpretable multi-variate classifiers exploiting DNA-methylation and miRNA data. Front Genet 2019; 10: 1041

[57] Polano M, Chierici M, Dal Bo M, Gentilini D, Di Cintio F, Baboci L, Gibbs DL, Furlanello C, Toffoli G. A pan-cancer approach to predict responsiveness to immune checkpoint inhibitors by machine learning. Cancers (Basel) 2019; 11(10): 1562

[58] Ji GW, Jiao CY, Xu ZG, Li XC, Wang K, Wang XH. Development and validation of a gradient boosting machine to predict prognosis after liver resection for intrahepatic cholangiocarcinoma. BMC Cancer 2022; 22(1): 258

[59] Pfob A, Mehrara BJ, Nelson JA, Wilkins EG, Pusic AL, Sidey-Gibbons C. Towards patient-centered decision-making in breast cancer surgery: machine learning to predict individual patient-reported outcomes at 1-year follow-up. Ann Surg 2023; 277(1): e144–e152

[60] Ma B, Meng F, Yan G, Yan H, Chai B, Song F. Diagnostic classification of cancers using extreme gradient boosting algorithm and multi-omics data. Comput Biol Med 2020; 121: 103761

[61] Cortes C, Vapnik V. Support-vector networks. Mach Learn 1995; 20(3): 273–297

[62] Noble WS. What is a support vector machine. Nat Biotechnol 2006; 24(12): 1565–1567

[63] Furey TS, Cristianini N, Duffy N, Bednarski DW, Schummer M, Haussler D. Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 2000; 16(10): 906–914

[64] Dorman SN, Baranova K, Knoll JHM, Urquhart BL, Mariani G, Carcangiu ML, Rogan PK. Genomic signatures for paclitaxel and gemcitabine resistance in breast cancer derived by machine learning. Mol Oncol 2016; 10(1): 85–100

[65] Zhang B, He X, Ouyang F, Gu D, Dong Y, Zhang L, Mo X, Huang W, Tian J, Zhang S. Radiomic machine-learning classifiers for prognostic biomarkers of advanced nasopharyngeal carcinoma. Cancer Lett 2017; 403: 21–27

[66] Liu Z, Zhang XY, Shi YJ, Wang L, Zhu HT, Tang Z, Wang S, Li XT, Tian J, Sun YS. Radiomics analysis for evaluation of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Clin Cancer Res 2017; 23(23): 7253–7262

[67] Yang L, Dong D, Fang M, Zhu Y, Zang Y, Liu Z, Zhang H, Ying J, Zhao X, Tian J. Can CT-based radiomics signature predict KRAS/NRAS/BRAF mutations in colorectal cancer. Eur Radiol 2018; 28(5): 2058–2067

[68] Abeel T, Helleputte T, Van de Peer Y, Dupont P, Saeys Y. Robust biomarker identification for cancer diagnosis with ensemble feature selection methods. Bioinformatics 2010; 26(3): 392–398

[69] Li Y, Zhao J, Yu S, Wang Z, He X, Su Y, Guo T, Sheng H, Chen J, Zheng Q, Li Y, Guo W, Cai X, Shi G, Wu J, Wang L, Wang P, He X, Huang S. Extracellular vesicles long RNA sequencing reveals abundant mRNA, circRNA, and lncRNA in human blood as potential biomarkers for cancer diagnosis. Clin Chem 2019; 65(6): 798–808

[70] Qiu J, Peng B, Tang Y, Qian Y, Guo P, Li M, Luo J, Chen B, Tang H, Lu C, Cai M, Ke Z, He W, Zheng Y, Xie D, Li B, Yuan Y. CpG methylation signature predicts recurrence in early-stage hepatocellular carcinoma: results from a multicenter study. J Clin Oncol 2017; 35(7): 734–742

[71] Jiang Y, Xie J, Han Z, Liu W, Xi S, Huang L, Huang W, Lin T, Zhao L, Hu Y, Yu J, Zhang Q, Li T, Cai S, Li G. Immunomarker support vector machine classifier for prediction of gastric cancer survival and adjuvant chemotherapeutic benefit. Clin Cancer Res 2018; 24(22): 5574–5584

[72] Cheong JH, Wang SC, Park S, Porembka MR, Christie AL, Kim H, Kim HS, Zhu H, Hyung WJ, Noh SH, Hu B, Hong C, Karalis JD, Kim IH, Lee SH, Hwang TH. Development and validation of a prognostic and predictive 32-gene signature for gastric cancer. Nat Commun 2022; 13(1): 774

[73] Delahunt B, Eble JN, Egevad L, Samaratunga H. Grading of renal cell carcinoma. Histopathology 2019; 74(1): 4–17

