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Cited by 293Artificial Intelligence in Healthcare and EducationAI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Artificial intelligence (AI) in medicine, current applications and future role with special emphasis on its potential and promise in pathology: present and future impact, obstacles including costs and acceptance among pathologists, practical and philosophical considerations. A comprehensive review

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Ahmad, Zubair, Rahim, Shabina, Zubair, Maha, Abdul-Ghafar, Jamshid·2021·Diagnostic Pathology
DOI 10.1186/s13000-021-01085-4PMC PMC7971952
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Abstract

BACKGROUND: The role of Artificial intelligence (AI) which is defined as the ability of computers to perform tasks that normally require human intelligence is constantly expanding. Medicine was slow to embrace AI. However, the role of AI in medicine is rapidly expanding and promises to revolutionize patient care in the coming years. In addition, it has the ability to democratize high level medical care and make it accessible to all parts of the world. MAIN TEXT: Among specialties of medicine, some like radiology were relatively quick to adopt AI whereas others especially pathology (and surgical pathology in particular) are only just beginning to utilize AI. AI promises to play a major role in accurate diagnosis, prognosis and treatment of cancers. In this paper, the general principles of AI are defined first followed by a detailed discussion of its current role in medicine. In the second half of this comprehensive review, the current and future role of AI in surgical pathology is discussed in detail including an account of the practical difficulties involved and the fear of pathologists of being replaced by computer algorithms. A number of recent studies which demonstrate the usefulness of AI in the practice of surgical pathology are highlighted. CONCLUSION: AI has the potential to transform the practice of surgical pathology by ensuring rapid and accurate results and enabling pathologists to focus on higher level diagnostic and consultative tasks such as integrating molecular, morphologic and clinical information to make accurate diagnosis in difficult cases, determine prognosis objectively and in this way contribute to personalized care.

