{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T02:14:10Z","timestamp":1792030450931,"version":"4.4.0"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2024,6,20]],"date-time":"2024-06-20T00:00:00Z","timestamp":1718841600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,6,20]],"date-time":"2024-06-20T00:00:00Z","timestamp":1718841600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Artif Intell Rev"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Artificial intelligence (AI) is being used increasingly to detect fatal diseases such as cancer. The potential reduction in human error, rapid diagnosis, and consistency of judgment are the primary motives for using these applications. Artificial Neural Networks and Convolution Neural Networks are popular AI techniques being increasingly used in diagnosis. Numerous academics have explored and evaluated AI methods used in the detection of various cancer types for comparison and analysis. This study presents a thorough evaluation of the AI techniques used in cancer detection based on extensively researched studies and research trials published on the subject. The manuscript offers a thorough evaluation and comparison of the AI methods applied to the detection of five primary cancer types: breast cancer, lung cancer, colorectal cancer, prostate cancer, skin cancer, and digestive cancer. To determine how well these models compare with medical professionals\u2019 judgments, the opinions of developed models and of experts are compared and provided in this paper.<\/jats:p>","DOI":"10.1007\/s10462-024-10783-6","type":"journal-article","created":{"date-parts":[[2024,6,20]],"date-time":"2024-06-20T06:01:31Z","timestamp":1718863291000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["A comprehensive assessment of artificial intelligence applications for cancer diagnosis"],"prefix":"10.1007","volume":"57","author":[{"given":"Gaurav","family":"Singh","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anushka","family":"Kamalja","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rohit","family":"Patil","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ashutosh","family":"Karwa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Akansha","family":"Tripathi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pallavi","family":"Chavan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,6,20]]},"reference":[{"key":"10783_CR1","doi-asserted-by":"publisher","first-page":"811","DOI":"10.1007\/s10462-020-09865-y","volume":"54","author":"A Adegun","year":"2021","unstructured":"Adegun A, Viriri S (2021) Deep learning techniques for skin lesion analysis and melanoma cancer detection: a survey of state-of-the-art. Artif Intell Rev 54:811\u2013841","journal-title":"Artif Intell Rev"},{"key":"10783_CR2","first-page":"2666","volume":"5","author":"MS Ali","year":"2021","unstructured":"Ali MS, Miah MS et al (2021) An enhanced technique of skin cancer classification using deep convolutional neural network with transfer learning models. Mach Learn Appl 5:2666\u20138270","journal-title":"Mach Learn Appl"},{"key":"10783_CR3","doi-asserted-by":"publisher","first-page":"149","DOI":"10.2528\/PIER05081802","volume":"58","author":"G Bindu","year":"2006","unstructured":"Bindu G, Lonappan A, Thomas V et al (2006) Active microwave imaging for breast cancer detection. Progr Electromagn Res 58:149\u2013169","journal-title":"Progr Electromagn Res"},{"key":"10783_CR4","first-page":"31","volume":"13","author":"H Booven","year":"2021","unstructured":"Booven H, Kuchakulla M et al (2021) A systematic review of artificial intelligence in prostate cancer. Media Neliti Com 13:31\u201339","journal-title":"Media Neliti Com"},{"issue":"24","key":"10783_CR5","doi-asserted-by":"publisher","first-page":"13409","DOI":"10.3390\/ijerph182413409","volume":"18","author":"K Das","year":"2021","unstructured":"Das K et al (2021) Machine learning and its application in skin cancer. Int J Environ Res Public Health 18(24):13409","journal-title":"Int J Environ Res Public Health"},{"issue":"September","key":"10783_CR6","first-page":"1","volume":"10","author":"J Dong","year":"2020","unstructured":"Dong