[74] FIGO Committee on Gynecologic Oncology. FIGO staging for carcinoma of the vulva, cervix, and corpus uteri. Int J Gynaecol Obstet 2014; 125(2): 97–98

[75] Bektas CT, Kocak B, Yardimci AH, Turkcanoglu MH, Yucetas U, Koca SB, Erdim C, Kilickesmez O. Clear cell renal cell carcinoma: machine learning-based quantitative computed tomography texture analysis for prediction of Fuhrman nuclear grade. Eur Radiol 2019; 29(3): 1153–1163

[76] Xie L, Chu R, Wang K, Zhang X, Li J, Zhao Z, Yao S, Wang Z, Dong T, Yang X, Su X, Qiao X, Song K, Kong B. Prognostic assessment of cervical cancer patients by clinical staging and surgical-pathological factor: a support vector machine-based approach. Front Oncol 2020; 10: 1353

[77] Xu G, Zhang M, Zhu H, Xu J. A 15-gene signature for prediction of colon cancer recurrence and prognosis based on SVM. Gene 2017; 604: 33–40

[78] Jiang D, Lei T, Wang Z, Shen C, Cao D, Hou T. ADMET evaluation in drug discovery. 20. Prediction of breast cancer resistance protein inhibition through machine learning. J Cheminform 2020; 12(1): 16

[79] Huang X, Zhang L, Wang B, Li F, Zhang Z. Feature clustering based support vector machine recursive feature elimination for gene selection. Appl Intell 2018; 48(3): 594–607

[80] Wang H, Zheng B, Yoon SW, Ko HS. A support vector machine-based ensemble algorithm for breast cancer diagnosis. Eur J Oper Res 2018; 267(2): 687–699

[81] Cooper GF, Herskovits E. A Bayesian method for the induction of probabilistic networks from data. Mach Learn 1992; 9(4): 309–347

[82] Friedman N, Geiger D, Goldszmidt M. Bayesian network classifiers. Mach Learn 1997; 29(2/3): 131–163

[83] Johnson M, Albizri A, Simsek S. Artificial intelligence in healthcare operations to enhance treatment outcomes: a framework to predict lung cancer prognosis. Ann Oper Res 2022; 308(1-2): 275–305

[84] Li R, Zhang C, Du K, Dan H, Ding R, Cai Z, Duan L, Xie Z, Zheng G, Wu H, Ren G, Dou X, Feng F, Zheng J. Analysis of prognostic factors of rectal cancer and construction of a prognostic prediction model based on Bayesian network. Front Public Health 2022; 10: 842970

[85] Wright GW, Huang DW, Phelan JD, Coulibaly ZA, Roulland S, Young RM, Wang JQ, Schmitz R, Morin RD, Tang J, Jiang A, Bagaev A, Plotnikova O, Kotlov N, Johnson CA, Wilson WH, Scott DW, Staudt LM. A probabilistic classification tool for genetic subtypes of diffuse large B cell lymphoma with therapeutic implications. Cancer Cell 2020; 37(4): 551–568.e14

[86] Jochems A, Deist TM, van Soest J, Eble M, Bulens P, Coucke P, Dries W, Lambin P, Dekker A. Distributed learning: developing a predictive model based on data from multiple hospitals without data leaving the hospital—a real life proof of concept. Radiother Oncol 2016; 121(3): 459–467

[87] Braman NM, Etesami M, Prasanna P, Dubchuk C, Gilmore H, Tiwari P, Plecha D, Madabhushi A. Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI. Breast Cancer Res 2017; 19(1): 57

[88] Yang L, Fu B, Li Y, Liu Y, Huang W, Feng S, Xiao L, Sun L, Deng L, Zheng X, Ye F, Bu H. Prediction model of the response to neoadjuvant chemotherapy in breast cancers by a Naive Bayes algorithm. Comput Methods Programs Biomed 2020; 192: 105458

[89] Rouhi R, Jafari M, Kasaei S, Keshavarzian P. Benign and malignant breast tumors classification based on region growing and CNN segmentation. Expert Syst Appl 2015; 42(3): 990–1002

[90] Russo DP, Zorn KM, Clark AM, Zhu H, Ekins S. Comparing multiple machine learning algorithms and metrics for estrogen receptor binding prediction. Mol Pharm 2018; 15(10): 4361–4370

[91] Karabatak M. A new classifier for breast cancer detection based on Naïve Bayesian. Measurement 2015; 72: 32–36

[92] Abdar M, Zomorodi-Moghadam M, Zhou X, Gururajan R, Tao X, Barua PD, Gururajan R. A new nested ensemble technique for automated diagnosis of breast cancer. Pattern Recognit Lett 2020; 132: 123–131

[93] Farid DM, Zhang L, Rahman CM, Hossain MA, Strachan R. Hybrid decision tree and naïve Bayes classifiers for multi-class classification tasks. Expert Syst Appl 2014; 41(4): 1937–1946