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  1. [1]Farnell DA Huntsman D Bashashati A The coming 15 years in gynaecological pathology: digitisation, artificial intelligence, and new technologies Histopathology. 2020 76 1 171 177 10.1111/his.13991 31846526 · DOI
  2. [2]Goldenberg SL Nir G Salcudean SE A new era: artificial intelligence and machine learning in prostate cancer Nat Rev Urol 2019 16 7 391 403 10.1038/s41585-019-0193-3 31092914 · DOI
  3. [3]Bonanno E Toschi N Bombonati A Muto P Schillaci O Imaging diagnostic and pathology in the Management of Oncological-Patients Contrast Media Mol Imaging 2019 2019 2513680 10.1155/2019/2513680 31316305 PMC6604463 · DOI
  4. [4]Cohen S Furie MB Artificial intelligence and pathobiology join forces in the American journal of pathology Am J Pathol 2019 189 1 4 5 10.1016/j.ajpath.2018.11.002 30558721 · DOI
  5. [5]Wong STC Is pathology prepared for the adoption of artificial intelligence? Cancer Cytopathol 2018 126 6 373 375 10.1002/cncy.21994 29663732 · DOI
  6. [6]Golden JA Deep learning algorithms for detection of lymph node metastases from breast Cancer: helping artificial intelligence be seen JAMA. 2017 318 22 2184 2186 10.1001/jama.2017.14580 29234791 · DOI
  7. [9]Yu KH Kohane IS Framing the challenges of artificial intelligence in medicine BMJ Qual Saf 2019 28 3 238 241 10.1136/bmjqs-2018-008551 30291179 · DOI · Paper page
  8. [10]Nakagawa K Ishihara R Aoyama K Ohmori M Nakahira H Classification for invasion depth of esophageal squamous cell carcinoma using a deep neural network compared with experienced endoscopists Gastrointest Endosc 2019 90 3 407 414 10.1016/j.gie.2019.04.245 31077698 · DOI
  9. [11]Horie Y Yoshio T Aoyama K Yoshimizu S Horiuchi Y Diagnostic outcomes of esophageal cancer by artificial intelligence using convolutional neural networks Gastrointest Endosc 2019 89 1 25 32 10.1016/j.gie.2018.07.037 30120958 · DOI · Paper page
  10. [12]Hirasawa T Aoyama K Tanimoto T Ishihara S Shichijo S Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images Gastric Cancer 2018 21 4 653 660 10.1007/s10120-018-0793-2 29335825 · DOI · Paper page
  11. [14]Pavlova O Gilliet M Hohl D Digital pathology for the dermatologist Rev Med Suisse 2020 16 688 618 621 32239834
  12. [15]Gulshan V Peng L Coram M Stumpe MC Wu D Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs JAMA. 2016 316 22 2402 2410 10.1001/jama.2016.17216 27898976 · DOI · Paper page
  13. [16]Dong Y Xu L Fan Y Xiang P Gao X A novel surgical predictive model for Chinese Crohn's disease patients Medicine (Baltimore) 2019 98 46 e17510 10.1097/MD.0000000000017510 31725605 PMC6867775 · DOI
  14. [17]Wall J Krummel T The digital surgeon: How big data, automation, and artificial intelligence will change surgical practice J Pediatr Surg 2020 55S 47 50 10.1016/j.jpedsurg.2019.09.008 31767194 · DOI
  15. [18]Lee JH Ha EJ Kim JH Application of deep learning to the diagnosis of cervical lymph node metastasis from thyroid cancer with CT Eur Radiol 2019 29 10 5452 5457 10.1007/s00330-019-06098-8 30877461 · DOI
  16. [19]Liu X Zhou H Hu Z Jin Q Wang J Ye B Clinical application of artificial intelligence recognition technology in the diagnosis of stage T1 lung Cancer Zhongguo Fei Ai Za Zhi 2019 22 5 319 323 10.3779/j.issn.1009-3419.2019.05.09 31109442 PMC6533191 · DOI
  17. [20]Kudo Y Predicting cancer outcome: artificial intelligence vs. pathologists Oral Dis 2019 25 3 643 645 10.1111/odi.12954 30095205 · DOI
  18. [21]Nakamoto T Takahashi W Haga A Takahashi S Kiryu S Nawa K Ohta T Ozaki S Nozawa Y Tanaka S Mukasa A Nakagawa K Prediction of malignant glioma grades using contrast-enhanced T1-weighted and T2-weighted magnetic resonance images based on a radiomic analysis Sci Rep 2019 9 1 19411 10.1038/s41598-019-55922-0 31857632 PMC6923390 · DOI
  19. [22]Mobadersany P Yousefi S Amgad M Gutman DA Barnholtz-Sloan JS Predicting cancer outcomes from histology and genomics using convolutional networks Proc Natl Acad Sci U S A 2018 115 13 E2970 E2979 10.1073/pnas.1717139115 29531073 PMC5879673 · DOI · Paper page
  20. [23]Ahammed Muneer KV Rajendran VR K PJ Glioma Tumor Grade Identification Using Artificial Intelligent Techniques J Med Syst 2019 43 5 113 10.1007/s10916-019-1228-2 30900029 · DOI
  21. [24]Koelzer VH Sirinukunwattana K Rittscher J Mertz KD Precision immunoprofiling by image analysis and artificial intelligence Virchows Arch 2019 474 4 511 522 10.1007/s00428-018-2485-z 30470933 PMC6447694 · DOI
  22. [25]Printz C Artificial intelligence platform for oncology could assist in treatment decisions Cancer. 2017 123 6 905 10.1002/cncr.30655 28263386 · DOI
  23. [26]Somashekhar SP Sepúlveda MJ Puglielli S Norden AD Watson for oncology and breast cancer treatment recommendations: agreement with an expert multidisciplinary tumor board Ann Oncol 2018 29 2 418 423 10.1093/annonc/mdx781 29324970 · DOI
  24. [27]Liu C Liu X Wu F Xie M Feng Y Using artificial intelligence (Watson for oncology) for treatment recommendations amongst Chinese patients with lung Cancer: feasibility study J Med Internet Res 2018 20 9 e11087 10.2196/11087 30257820 PMC6231834 · DOI
  25. [28]Kayser K Gortler J Bogovac M Bogovac A Goldmann T AI (artificial intelligence) in histopathology--from image analysis to automated diagnosis Folia Histochem Cytobiol 2009 47 3 355 361 10.2478/v10042-009-0087-y 20164018 · DOI
  26. [29]Farahani N Pantanowitz L Overview of Telepathology Surg Pathol Clin 2015 8 2 223 231 10.1016/j.path.2015.02.018 26065796 · DOI