J, Geng Y, Lu D, Li B, Tian L, Lin D, Zhang Y (2020) Clinical trials for artificial intelligence in cancer diagnosis: a cross-sectional study of registered trials in clinicaltrials.gov. Front Oncol 10(September):1\u20136","journal-title":"Front Oncol"},{"issue":"9","key":"10783_CR7","first-page":"975","volume":"112","author":"R Goel","year":"2015","unstructured":"Goel R, Singh S (2015) Skin cancer detection using GLCM matrix analysis and back propagation neural network classifier. Int J Comput Appl 112(9):975\u20138887","journal-title":"Int J Comput Appl"},{"issue":"10","key":"10783_CR8","doi-asserted-by":"publisher","first-page":"3313","DOI":"10.3390\/jcm9103313","volume":"9","author":"H Goyal","year":"2020","unstructured":"Goyal H, Mann R et al (2020) Scope of artificial intelligence in screening and diagnosis of colorectal cancer. J Clin Med 9(10):3313","journal-title":"J Clin Med"},{"issue":"3","key":"10783_CR9","doi-asserted-by":"publisher","first-page":"183","DOI":"10.5152\/dir.2019.19125","volume":"25","author":"S Harmon","year":"2019","unstructured":"Harmon S, Tuncer S et al (2019) Artificial intelligence at intersection of pathology and radiology in prostate cancer. Diagn Interv Radiol 25(3):183\u2013188","journal-title":"Diagn Interv Radiol"},{"key":"10783_CR10","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1016\/j.ejca.2019.07.019","volume":"120","author":"A Hekler","year":"2019","unstructured":"Hekler A et al (2019) Superior skin cancer classification by the combination of human and artificial intelligence. Eur J Cancer 120:114\u2013121","journal-title":"Eur J Cancer"},{"issue":"4","key":"10783_CR11","doi-asserted-by":"publisher","first-page":"653","DOI":"10.1007\/s10120-018-0793-2","volume":"21","author":"T Hirasawa","year":"2018","unstructured":"Hirasawa T et al (2018) Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images. Gastric Cancer 21(4):653\u2013660","journal-title":"Gastric Cancer"},{"issue":"1","key":"10783_CR12","first-page":"1","volume":"12","author":"C Ho","year":"2022","unstructured":"Ho C, Zhao Z et al (2022) A promising deep learning-assistive algorithm for histopathological screening of colorectal cancer. Artif Intell Rev 12(1):1\u20139","journal-title":"Artif Intell Rev"},{"issue":"5","key":"10783_CR13","doi-asserted-by":"publisher","first-page":"3299","DOI":"10.1007\/s10462-020-09948-w","volume":"54","author":"M Injadat","year":"2021","unstructured":"Injadat M, Moubayed A, Nassif A, Shami A (2021) Machine learning towards intelligent systems: applications, challenges, and opportunities. Artif Intell Rev 54(5):3299\u20133348","journal-title":"Artif Intell Rev"},{"key":"10783_CR14","unstructured":"Kanimozhi T, Murthi A (2016) Computer aided melanoma skin cancer detection using artificial neural network classifier. Singaporean Journal of Scientific Research (SJSR). J Selected Areas Microelectron (JSAM) 8(2):35\u201342"},{"issue":"3","key":"10783_CR15","doi-asserted-by":"publisher","first-page":"e138","DOI":"10.1016\/S2589-7500(20)30003-0","volume":"2","author":"H Kim","year":"2020","unstructured":"Kim H et al (2020) Changes in cancer detection and false-positive recall in mammography using artificial intelligence: a retrospective, multireader study. Lancet Digital Health 2(3):e138\u2013e148","journal-title":"Lancet Digital Health"},{"issue":"3","key":"10783_CR16","doi-asserted-by":"publisher","first-page":"514","DOI":"10.3390\/diagnostics11030514","volume":"11","author":"N Lorenzovici","year":"2021","unstructured":"Lorenzovici N, Dulf E et al (2021) Artificial intelligence in colorectal cancer diagnosis using clinical data: non-invasive approach. Diagnostics 11(3):514","journal-title":"Diagnostics"},{"issue":"10","key":"10783_CR17","doi-asserted-by":"publisher","first-page":"94","DOI":"10.9790\/0853-15100494101","volume":"15","author":"M Mustafa","year":"2016","unstructured":"Mustafa M, Azizi A et al (2016) Lung cancer: risk factors, management, and prognosis. IOSR J Dental Med Sci 15(10):94\u2013101","journal-title":"IOSR J Dental Med Sci"},{"issue":"1","key":"10783_CR18","first-page":"98","volume":"2021","author":"M Naderan","year":"2021","unstructured":"Naderan M (2021) Review methods for breast cancer detection using artificial intelligence and deep learning methods. Syst Res Inf Technol 2021(1):98\u2013102","journal-title":"Syst Res Inf Technol"},{"issue":"36","key":"10783_CR19","doi-asserted-by":"publisher","first-page":"5408","DOI":"10.3748\/wjg.v26.i36.5408","volume":"26","author":"PH Niu","year":"2020","unstructured":"Niu PH, Zhao L-L et al (2020) Artificial intelligence in gastric cancer: application and future perspectives. World J Gastroenterol 26(36):5408\u20135419","journal-title":"World J Gastroenterol"},{"issue":"1","key":"10783_CR20","doi-asserted-by":"publisher","first-page":"9","DOI":"10.17146\/aij.2019.860","volume":"45","author":"T Pandiangan","year":"2019","unstructured":"Pandiangan T, Bali I, Silalahi A (2019) Early lung cancer detection using artificial neural network. Atom Indones 45(1):9\u201315","journal-title":"Atom Indones"},{"issue":"5","key":"10783_CR21","doi-asserted-by":"publisher","first-page":"3747","DOI":"10.1007\/s10462-021-10084-2","volume":"55","author":"K Patel","year":"2022","unstructured":"Patel K et al (2022) A survey on artificial intelligence techniques for chronic diseases: open issues and challenges. Artif Intell Rev 55(5):3747\u20133800","journal-title":"Artif Intell Rev"},{"key":"10783_CR22","doi-asserted-by":"publisher","first-page":"1588","DOI":"10.1038\/s41379-021-00794-x","volume":"34","author":"S Perincheri","year":"2021","unstructured":"Perincheri S, Levi A et al (2021) An independent assessment of an artificial intelligence system for prostate cancer detection shows strong diagnostic accuracy. Mod Pathol 34:1588\u20131595","journal-title":"Mod Pathol"},{"issue":"7","key":"10783_CR23","doi-asserted-by":"publisher","first-page":"10","DOI":"10.14569\/IJARAI.2014.030703","volume":"3","author":"C Prasetyo","year":"2014","unstructured":"Prasetyo C, Kardiana A, Yuliwulandari R (2014) Breast cancer diagnosis using artificial neural networks with extreme learning techniques. Int J Adv Res Artif Intell 3(7):10\u201314","journal-title":"Int J Adv Res Artif Intell"},{"issue":"9","key":"10783_CR24","doi-asserted-by":"publisher","first-page":"916","DOI":"10.1093\/jnci\/djy222","volume":"111","author":"A Rodriguez-Ruiz","year":"2019","unstructured":"Rodriguez-Ruiz A, L\u00e5ng K, Gubern-Merida A, Broeders M, Gennaro G, Clauser P, Helbich TH, Chevalier M, Tan T, Mertelmeier T et al (2019) Stand-alone artificial intelligence for breast cancer detection in mammography: comparison with 101 radiologists. JNCI J Natl Cancer Inst 111(9):916\u2013922","journal-title":"JNCI J Natl Cancer Inst"},{"issue":"9","key":"10783_CR25","doi-asserted-by":"publisher","first-page":"1274","DOI":"10.1016\/j.jiph.2020.06.033","volume":"13","author":"T Saba","year":"2020","unstructured":"Saba T (2020) Recent advancement in cancer detection using machine learning: systematic survey of decades, comparisons and challenges. J Infect Public Health 13(9):1274\u20131289","journal-title":"J Infect Public Health"},{"issue":"8","key":"10783_CR26","first-page":"1","volume":"12","author":"K Sathykumar","year":"2020","unstructured":"Sathykumar K, Munoz M et al (2020) Automated lung cancer detection using artificial intelligence (AI) deep convolutional neural networks: a narrative literature review. Cureus 12(8):1\u20139","journal-title":"Cureus"},{"issue":"01","key":"10783_CR27","doi-asserted-by":"publisher","first-page":"1121","DOI":"10.22214\/ijraset.2021.38582","volume":"09","author":"HU Sharif","year":"2021","unstructured":"Sharif HU (2021) Breast cancer detection using artificial neural networks. Int J Res Appl Sci Eng Technol 09(01):1121\u20131126","journal-title":"Int J Res Appl Sci Eng Technol"},{"issue":"4","key":"10783_CR28","doi-asserted-by":"publisher","first-page":"2641","DOI":"10.1007\/s10462-021-10074-4","volume":"55","author":"KA Shastry","year":"2022","unstructured":"Shastry KA, Sanjay H (2022) Cancer diagnosis using artificial intelligence: a review. Artif Intell Rev 55(4):2641\u20132673","journal-title":"Artif Intell Rev"},{"key":"10783_CR29","doi-asserted-by":"publisher","first-page":"239","DOI":"10.2147\/CMAR.S149619","volume":"10","author":"R