[94] Chen S, Webb GI, Liu L, Ma X. A novel selective naïve Bayes algorithm. Knowl Base Syst 2020; 192: 105361

[95] Cover T, Hart P. Nearest neighbor pattern classification. IEEE Trans Inf Theory 1967; 13(1): 21–27

[96] Peterson LE. K-nearest neighbor. Scholarpedia J 2009; 4(2): 1883

[97] Dhahbi S, Barhoumi W, Zagrouba E. Breast cancer diagnosis in digitized mammograms using curvelet moments. Comput Biol Med 2015; 64: 79–90

[98] Huang Q, Huang Y, Luo Y, Yuan F, Li X. Segmentation of breast ultrasound image with semantic classification of superpixels. Med Image Anal 2020; 61: 101657

[99] Waugh SA, Purdie CA, Jordan LB, Vinnicombe S, Lerski RA, Martin P, Thompson AM. Magnetic resonance imaging texture analysis classification of primary breast cancer. Eur Radiol 2016; 26(2): 322–330

[100] Leithner D, Horvat JV, Marino MA, Bernard-Davila B, Jochelson MS, Ochoa-Albiztegui RE, Martinez DF, Morris EA, Thakur S, Pinker K. Radiomic signatures with contrast-enhanced magnetic resonance imaging for the assessment of breast cancer receptor status and molecular subtypes: initial results. Breast Cancer Res 2019; 21(1): 106

[101] García-Laencina PJ, Abreu PH, Abreu MH, Afonoso N. Missing data imputation on the 5-year survival prediction of breast cancer patients with unknown discrete values. Comput Biol Med 2015; 59: 125–133

[102] Wu W, Parmar C, Grossmann P, Quackenbush J, Lambin P, Bussink J, Mak R, Aerts HJ. Exploratory study to identify radiomics classifiers for lung cancer histology. Front Oncol 2016; 6: 71

[103] Tandel GS, Balestrieri A, Jujaray T, Khanna NN, Saba L, Suri JS. Multiclass magnetic resonance imaging brain tumor classification using artificial intelligence paradigm. Comput Biol Med 2020; 122: 103804

[104] Wang A, An N, Chen G, Li L, Alterovitz G. Accelerating wrapper-based feature selection with K-nearest-neighbor. Knowl Base Syst 2015; 83: 81–91

[105] Saeys Y, Inza I, Larrañaga P. A review of feature selection techniques in bioinformatics. Bioinformatics 2007; 23(19): 2507–2517

[106] Kar S, Das Sharma K, Maitra M. Gene selection from microarray gene expression data for classification of cancer subgroups employing PSO and adaptive K-nearest neighborhood technique. Expert Syst Appl 2015; 42(1): 612–627

[107] Zhang S, Li X, Zong M, Zhu X, Wang R. Efficient kNN classification with different numbers of nearest neighbors. IEEE Trans Neural Netw Learn Syst 2018; 29(5): 1774–1785

[108] Uddin S, Haque I, Lu H, Moni MA, Gide E. Comparative performance analysis of K-nearest neighbour (KNN) algorithm and its different variants for disease prediction. Sci Rep 2022; 12(1): 6256

[109] Krogh A. What are artificial neural networks. Nat Biotechnol 2008; 26(2): 195–197

[110] Khan J, Wei JS, Ringnér M, Saal LH, Ladanyi M, Westermann F, Berthold F, Schwab M, Antonescu CR, Peterson C, Meltzer PS. Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nat Med 2001; 7(6): 673–679

[111] Abbass HA. An evolutionary artificial neural networks approach for breast cancer diagnosis. Artif Intell Med 2002; 25(3): 265–281

[112] Wei JS, Greer BT, Westermann F, Steinberg SM, Son CG, Chen QR, Whiteford CC, Bilke S, Krasnoselsky AL, Cenacchi N, Catchpoole D, Berthold F, Schwab M, Khan J. Prediction of clinical outcome using gene expression profiling and artificial neural networks for patients with neuroblastoma. Cancer Res 2004; 64(19): 6883–6891

[113] Delen D, Walker G, Kadam A. Predicting breast cancer survivability: a comparison of three data mining methods. Artif Intell Med 2005; 34(2): 113–127

[114] Dheeba J, Albert Singh N, Tamil Selvi S. Computer-aided detection of breast cancer on mammograms: a swarm intelligence optimized wavelet neural network approach. J Biomed Inform 2014; 49: 45–52

[115] Bhardwaj A, Tiwari A. Breast cancer diagnosis using Genetically Optimized Neural Network model. Expert Syst Appl 2015; 42(10): 4611–4620

