Literature DB >> 33731170

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.

Zubair Ahmad1, Shabina Rahim1, Maha Zubair1, Jamshid Abdul-Ghafar2.   

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.

Entities:  

Keywords:  Artificial intelligence; Medicine; Pathology

Mesh:

Year:  2021        PMID: 33731170      PMCID: PMC7971952          DOI: 10.1186/s13000-021-01085-4

Source DB:  PubMed          Journal:  Diagn Pathol        ISSN: 1746-1596            Impact factor:   2.644


  55 in total

1.  Framing the challenges of artificial intelligence in medicine.

Authors:  Kun-Hsing Yu; Isaac S Kohane
Journal:  BMJ Qual Saf       Date:  2018-10-05       Impact factor: 7.035

2.  History of disruptions in laboratory medicine: what have we learned from predictions?

Authors:  Larry J Kricka
Journal:  Clin Chem Lab Med       Date:  2019-02-25       Impact factor: 3.694

3.  Automated histological classification of whole-slide images of gastric biopsy specimens.

Authors:  Hiroshi Yoshida; Taichi Shimazu; Tomoharu Kiyuna; Atsushi Marugame; Yoshiko Yamashita; Eric Cosatto; Hirokazu Taniguchi; Shigeki Sekine; Atsushi Ochiai
Journal:  Gastric Cancer       Date:  2017-06-02       Impact factor: 7.370

Review 4.  [The age of artificial intelligence in lung cancer pathology: Between hope, gloom and perspectives].

Authors:  Simon Heeke; Hervé Delingette; Youta Fanjat; Elodie Long-Mira; Sandra Lassalle; Véronique Hofman; Jonathan Benzaquen; Charles-Hugo Marquette; Paul Hofman; Marius Ilié
Journal:  Ann Pathol       Date:  2019-02-13       Impact factor: 0.407

5.  Dermatologist-level classification of skin cancer with deep neural networks.

Authors:  Andre Esteva; Brett Kuprel; Roberto A Novoa; Justin Ko; Susan M Swetter; Helen M Blau; Sebastian Thrun
Journal:  Nature       Date:  2017-01-25       Impact factor: 49.962

6.  Artificial intelligence and bladder cancer arrays.

Authors:  P J Wild; J W F Catto; M F Abbod; D A Linkens; A Herr; C Pilarsky; C Wissmann; R Stoehr; S Denzinger; R Knuechel; F C Hamdy; A Hartmann
Journal:  Verh Dtsch Ges Pathol       Date:  2007

7.  [Digital pathology for the dermatologist].

Authors:  Olesya Pavlova; Michel Gilliet; Daniel Hohl
Journal:  Rev Med Suisse       Date:  2020-04-01

Review 8.  AI (artificial intelligence) in histopathology--from image analysis to automated diagnosis.

Authors:  Klaus Kayser; Jürgen Görtler; Milica Bogovac; Aleksandar Bogovac; Torsten Goldmann; Ekkehard Vollmer; Gian Kayser
Journal:  Folia Histochem Cytobiol       Date:  2009-01       Impact factor: 1.698

9.  Deep learning based tissue analysis predicts outcome in colorectal cancer.

Authors:  Dmitrii Bychkov; Nina Linder; Riku Turkki; Stig Nordling; Panu E Kovanen; Clare Verrill; Margarita Walliander; Mikael Lundin; Caj Haglund; Johan Lundin
Journal:  Sci Rep       Date:  2018-02-21       Impact factor: 4.379

10.  Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features.

Authors:  Kun-Hsing Yu; Ce Zhang; Gerald J Berry; Russ B Altman; Christopher Ré; Daniel L Rubin; Michael Snyder
Journal:  Nat Commun       Date:  2016-08-16       Impact factor: 14.919

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  8 in total

1.  Integrating Artificial Intelligence for Clinical and Laboratory Diagnosis - a Review.

Authors:  Taran Rishit Undru; Utkarsha Uday; Jyothi Tadi Lakshmi; Ariyanachi Kaliappan; Saranya Mallamgunta; Shalam Sheerin Nikhat; V Sakthivadivel; Archana Gaur
Journal:  Maedica (Bucur)       Date:  2022-06

2.  Artificial Intelligence-Assisted Score Analysis for Predicting the Expression of the Immunotherapy Biomarker PD-L1 in Lung Cancer.

Authors:  Guoping Cheng; Fuchuang Zhang; Yishi Xing; Xingyi Hu; He Zhang; Shiting Chen; Mengdao Li; Chaolong Peng; Guangtai Ding; Dadong Zhang; Peilin Chen; Qingxin Xia; Meijuan Wu
Journal:  Front Immunol       Date:  2022-07-01       Impact factor: 8.786

Review 3.  The state of the art for artificial intelligence in lung digital pathology.

Authors:  Vidya Sankar Viswanathan; Paula Toro; Germán Corredor; Sanjay Mukhopadhyay; Anant Madabhushi
Journal:  J Pathol       Date:  2022-06-20       Impact factor: 9.883

Review 4.  Implementation of Artificial Intelligence in Diagnostic Practice as a Next Step after Going Digital: The UMC Utrecht Perspective.

Authors:  Rachel N Flach; Nina L Fransen; Andreas F P Sonnen; Tri Q Nguyen; Gerben E Breimer; Mitko Veta; Nikolas Stathonikos; Carmen van Dooijeweert; Paul J van Diest
Journal:  Diagnostics (Basel)       Date:  2022-04-21

5.  Eyelid carcinomas: Tumor aggressiveness tendencies for smokers compared to non-smokers.

Authors:  Razvan Mercut; Irina Maria Mercut; Adina Dorina Glodeanu; Mihaela Ionescu; Adina Turcu; Alin Stefanescu-Dima; Marius Eugen Ciurea
Journal:  Exp Ther Med       Date:  2022-01-21       Impact factor: 2.447

Review 6.  Recent Application of Artificial Intelligence in Non-Gynecological Cancer Cytopathology: A Systematic Review.

Authors:  Nishant Thakur; Mohammad Rizwan Alam; Jamshid Abdul-Ghafar; Yosep Chong
Journal:  Cancers (Basel)       Date:  2022-07-20       Impact factor: 6.575

7.  Global Research Trends of Artificial Intelligence on Histopathological Images: A 20-Year Bibliometric Analysis.

Authors:  Wentong Zhou; Ziheng Deng; Yong Liu; Hui Shen; Hongwen Deng; Hongmei Xiao
Journal:  Int J Environ Res Public Health       Date:  2022-09-15       Impact factor: 4.614

8.  Automated quality assessment of large digitised histology cohorts by artificial intelligence.

Authors:  Maryam Haghighat; Lisa Browning; Korsuk Sirinukunwattana; Stefano Malacrino; Nasullah Khalid Alham; Richard Colling; Ying Cui; Emad Rakha; Freddie C Hamdy; Clare Verrill; Jens Rittscher
Journal:  Sci Rep       Date:  2022-03-23       Impact factor: 4.379

  8 in total

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