{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T19:44:40Z","timestamp":1786391080799,"version":"3.56.0"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2019,4,26]],"date-time":"2019-04-26T00:00:00Z","timestamp":1556236800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2019,4,26]],"date-time":"2019-04-26T00:00:00Z","timestamp":1556236800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"American Society of Clinical Oncology Conquer Cancer Foundation The Brain Tumour Foundation of Canada"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Digit. Med."],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Advancements in computer vision and artificial intelligence (AI) carry the potential to make significant contributions to health care, particularly in diagnostic specialties such as radiology and pathology. The impact of these technologies on physician stakeholders is the subject of significant speculation. There is however a dearth of information regarding the opinions, enthusiasm, and concerns of the pathology community at large. Here, we report results from a survey of 487 pathologist-respondents practicing in 54 countries, conducted to examine perspectives on AI implementation in clinical practice. Despite limitations, including difficulty with quantifying response bias and verifying identity of respondents to this anonymous and voluntary survey, several interesting findings were uncovered. Overall, respondents carried generally positive attitudes towards AI, with nearly 75% reporting interest or excitement in AI as a diagnostic tool to facilitate improvements in workflow efficiency and quality assurance in pathology. Importantly, even within the more optimistic cohort, a significant number of respondents endorsed concerns about AI, including the potential for job displacement and replacement. Overall, around 80% of respondents predicted the introduction of AI technology in the pathology laboratory within the coming decade. Attempts to identify statistically significant demographic characteristics (e.g., age, sex, type\/place of practice) predictive of attitudes towards AI using Kolmogorov\u2013Smirnov (KS) testing revealed several associations. Important themes which were commented on by respondents included the need for increasing efforts towards physician training and resolving medical-legal implications prior to the generalized implementation of AI in pathology.<\/jats:p>","DOI":"10.1038\/s41746-019-0106-0","type":"journal-article","created":{"date-parts":[[2019,4,26]],"date-time":"2019-04-26T10:03:17Z","timestamp":1556272997000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":254,"title":["Physician perspectives on integration of artificial intelligence into diagnostic pathology"],"prefix":"10.1038","volume":"2","author":[{"given":"Shihab","family":"Sarwar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anglin","family":"Dent","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kevin","family":"Faust","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maxime","family":"Richer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ugljesa","family":"Djuric","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7948-7530","authenticated-orcid":false,"given":"Randy","family":"Van Ommeren","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5291-9068","authenticated-orcid":false,"given":"Phedias","family":"Diamandis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2019,4,26]]},"reference":[{"key":"106_CR1","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/S0933-3657(01)00077-X","volume":"23","author":"I Kononenko","year":"2001","unstructured":"Kononenko, I. Machine learning for medical diagnosis: history, state of the art and perspective. Artif. Intell. Med. 23, 89\u2013109 (2001).","journal-title":"Artif. Intell. Med"},{"key":"106_CR2","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1038\/nature16961","volume":"529","author":"D Silver","year":"2016","unstructured":"Silver, D. et al. Mastering the game of Go with deep neural networks and tree search. Nature 529, 484\u2013489 (2016).","journal-title":"Nature"},{"key":"106_CR3","unstructured":"Silver, D. H. et al. Mastering chess and shogi by self-play with a general reinforcement learning algorithm. arXiv.org arXiv:1712.01815, 1\u201319 (2017)."},{"key":"106_CR4","unstructured":"Raghu, A. K. et al. Reinforcement learning for sepsis treatment. arXiv.org arXiv:1711.09602 (2017)."},{"key":"106_CR5","doi-asserted-by":"publisher","first-page":"574","DOI":"10.1148\/radiol.2017162326","volume":"284","author":"P Lakhani","year":"2017","unstructured":"Lakhani, P. & Sundaram, B. Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology 284, 574\u2013582 (2017).","journal-title":"Radiology"},{"key":"106_CR6","doi-asserted-by":"publisher","DOI":"10.1038\/srep24454","volume":"6","author":"JZ Cheng","year":"2016","unstructured":"Cheng, J. Z. et al. Computer-aided diagnosis with deep learning architecture: applications to breast lesions in US images and pulmonary nodules in CT scans. Sci. Rep. 6, 24454 (2016).","journal-title":"Sci. Rep."},{"key":"106_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1117\/1.JBO.22.6.060503","volume":"22","author":"ML Halicek","year":"2017","unstructured":"Halicek, M. L. et al. Deep convolutional neural networks for classifying head and neck cancer using hyperspectral imaging. J. Biomed. Opt. 22, 1\u20134 (2017).","journal-title":"J. Biomed. Opt."},{"key":"106_CR8","first-page":"1","volume":"3","author":"BL Huynh","year":"2016","unstructured":"Huynh, B. L. & Giger, H. M. Digital mammographic tumor classification using transfer learning from deep convolutional neural networks. J. Med. Imaging 3, 1\u20135 (2016).","journal-title":"J. Med. Imaging"},{"key":"106_CR9","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1111\/bjd.16924","volume":"180","author":"Y Fujisawa","year":"2018","unstructured":"Fujisawa, Y. et al. Deep learning-based, computer-aided classifier developed with a small dataset of clinical images surpasses board-certified dermatologists in skin tumor diagnosis. Br. J. Dermatol. 180, 373\u2013381 (2018).","journal-title":"Br. J. Dermatol"},{"key":"106_CR10","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1038\/nature21056","volume":"542","author":"A Esteva","year":"2017","unstructured":"Esteva, A. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115\u2013118 (2017).","journal-title":"Nature"},{"key":"106_CR11","doi-asserted-by":"publisher","first-page":"2402","DOI":"10.1001\/jama.2016.17216","volume":"316","author":"V Gulshan","year":"2016","unstructured":"Gulshan, V. et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316, 2402\u20132410 (2016).","journal-title":"JAMA"},{"key":"106_CR12","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-018-21758-3","volume":"8","author":"D Bychkov","year":"2018","unstructured":"Bychkov, D. et al. Deep learning based tissue analysis predicts outcome in colorectal cancer. Sci. Rep. 8, 3395 (2018).","journal-title":"Sci. Rep."},{"key":"106_CR13","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1016\/j.media.2014.11.010","volume":"20","author":"M Veta","year":"2015","unstructured":"Veta, M. et al. Assessment of algorithms for mitosis detection in breast cancer histopathology images. Med. Image Anal. 20, 237\u2013248 (2015).","journal-title":"Med. Image Anal."},{"key":"106_CR14","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1080\/10408363.2018.1536111","volume":"56","author":"QF Xie","year":"2019","unstructured":"Xie, Q. F. et al. Deep learning for image analysis: Personalizing medicine closer to the point of care. Crit. Rev. Clin. Lab. Sci. 56, 61\u201373 (2019).","journal-title":"Crit. Rev. Clin. Lab. Sci."},{"key":"106_CR15","doi-asserted-by":"publisher","first-page":"1196","DOI":"10.1109\/TMI.2016.2525803","volume":"35","author":"K Sirinukunwattana","year":"2016","unstructured":"Sirinukunwattana, K. et al. Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images. IEEE Trans. Med. Imaging 35, 1196\u20131206 (2016).","journal-title":"IEEE Trans. Med. Imaging"},{"key":"106_CR16","first-page":"411","volume":"16","author":"DC Ciresan","year":"2013","unstructured":"Ciresan, D. C., Giusti, A., Gambardella, L. M. & Schmidhuber, J. Mitosis detection in breast cancer histology images with deep neural networks. Med. Image Comput. Comput. Assist. Inter. 16, 411\u2013418 (2013).","journal-title":"Med. Image Comput. Comput. Assist. Inter."},{"key":"106_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-018-2184-4","volume":"19","author":"KX Faust","year":"2018","unstructured":"Faust, K. X. et al. Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction. BMC Bioinforma. 19, 1\u201315 (2018).","journal-title":"BMC Bioinforma."},{"key":"106_CR18","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/j.trsl.2017.10.010","volume":"194","author":"S Robertson","year":"2018","unstructured":"Robertson, S., Azizpour, H., Smith, K. & Hartman, J. Digital image analysis in breast pathology-from image processing techniques to artificial intelligence. Transl. Res. 194, 19\u201335 (2018).","journal-title":"Transl. Res."},{"key":"106_CR19","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/S0933-3657(01)00094-X","volume":"24","author":"ZH Zhou","year":"2002","unstructured":"Zhou, Z. H., Jiang, Y., Yang, Y. B. & Chen, S. F. Lung cancer cell identification based on artificial neural network ensembles. Artif. Intell. Med. 24, 25\u201336 (2002).","journal-title":"Artif. Intell. Med."