[116] Alshayeji MH, Ellethy H, Abed S, Gupta R. Computer-aided detection of breast cancer on the Wisconsin dataset: an artificial neural networks approach. Biomed Signal Process Control 2022; 71: 103141

[117] Almeida PP, Cardoso CP, de Freitas LM. PDAC-ANN: an artificial neural network to predict pancreatic ductal adenocarcinoma based on gene expression. BMC Cancer 2020; 20(1): 82

[118] LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015; 521(7553): 436–444

[119] LeCunYBoser BDenkerJHendersonDHowardR HubbardWJackel L. Handwritten digit recognition with a back-propagation network. NIPS'89: Proceedings of the 2nd International Conference on Neural Information Processing Systems. 1989; 396–404

[120] Yamashita R, Nishio M, Do RKG, Togashi K. Convolutional neural networks: an overview and application in radiology. Insights Imaging 2018; 9(4): 611–629

[121] Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017; 542(7639): 115–118

[122] Bychkov D, Linder N, Turkki R, Nordling S, Kovanen PE, Verrill C, Walliander M, Lundin M, Haglund C, Lundin J. Deep learning based tissue analysis predicts outcome in colorectal cancer. Sci Rep 2018; 8(1): 3395

[123] Kather JN, Krisam J, Charoentong P, Luedde T, Herpel E, Weis CA, Gaiser T, Marx A, Valous NA, Ferber D, Jansen L, Reyes-Aldasoro CC, Zörnig I, Jäger D, Brenner H, Chang-Claude J, Hoffmeister M, Halama N. Predicting survival from colorectal cancer histology slides using deep learning: a retrospective multicenter study. PLoS Med 2019; 16(1): e1002730

[124] Skrede OJ, De Raedt S, Kleppe A, Hveem TS, Liestøl K, Maddison J, Askautrud HA, Pradhan M, Nesheim JA, Albregtsen F, Farstad IN, Domingo E, Church DN, Nesbakken A, Shepherd NA, Tomlinson I, Kerr R, Novelli M, Kerr DJ, Danielsen HE. Deep learning for prediction of colorectal cancer outcome: a discovery and validation study. Lancet 2020; 395(10221): 350–360

[125] Sirinukunwattana K, Domingo E, Richman SD, Redmond KL, Blake A, Verrill C, Leedham SJ, Chatzipli A, Hardy C, Whalley CM, Wu CH, Beggs AD, McDermott U, Dunne PD, Meade A, Walker SM, Murray GI, Samuel L, Seymour M, Tomlinson I, Quirke P, Maughan T, Rittscher J, Koelzer VH; S:CORT consortium. Image-based consensus molecular subtype (imCMS) classification of colorectal cancer using deep learning. Gut 2021; 70(3): 544–554

[126] Kather JN, Heij LR, Grabsch HI, Loeffler C, Echle A, Muti HS, Krause J, Niehues JM, Sommer KAJ, Bankhead P, Kooreman LFS, Schulte JJ, Cipriani NA, Buelow RD, Boor P, Ortiz-Brüchle NN, Hanby AM, Speirs V, Kochanny S, Patnaik A, Srisuwananukorn A, Brenner H, Hoffmeister M, van den Brandt PA, Jäger D, Trautwein C, Pearson AT, Luedde T. Pan-cancer image-based detection of clinically actionable genetic alterations. Nat Cancer 2020; 1(8): 789–799

[127] Yang J, Ju J, Guo L, Ji B, Shi S, Yang Z, Gao S, Yuan X, Tian G, Liang Y, Yuan P. Prediction of HER2-positive breast cancer recurrence and metastasis risk from histopathological images and clinical information via multimodal deep learning. Comput Struct Biotechnol J 2022; 20: 333–342

[128] Xu Y, Hosny A, Zeleznik R, Parmar C, Coroller T, Franco I, Mak RH, Aerts HJWL. Deep learning predicts lung cancer treatment response from serial medical imaging. Clin Cancer Res 2019; 25(11): 3266–3275

[129] Freer TW, Ulissey MJ. Screening mammography with computer-aided detection: prospective study of 12,860 patients in a community breast center. Radiology 2001; 220(3): 781–786

[130] Kooi T, Litjens G, van Ginneken B, Gubern-Mérida A, Sánchez CI, Mann R, den Heeten A, Karssemeijer N. Large scale deep learning for computer aided detection of mammographic lesions. Med Image Anal 2017; 35: 303–312

[131] Ren S, He K, Girshick R, Sun J. Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 2017; 39(6): 1137–1149

[132] Moreira IC, Amaral I, Domingues I, Cardoso A, Cardoso MJ, Cardoso JS. INbreast: toward a full-field digital mammographic database. Acad Radiol 2012; 19(2): 236–248

[133] Shen L, Margolies LR, Rothstein JH, Fluder E, McBride R, Sieh W. Deep learning to improve breast cancer detection on screening mammography. Sci Rep 2019; 9(1): 12495