  27. [30]Dietz RL Hartman DJ Zheng L Wiley C Pantanowitz L Review of the use of telepathology for intraoperative consultation Expert Rev Med Devices 2018 15 12 883 890 10.1080/17434440.2018.1549987 30451027 · DOI
  28. [31]Susaki EA Recent developments in a automated diagnosis of pathological images and three-dimensional histopathology Brain Nerve 2019 71 7 723 732 10.11477/mf.1416201344 31289246 · DOI
  29. [32]Zemouri R Devalland C Valmary-Degano S Zerhouni N Neural network: a future in pathology? Ann Pathol 2019 39 2 119 129 10.1016/j.annpat.2019.01.004 30773224 · DOI
  30. [33]Napolitano G Marshall A Hamilton P Gavin AT Machine learning classification of surgical pathology reports and chunk recognition for information extraction noise reduction Artif Intell Med 2016 70 77 83 10.1016/j.artmed.2016.06.001 27431038 · DOI
  31. [34]Colling R Pitman H Oien K Rajpoot N Macklin P Artificial intelligence in digital pathology: a roadmap to routine use in clinical practice J Pathol 2019 249 2 143 150 10.1002/path.5310 31144302 · DOI
  32. [35]Jones AD Graff JP Darrow M Borowsky A Olson KA Impact of pre-analytical variables on deep learning accuracy in histopathology Histopathology. 2019 75 1 39 53 10.1111/his.13844 30801768 PMC6591093 · DOI
  33. [36]Saravanan C Schumacher V Brown D Dunstan R Galarneau JR Meeting report: tissue-based image analysis Toxicol Pathol 2017 45 7 983 1003 10.1177/0192623317737468 29162012 · DOI
  34. [37]Nabi J Artificial intelligence can augment global pathology initiatives Lancet. 2018 392 10162 2351 2352 10.1016/S0140-6736(18)32209-8 30527613 · DOI
  35. [38]Granter SR Beck AH Papke DJ Jr Straw men, deep learning, and the future of the human Microscopist: response to "artificial intelligence and the pathologist: future Frenemies?" Arch Pathol Lab Med. 2017 141 5 624 10.5858/arpa.2017-0023-ED 28447901 · DOI
  36. [39]Yu KH Zhang C Berry GJ Altman RB Re C Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features Nat Commun 2016 7 12474 10.1038/ncomms12474 27527408 PMC4990706 · DOI · Paper page
  37. [40]Kumar N Tafe LJ Higgins JH Peterson JD de Abreu FB Identifying associations between somatic mutations and clinicopathologic findings in lung cancer pathology reports Methods Inf Med 2018 57 1 63 73 10.3414/ME17-01-0039 29621832 · DOI
  38. [42]Granter SR Beck AH Papke DJ Jr AlphaGo, deep learning, and the future of the human Microscopist Arch Pathol Lab Med. 2017 141 5 619 621 10.5858/arpa.2016-0471-ED 28447900 · DOI · Paper page
  39. [43]Sharma G Carter A Artificial intelligence and the pathologist: future Frenemies? Arch Pathol Lab Med 2017 141 5 622 623 10.5858/arpa.2016-0593-ED 28447905 · DOI
  40. [44]Wild PJ Catto JW Abbod MF Linkens DA Herr A Artificial intelligence and bladder cancer arrays Verh Dtsch Ges Pathol 2007 91 308 319 18314629
  41. [45]Heeke S Delingette H Fanjat Y Long-Mira E Lassalle S The age of artificial intelligence in lung cancer pathology: between hope, gloom and perspectives Ann Pathol 2019 39 2 130 6 10.1016/j.annpat.2019.01.003 30772062 · DOI
  42. [46]Robertson S Azizpour H Smith K Hartman J Digital image analysis in breast pathology-from image processing techniques to artificial intelligence Transl Res 2018 194 19 35 10.1016/j.trsl.2017.10.010 29175265 · DOI
  43. [47]Steiner DF MacDonald R Liu Y Truszkowski P Hipp JD Impact of deep learning assistance on the Histopathologic review of lymph nodes for metastatic breast Cancer Am J Surg Pathol 2018 42 12 1636 1646 10.1097/PAS.0000000000001151 30312179 PMC6257102 · DOI · Paper page
  44. [48]Yala A Barzilay R Salama L Griffin M Sollender G Using machine learning to parse breast pathology reports Breast Cancer Res Treat 2017 161 2 203 211 10.1007/s10549-016-4035-1 27826755 · DOI
  45. [49]Bychkov D Linder N Turkki R Nordling S Kovanen PE Deep learning based tissue analysis predicts outcome in colorectal cancer Sci Rep 2018 8 1 3395 10.1038/s41598-018-21758-3 29467373 PMC5821847 · DOI · Paper page
  46. [50]Yoshida H Shimazu T Kiyuna T Marugame A Yamashita Y Automated histological classification of whole-slide images of gastric biopsy specimens Gastric Cancer 2018 21 2 249 257 10.1007/s10120-017-0731-8 28577229 · DOI
  47. [51]Qu J Hiruta N Terai K Nosato H Murakawa M Gastric pathology image classification using stepwise fine-tuning for deep neural networks J Healthc Eng 2018 2018 8961781 10.1155/2018/8961781 30034677 PMC6033298 · DOI
  48. [52]Liu Y Kohlberger T Norouzi M Dahl GE Smith JL Artificial intelligence-based breast Cancer nodal metastasis detection: insights into the black box for pathologists Arch Pathol Lab Med. 2019 143 7 859 868 10.5858/arpa.2018-0147-OA 30295070 · DOI
  49. [53]Litjens G, Bandi P, Bejnordi BE, Geessink O, Balkenhol M, et al. 1399 H&E-stained sentinel lymph node sections of breast cancer patients: the CAMELYON dataset. Gigascience. 2018;7(6). 10.1093/gigascience/giy065. PMC6007545 29860392 · DOI
  50. [54]Zhang L Wu Y Zheng B Su L Chen Y Rapid histology of laryngeal squamous cell carcinoma with deep-learning based stimulated Raman scattering microscopy Theranostics. 2019 9 9 2541 2554 10.7150/thno.32655 31131052 PMC6526002 · DOI
  51. [55]Yadav KK How AI Is optimizing the detection and Management of Prostate Cancer IEEE Pulse 2018 9 5 19 10.1109/MPUL.2018.2866354 30273137 · DOI
  52. [56]Kricka LJ History of disruptions in laboratory medicine: what have we learned from predictions? Clin Chem Lab Med 2019 57 3 308 311 10.1515/cclm-2018-0518 29927745 · DOI