Sitarz","year":"2018","unstructured":"Sitarz R, Skierucha M et al (2018) Gastric cancer: epidemiology, prevention, classification, and treatment. Cancer Manag Res 10:239\u2013248","journal-title":"Cancer Manag Res"},{"key":"10783_CR30","doi-asserted-by":"publisher","first-page":"3287","DOI":"10.1007\/s10462-019-09764-x","volume":"53","author":"M Sokouti","year":"2019","unstructured":"Sokouti M, Sokouti M, Sokouti B (2019) A systematic review and meta-analysis on performance of intelligent systems in lung cancer: where are we? Artif Intell Rev 53:3287\u20133298","journal-title":"Artif Intell Rev"},{"issue":"2","key":"10783_CR31","doi-asserted-by":"publisher","first-page":"254","DOI":"10.1111\/den.13897","volume":"33","author":"H Suzuki","year":"2021","unstructured":"Suzuki H, Yoshitaka T et al (2021) Artificial intelligence for cancer detection of the upper gastrointestinal tract. Dig Endosc 33(2):254\u2013262","journal-title":"Dig Endosc"},{"key":"10783_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.ebiom.2020.103146","volume":"62","author":"D Tang","year":"2020","unstructured":"Tang D et al (2020) Development and validation of a real-time artificial intelligence-assisted system for detecting early gastric cancer: a multicentre retrospective diagnostic study. EBioMedicine 62:103146","journal-title":"EBioMedicine"},{"issue":"38","key":"10783_CR33","doi-asserted-by":"publisher","first-page":"6399","DOI":"10.3748\/wjg.v27.i38.6399","volume":"27","author":"M Viscaino","year":"2021","unstructured":"Viscaino M, Bustos J et al (2021) Artificial intelligence for the early detection of colorectal cancer: a comprehensive review of its advantages and misconceptions. World J Gastroenterol 27(38):6399\u20136414","journal-title":"World J Gastroenterol"},{"issue":"4","key":"10783_CR34","doi-asserted-by":"publisher","first-page":"625","DOI":"10.1007\/s10278-019-00192-5","volume":"32","author":"A Watanabe","year":"2019","unstructured":"Watanabe A, Lim V, Vu H et al (2019) Improved cancer detection using artificial intelligence: a retrospective evaluation of missed cancers on mammography. J Digit Imaging 32(4):625\u2013637","journal-title":"J Digit Imaging"},{"issue":"1","key":"10783_CR35","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1007\/s10462-021-10034-y","volume":"55","author":"C Yu","year":"2022","unstructured":"Yu C, Helwig E (2022) The role of AI technology in prediction, diagnosis and treatment of colorectal cancer. Artif Intell Rev 55(1):323\u2013343","journal-title":"Artif Intell Rev"}],"container-title":["Artificial Intelligence Review"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-024-10783-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10462-024-10783-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-024-10783-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,15]],"date-time":"2024-07-15T10:23:26Z","timestamp":1721039006000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10462-024-10783-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,20]]},"references-count":35,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2024,7]]}},"alternative-id":["10783"],"URL":"https:\/\/doi.org\/10.1007\/s10462-024-10783-6","relation":{},"ISSN":["1573-7462"],"issn-type":[{"value":"1573-7462","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,20]]},"assertion":[{"value":"4 May 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 June 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All the authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest (such as honoraria; educational grants; participation in speakers\u2019 bureaus; membership, employment, consultancies, stock ownership, or other equity interest; and expert testimony or patent-licensing arrangements) or non-financial interest (such as personal or professional relationships, affiliations, knowledge, or beliefs) in the subject matter or materials discussed in this manuscript.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"No human or animal was harmed while performing this review.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Research involving human participants and\/or animals"}}],"article-number":"179"}}