[134] Hekler A, Utikal JS, Enk AH, Berking C, Klode J, Schadendorf D, Jansen P, Franklin C, Holland-Letz T, Krahl D, von Kalle C, Fröhling S, Brinker TJ. Pathologist-level classification of histopathological melanoma images with deep neural networks. Eur J Cancer 2019; 115: 79–83

[135] Ardila D, Kiraly AP, Bharadwaj S, Choi B, Reicher JJ, Peng L, Tse D, Etemadi M, Ye W, Corrado G, Naidich DP, Shetty S. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat Med 2019; 25(6): 954–961

[136] McKinney SM, Sieniek M, Godbole V, Godwin J, Antropova N, Ashrafian H, Back T, Chesus M, Corrado GS, Darzi A, Etemadi M, Garcia-Vicente F, Gilbert FJ, Halling-Brown M, Hassabis D, Jansen S, Karthikesalingam A, Kelly CJ, King D, Ledsam JR, Melnick D, Mostofi H, Peng L, Reicher JJ, Romera-Paredes B, Sidebottom R, Suleyman M, Tse D, Young KC, De Fauw J, Shetty S. International evaluation of an AI system for breast cancer screening. Nature 2020; 577(7788): 89–94

[137] Schaffter T, Buist DSM, Lee CI, Nikulin Y, Ribli D, Guan Y, Lotter W, Jie Z, Du H, Wang S, Feng J, Feng M, Kim HE, Albiol F, Albiol A, Morrell S, Wojna Z, Ahsen ME, Asif U, Jimeno Yepes A, Yohanandan S, Rabinovici-Cohen S, Yi D, Hoff B, Yu T, Chaibub Neto E, Rubin DL, Lindholm P, Margolies LR, McBride RB, Rothstein JH, Sieh W, Ben-Ari R, Harrer S, Trister A, Friend S, Norman T, Sahiner B, Strand F, Guinney J, Stolovitzky G;, the DM DREAM Consortium; Mackey L, Cahoon J, Shen L, Sohn JH, Trivedi H, Shen Y, Buturovic L, Pereira JC, Cardoso JS, Castro E, Kalleberg KT, Pelka O, Nedjar I, Geras KJ, Nensa F, Goan E, Koitka S, Caballero L, Cox DD, Krishnaswamy P, Pandey G, Friedrich CM, Perrin D, Fookes C, Shi B, Cardoso Negrie G, Kawczynski M, Cho K, Khoo CS, Lo JY, Sorensen AG, Jung H. Evaluation of combined artificial intelligence and radiologist assessment to interpret screening mammograms. JAMA Netw Open 2020; 3(3): e200265

[138] Wu N, Phang J, Park J, Shen Y, Huang Z, Zorin M, Jastrzębski S, Févry T, Katsnelson J, Kim E, Wolfson S, Parikh U, Gaddam S, Lin LLY, Ho K, Weinstein JD, Reig B, Gao Y, Toth H, Pysarenko K, Lewin A, Lee J, Airola K, Mema E, Chung S, Hwang E, Samreen N, Kim SG, Heacock L, Moy L, Cho K, Geras KJ. Deep neural networks improve radiologists’ performance in breast cancer screening. IEEE Trans Med Imaging 2020; 39(4): 1184–1194

[139] Tabibu S, Vinod PK, Jawahar CV. Pan-renal cell carcinoma classification and survival prediction from histopathology images using deep learning. Sci Rep 2019; 9(1): 10509

[140] Jin X, Zhou YF, Ma D, Zhao S, Lin CJ, Xiao Y, Fu T, Liu CL, Chen YY, Xiao WX, Liu YQ, Chen QW, Yu Y, Shi LM, Shi JX, Huang W, Robertson JFR, Jiang YZ, Shao ZM. Molecular classification of hormone receptor-positive HER2-negative breast cancer. Nat Genet 2023; 55(10): 1696–1708

[141] Munquad S, Das AB. DeepAutoGlioma: a deep learning autoencoder-based multi-omics data integration and classification tools for glioma subtyping. BioData Min 2023; 16(1): 32

[142] Chen M, Zhang B, Topatana W, Cao J, Zhu H, Juengpanich S, Mao Q, Yu H, Cai X. Classification and mutation prediction based on histopathology H&E images in liver cancer using deep learning. NPJ Precis Oncol 2020; 4(1): 14

[143] Coudray N, Ocampo PS, Sakellaropoulos T, Narula N, Snuderl M, Fenyö D, Moreira AL, Razavian N, Tsirigos A. Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nat Med 2018; 24(10): 1559–1567

[144] Sharifi-Noghabi H, Zolotareva O, Collins CC, Ester M. MOLI: multi-omics late integration with deep neural networks for drug response prediction. Bioinformatics 2019; 35(14): i501–i509