Cited by

Sorted by citations

Authors
Ahmad, Z., Rahim, S., Zubair, M., & Abdul-Ghafar, J.
Affiliations
Aga Khan University Hospital (PK)
ORCID
Venue
Diagnostic Pathology
Publisher
Springer Science and Business Media LLC
Published
2021-03-17
Volume
16
Issue
1
Pages
24-24
ISSN
1746-1596 (ISSN-L)
DOI
10.1186/s13000-021-01085-4
PMC
PMC7971952
OpenAlex
W3136040801
Cited by
293
References
4
Citations by year
2026: 52 · 2025: 85 · 2024: 79 · 2023: 45 · 2022: 27 · 2021: 5
FWCI
3.17
Type
journal article
Language
en
Open access
Licence
Locations
  • Diagnostic Pathology (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • eCommons - AKU (Aga Khan University) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
Keywords
artificial intelligence in healthcare, surgical pathology, AI adoption barriers, cancer diagnosis, prognosis, personalized medicine, AI implementation costs
MeSH terms
Pathologists, Artificial Intelligence, Attitude of Health Personnel, Attitude to Computers, Cost-Benefit Analysis, Humans, Image Interpretation, Computer-Assisted, Health Knowledge, Attitudes, Practice, Microscopy, Pathology, Practice Patterns, Physicians', Predictive Value of Tests, Reproducibility of Results, Health Care Costs

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