},{"key":"106_CR20","first-page":"05718","volume":"1606","author":"D Wang","year":"2016","unstructured":"Wang, D., Khosla, A., Gargeya, R., Irshad, H. & Beck, A. H. Deep learning for identifying metastatic breast cancer. arXiv 1606, 05718 (2016).","journal-title":"arXiv"},{"key":"106_CR21","doi-asserted-by":"publisher","first-page":"1559","DOI":"10.1038\/s41591-018-0177-5","volume":"24","author":"NO Coudray","year":"2018","unstructured":"Coudray, N. O. et al. Classification and mutation prediction from non\u2013small cell lung cancer histopathology images using deep learning. Nat. Med. 24, 1559\u20131567 (2018).","journal-title":"Nat. Med."},{"key":"106_CR22","doi-asserted-by":"publisher","DOI":"10.1038\/srep26286","volume":"6","author":"G Litjens","year":"2016","unstructured":"Litjens, G. et al. Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis. Sci. Rep. 6, 26286 (2016).","journal-title":"Sci. Rep."},{"key":"106_CR23","doi-asserted-by":"crossref","unstructured":"de Bel, T. H. et al. Automatic segmentation of histopathological slides of renal tissue using deep learning. Digital Pathology 1058112 (2018).","DOI":"10.1117\/12.2293717"},{"key":"106_CR24","first-page":"1","volume":"22","author":"U Djuric","year":"2017","unstructured":"Djuric, U., Zadeh, G., Aldape, K. & Diamandis, P. Precision histology: how deep learning is poised to revitalize histomorphology for personalized cancer care. npj Precisi. Oncol. 22, 1\u20135 (2017).","journal-title":"npj Precisi. Oncol."},{"key":"106_CR25","unstructured":"Ching, T. et al. Opportunities and obstacles for deep learning in biology and medicine. J R Soc Interface 15, pii: 20170387 (2018)."},{"key":"106_CR26","unstructured":"FDA allows marketing of first whole slide imaging system for digital pathology, https:\/\/www.fda.gov\/newsevents\/newsroom\/pressannouncements\/ucm552742.htm. (2017)."},{"key":"106_CR27","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1111\/his.12993","volume":"70","author":"J Griffin","year":"2017","unstructured":"Griffin, J. & Treanor, D. Digital pathology in clinical use: where are we now and what is holding us back? Histopathology 70, 134\u2013145 (2017).","journal-title":"Histopathology"},{"key":"106_CR28","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1016\/j.media.2016.06.037","volume":"33","author":"A Madabhushi","year":"2016","unstructured":"Madabhushi, A. & Lee, G. Image analysis and machine learning in digital pathology: Challenges and opportunities. Med. Image Anal. 33, 170\u2013175 (2016).","journal-title":"Med. Image Anal"},{"key":"106_CR29","doi-asserted-by":"crossref","first-page":"615","DOI":"10.5858\/2007-131-615-TMAOEI","volume":"131","author":"MJ Kornstein","year":"2007","unstructured":"Kornstein, M. J. & Byrne, S. P. The medicolegal aspect of error in pathology: a search of jury verdicts and settlements. Arch. Pathol. Lab Med. 131, 615\u2013618 (2007).","journal-title":"Arch. Pathol. Lab Med"},{"key":"106_CR30","doi-asserted-by":"publisher","first-page":"506","DOI":"10.1016\/j.clsr.2015.05.012","volume":"31","author":"M Schellekens","year":"2015","unstructured":"Schellekens, M. Self-driving cars and the chilling effect of liability law. Comput. Law Secur. Rev. 31, 506\u2013517 (2015).","journal-title":"Comput. Law Secur. Rev."},{"key":"106_CR31","unstructured":"Meeting Pathology Demand: Histopathology Workforce Census 2017-2018, https:\/\/www.rcpath.org\/uploads\/assets\/uploaded\/aff26c51-8b62-463f-98625b1d3f6174b6.pdf (2018)."},{"key":"106_CR32","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1080\/01621459.1951.10500769","volume":"46","author":"F Massey","year":"1951","unstructured":"Massey, F. The Kolmogorov-Smirnov Test for Goodness of Fit. J. Am. Stat. Assoc. 46, 68\u201378 (1951).","journal-title":"J. Am. Stat. Assoc."}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-019-0106-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-019-0106-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-019-0106-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,17]],"date-time":"2022-12-17T18:26:15Z","timestamp":1671301575000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-019-0106-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,4,26]]},"references-count":32,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2019,12]]}},"alternative-id":["106"],"URL":"https:\/\/doi.org\/10.1038\/s41746-019-0106-0","relation":{},"ISSN":["2398-6352"],"issn-type":[{"value":"2398-6352","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,4,26]]},"assertion":[{"value":"4 December 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 April 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 April 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"28"}}