[145] Sakellaropoulos T, Vougas K, Narang S, Koinis F, Kotsinas A, Polyzos A, Moss TJ, Piha-Paul S, Zhou H, Kardala E, Damianidou E, Alexopoulos LG, Aifantis I, Townsend PA, Panayiotidis MI, Sfikakis P, Bartek J, Fitzgerald RC, Thanos D, Mills Shaw KR, Petty R, Tsirigos A, Gorgoulis VG. A deep learning framework for predicting response to therapy in cancer. Cell Rep 2019; 29(11): 3367–3373.e4

[146] Kuenzi BM, Park J, Fong SH, Sanchez KS, Lee J, Kreisberg JF, Ma J, Ideker T. Predicting drug response and synergy using a deep learning model of human cancer cells. Cancer Cell 2020; 38(5): 672–684.e6

[147] Su R, Liu X, Wei L, Zou Q. Deep-Resp-Forest: a deep forest model to predict anti-cancer drug response. Methods 2019; 166: 91–102

[148] Preuer K, Lewis RPI, Hochreiter S, Bender A, Bulusu KC, Klambauer G. DeepSynergy: predicting anti-cancer drug synergy with deep learning. Bioinformatics 2018; 34(9): 1538–1546

[149] Rajkomar A, Oren E, Chen K, Dai AM, Hajaj N, Hardt M, Liu PJ, Liu X, Marcus J, Sun M, Sundberg P, Yee H, Zhang K, Zhang Y, Flores G, Duggan GE, Irvine J, Le Q, Litsch K, Mossin A, Tansuwan J, Wang D, Wexler J, Wilson J, Ludwig D, Volchenboum SL, Chou K, Pearson M, Madabushi S, Shah NH, Butte AJ, Howell MD, Cui C, Corrado GS, Dean J. Scalable and accurate deep learning with electronic health records. NPJ Digit Med 2018; 1(1): 18

[150] Yala A, Lehman C, Schuster T, Portnoi T, Barzilay R. A deep learning mammography-based model for improved breast cancer risk prediction. Radiology 2019; 292(1): 60–66

[151] Rasmy L, Xiang Y, Xie Z, Tao C, Zhi D. Med-BERT: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction. NPJ Digit Med 2021; 4(1): 86

[152] Miotto R, Li L, Kidd BA, Dudley JT. Deep Patient: an unsupervised representation to predict the future of patients from the electronic health records. Sci Rep 2016; 6(1): 26094

[153] Hosny A, Parmar C, Coroller TP, Grossmann P, Zeleznik R, Kumar A, Bussink J, Gillies RJ, Mak RH, Aerts HJWL. Deep learning for lung cancer prognostication: a retrospective multi-cohort radiomics study. PLoS Med 2018; 15(11): e1002711

[154] Hirschberg J, Manning CD. Advances in natural language processing. Science 2015; 349(6245): 261–266

[155] Yim WW, Yetisgen M, Harris WP, Kwan SW. Natural language processing in oncology: a review. JAMA Oncol 2016; 2(6): 797–804

[156] Castro SM, Tseytlin E, Medvedeva O, Mitchell K, Visweswaran S, Bekhuis T, Jacobson RS. Automated annotation and classification of BI-RADS assessment from radiology reports. J Biomed Inform 2017; 69: 177–187

[157] Patel TA, Puppala M, Ogunti RO, Ensor JE, He T, Shewale JB, Ankerst DP, Kaklamani VG, Rodriguez AA, Wong ST, Chang JC. Correlating mammographic and pathologic findings in clinical decision support using natural language processing and data mining methods. Cancer 2017; 123(1): 114–121

[158] Carrell DS, Halgrim S, Tran DT, Buist DSM, Chubak J, Chapman WW, Savova G. Using natural language processing to improve efficiency of manual chart abstraction in research: the case of breast cancer recurrence. Am J Epidemiol 2014; 179(6): 749–758

[159] Xu H, Aldrich MC, Chen Q, Liu H, Peterson NB, Dai Q, Levy M, Shah A, Han X, Ruan X, Jiang M, Li Y, Julien JS, Warner J, Friedman C, Roden DM, Denny JC. Validating drug repurposing signals using electronic health records: a case study of metformin associated with reduced cancer mortality. J Am Med Inform Assoc 2015; 22(1): 179–191

[160] Savova GK, Tseytlin E, Finan S, Castine M, Miller T, Medvedeva O, Harris D, Hochheiser H, Lin C, Chavan G, Jacobson RS. DeepPhe: a natural language processing system for extracting cancer phenotypes from clinical records. Cancer Res 2017; 77(21): e115–e118

[161] Savova GK, Danciu I, Alamudun F, Miller T, Lin C, Bitterman DS, Tourassi G, Warner JL. Use of natural language processing to extract clinical cancer phenotypes from electronic medical records. Cancer Res 2019; 79(21): 5463–5470

[162] Kehl KL, Elmarakeby H, Nishino M, Van Allen EM, Lepisto EM, Hassett MJ, Johnson BE, Schrag D. Assessment of deep natural language processing in ascertaining oncologic outcomes from radiology reports. JAMA Oncol 2019; 5(10): 1421–1429

[163] VaswaniAShazeer NParmarNUszkoreitJJonesL GomezANKaiser ŁPolosukhinI. Attention is all you need. In: Advances in Neural Information Processing Systems. 2017; 30

[164] Thirunavukarasu AJ, Ting DSJ, Elangovan K, Gutierrez L, Tan TF, Ting DSW. Large language models in medicine. Nat Med 2023; 29(8): 1930–1940

[165] Qiu X, Sun T, Xu Y, Shao Y, Dai N, Huang X. Pre-trained models for natural language processing: a survey. Sci China Technol Sci 2020; 63(10): 1872–1897

[166] RadfordANarasimhan KSalimansTSutskeverI. Improving Language Understanding by Generative Pre-Training. 2018. Available at the website of cdn.openai.com

[167] Yeo YH, Samaan JS, Ng WH, Ting PS, Trivedi H, Vipani A, Ayoub W, Yang JD, Liran O, Spiegel B, Kuo A. Assessing the performance of ChatGPT in answering questions regarding cirrhosis and hepatocellular carcinoma. Clin Mol Hepatol 2023; 29(3): 721–732

[168] Hopkins AM, Logan JM, Kichenadasse G, Sorich MJ. Artificial intelligence chatbots will revolutionize how cancer patients access information: ChatGPT represents a paradigm-shift. JNCI Cancer Spectr 2023; 7(2): pkad010

[169] Sorin V, Klang E, Sklair-Levy M, Cohen I, Zippel DB, Balint Lahat N, Konen E, Barash Y. Large language model (ChatGPT) as a support tool for breast tumor board. NPJ Breast Cancer 2023; 9(1): 44

[170] Johnson SB, King AJ, Warner EL, Aneja S, Kann BH, Bylund CL. Using ChatGPT to evaluate cancer myths and misconceptions: artificial intelligence and cancer information. JNCI Cancer Spectr 2023; 7(2): pkad015

[171] Chen S, Kann BH, Foote MB, Aerts HJWL, Savova GK, Mak RH, Bitterman DS. Use of artificial intelligence chatbots for cancer treatment information. JAMA Oncol 2023; 9(10): 1459–1462

[172] Coskun B, Ocakoglu G, Yetemen M, Kaygisiz O. Can ChatGPT, an artificial intelligence language model, provide accurate and high-quality patient information on prostate cancer. Urology 2023; 180: 35–58

[173] Sanderson K. GPT-4 is here: what scientists think. Nature 2023; 615(7954): 773

[174] Lyu Q, Tan J, Zapadka ME, Ponnatapura J, Niu C, Myers KJ, Wang G, Whitlow CT. Translating radiology reports into plain language using ChatGPT and GPT-4 with prompt learning: results, limitations, and potential. Vis Comput Ind Biomed Art 2023; 6(1): 9

[175] Fink MA, Bischoff A, Fink CA, Moll M, Kroschke J, Dulz L, Heußel CP, Kauczor HU, Weber TF. Potential of ChatGPT and GPT-4 for data mining of free-text CT reports on lung cancer. Radiology 2023; 308(3): e231362

[176] Ibrahim H, Liu X, Denniston AK. Reporting guidelines for artificial intelligence in healthcare research. Clin Exp Ophthalmol 2021; 49(5): 470–476

[177] Cruz Rivera S, Liu X, Chan AW, Denniston AK, Calvert MJ; SPIRIT-AI, CONSORT-AI Working Group; SPIRIT-AI, CONSORT-AI Steering Group; SPIRIT-AI, CONSORT-AI Consensus Group. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension. Nat Med 2020; 26(9): 1351–1363

[178] Liu X, Cruz Rivera S, Moher D, Calvert MJ, Denniston AK; SPIRIT-AI, CONSORT-AI Working Group. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Nat Med 2020; 26(9): 1364–1374

[179] Norgeot B, Quer G, Beaulieu-Jones BK, Torkamani A, Dias R, Gianfrancesco M, Arnaout R, Kohane IS, Saria S, Topol E, Obermeyer Z, Yu B, Butte AJ. Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist. Nat Med 2020; 26(9): 1320–1324

[180] Hernandez-Boussard T, Bozkurt S, Ioannidis JPA, Shah NH. MINIMAR (MINimum Information for Medical AI Reporting): developing reporting standards for artificial intelligence in health care. J Am Med Inform Assoc 2020; 27(12): 2011–2015

[181] Sounderajah V, Ashrafian H, Golub RM, Shetty S, De Fauw J, Hooft L, Moons K, Collins G, Moher D, Bossuyt PM, Darzi A, Karthikesalingam A, Denniston AK, Mateen BA, Ting D, Treanor D, King D, Greaves F, Godwin J, Pearson-Stuttard J, Harling L, McInnes M, Rifai N, Tomasev N, Normahani P, Whiting P, Aggarwal R, Vollmer S, Markar SR, Panch T, Liu X; STARD-AI Steering Committee. Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol. BMJ Open 2021; 11(6): e047709

[182] Collins GS, Dhiman P, Andaur Navarro CL, Ma J, Hooft L, Reitsma JB, Logullo P, Beam AL, Peng L, Van Calster B, van Smeden M, Riley RD, Moons KG. Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence. BMJ Open 2021; 11(7): e048008

[183] Sounderajah V, Ashrafian H, Rose S, Shah NH, Ghassemi M, Golub R, Kahn CE Jr, Esteva A, Karthikesalingam A, Mateen B, Webster D, Milea D, Ting D, Treanor D, Cushnan D, King D, McPherson D, Glocker B, Greaves F, Harling L, Ordish J, Cohen JF, Deeks J, Leeflang M, Diamond M, McInnes MDF, McCradden M, Abràmoff MD, Normahani P, Markar SR, Chang S, Liu X, Mallett S, Shetty S, Denniston A, Collins GS, Moher D, Whiting P, Bossuyt PM, Darzi A. A quality assessment tool for artificial intelligence-centered diagnostic test accuracy studies: QUADAS-AI. Nat Med 2021; 27(10): 1663–1665

[184] Vasey B, Nagendran M, Campbell B, Clifton DA, Collins GS, Denaxas S, Denniston AK, Faes L, Geerts B, Ibrahim M, Liu X, Mateen BA, Mathur P, McCradden MD, Morgan L, Ordish J, Rogers C, Saria S, Ting DSW, Watkinson P, Weber W, Wheatstone P, McCulloch P; DECIDE-AI expert group. Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI. BMJ 2022; 377: e070904

[185] Cohen JF, Korevaar DA, Altman DG, Bruns DE, Gatsonis CA, Hooft L, Irwig L, Levine D, Reitsma JB, de Vet HC, Bossuyt PM. STARD 2015 guidelines for reporting diagnostic accuracy studies: explanation and elaboration. BMJ Open 2016; 6(11): e012799

[186] Smith M, Sattler A, Hong G, Lin S. From code to bedside: implementing artificial intelligence using quality improvement methods. J Gen Intern Med 2021; 36(4): 1061–1066

[187] Wiens J, Saria S, Sendak M, Ghassemi M, Liu VX, Doshi-Velez F, Jung K, Heller K, Kale D, Saeed M, Ossorio PN, Thadaney-Israni S, Goldenberg A. Do no harm: a roadmap for responsible machine learning for health care. Nat Med 2019; 25(9): 1337–1340

[188] Larson DB, Harvey H, Rubin DL, Irani N, Tse JR, Langlotz CP. Regulatory frameworks for development and evaluation of artificial intelligence-based diagnostic imaging algorithms: summary and recommendations. J Am Coll Radiol 2021; 18(3 3 Pt A): 413–424

[189] Gunning D, Stefik M, Choi J, Miller T, Stumpf S, Yang GZ. XAI-explainable artificial intelligence. Sci Robot 2019; 4(37): eaay7120

[190] Klein H, Mazor T, Siegel E, Trukhanov P, Ovalle A, Vecchio Fitz CD, Zwiesler Z, Kumari P, Van Der Veen B, Marriott E, Hansel J, Yu J, Albayrak A, Barry S, Keller RB, MacConaill LE, Lindeman N, Johnson BE, Rollins BJ, Do KT, Beardslee B, Shapiro G, Hector-Barry S, Methot J, Sholl L, Lindsay J, Hassett MJ, Cerami E. MatchMiner: an open-source platform for cancer precision medicine. NPJ Precis Oncol 2022; 6(1): 69

[191] Li T, Shetty S, Kamath A, Jaiswal A, Jiang X, Ding Y, Kim Y. CancerGPT for few shot drug pair synergy prediction using large pretrained language models. NPJ Digit Med 2024; 7(1): 40

[192] Hartman RI, Trepanowski N, Chang MS, Tepedino K, Gianacas C, McNiff JM, Fung M, Braghiroli NF, Grant-Kels JM. Multicenter prospective blinded melanoma detection study with a handheld elastic scattering spectroscopy device. JAAD Int 2024; 15: 24–31

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