DIGITAL PATHOLOGY AND AI: A PARADIGM SHIFT IN PATHOLOGY EDUCATION
Main Article Content
Keywords
Digital pathology, AI-enhanced medical education, telepathology, precision medicine, curriculum development
Abstract
From transferring microscope images to telepathology and whole-slide imaging scanners, digital pathology's evolution spans a century. Today, digital pathology and AI are revolutionizing medical education. With digital pathology, students can access a vast repository of virtual slides, enabling them to study diseases and conditions more comprehensively. AI enhances this by aiding in image analysis, diagnosis, and pattern recognition, providing students with valuable insights and preparing them for real-world challenges in medicine. Recent advancements strongly impact its adoption, influencing medical education. Digital pathology streamlines data storage, remote communication, and AI potential. Interactive AI-driven platforms offer tailored learning experiences, fostering critical thinking and are adaptable to each student's pace and need. Integrating digital pathology and AI transforms medical education, enabling comprehensive disease study through virtual slides. This fusion adequately prepares the physicians of tomorrow, enhancing practical expertise and pathology education. The future sees AI and digital pathology integral to medical education, equipping the future physicians to navigate evolving technology and deliver exceptional care. Embracing this duo ensures readiness for the dynamic healthcare landscape
References
1. Bashshur RL, Krupinski EA, Weinstein RS, Dunn MR, Bashshur N. The Empirical Foundations of Telepathology: Evidence of Feasibility and Intermediate Effects. Telemed J E Health. 2017;23(3):155-191. doi:10.1089/tmj.2016.0278
2. Weinstein RS, Graham AR, Richter LC, et al. Overview of telepathology, virtual microscopy, and whole slide imaging: prospects for the future. Hum Pathol. 2009;40(8):1057-1069. doi:10.1016/j.humpath.2009.04.006
3. Pantanowitz, L., Valenstein, P. N., Evans, A. J., Kaplan, K. J., Pfeifer, J. D., Wilbur, D. C., Collins, L. C., & Colgan, T. J. (2011). Review of the current state of whole slide imaging in pathology.
Journal of pathology informatics, 2, 36. https://doi.org/10.4103/2153-3539.83746
4. Hanna MG, Ardon O, Reuter VE, et al. Integrating digital pathology into clinical practice
[published correction appears in Mod Pathol. 2021 Oct 13;:] [published correction appears in Mod Pathol. 2021 Nov 9;:]. Mod Pathol. 2022;35(2):152-164. doi:10.1038/s41379-021-00929-0
5. Williams BJ, Bottoms D, Treanor D. Future-proofing pathology: the case for clinical adoption of digital pathology. J Clin Pathol. 2017;70(12):1010-1018. doi:10.1136/jclinpath-2017-204644
6. Romero Lauro G, Cable W, Lesniak A, et al. Digital pathology consultations-a new era in digital imaging, challenges and practical applications. J Digit Imaging. 2013;26(4):668-677. doi:10.1007/s10278-013-9572-0
7. Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks [published correction appears in Nature. 2017 Jun 28;546(7660):686]. Nature. 2017;542(7639):115-118. doi:10.1038/nature21056
8. Cruz-Roa A, Gilmore H, Basavanhally A, et al. Accurate and reproducible invasive breast cancer detection in whole-slide images: A Deep Learning approach for quantifying tumor extent. Sci Rep. 2017;7: 46450. Published 2017 Apr 18. doi:10.1038/srep46450
9. Campanella G, Hanna MG, Geneslaw L, et al. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nat Med. 2019;25(8):1301-1309. doi:10.1038/s41591-019-0508-1
10. Ehteshami Bejnordi B, Veta M, Johannes van Diest P, et al. Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer. JAMA. 2017;318(22):2199-2210. doi:10.1001/jama.2017.14585
11. Bhargava R, Madabhushi A. Emerging Themes in Image Informatics and Molecular Analysis for
Digital Pathology. Annu Rev Biomed Eng. 2016; 18:387-412. doi:10.1146/annurev-bioeng- 112415-114722
12. Yu KH, Zhang C, Berry GJ, et al. Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features. Nat Commun. 2016; 7:12474. Published 2016 Aug 16. doi:10.1038/ncomms12474
13. Rajkomar A, Dean J, Kohane I. Machine Learning in Medicine. N Engl J Med. 2019;380(14):1347-1358. doi:10.1056/NEJMra1814259
14. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44-56. doi:10.1038/s41591-018-0300-7
15. Obermeyer Z, Emanuel EJ. Predicting the Future - Big Data, Machine Learning, and Clinical Medicine. N Engl J Med. 2016;375(13):1216-1219. doi:10.1056/NEJMp1606181
16. Hassell LA, Absar SF, Chauhan C, et al. Pathology Education Powered by Virtual and Digital Transformation: Now and the Future. Arch Pathol Lab Med. 2023;147(4):474-491. doi:10.5858/arpa.2021-0473-RA
17. Aungst TD, Patel R. Integrating Digital Health into the Curriculum-Considerations on the Current Landscape and Future Developments. J Med Educ Curric Dev. 2020; 7:2382120519901275. Published 2020 Jan 20. doi:10.1177/2382120519901275
18. Hamilton PW, Wang Y, McCullough SJ. Virtual microscopy and digital pathology in training and education. APMIS. 2012;120(4):305-315. doi:10.1111/j.1600-0463.2011. 02869.x
19. Reeves S, Perrier L, Goldman J, Freeth D, Zwarenstein M. Interprofessional education: effects on professional practice and healthcare outcomes (update). Cochrane Database Syst Rev. 2013;2013(3):CD002213. Published 2013 Mar 28. doi:10.1002/14651858.CD002213.pub3
20. van Woezik TET, Oosterman JP, Reuzel RPB, van der Wilt GJ, Koksma JJ. Practice-based learning: an appropriate means to acquire the attitude and skills for evidence-based medicine. Int J Med Educ. 2020; 11:140-145. Published 2020 Jul 24. doi:10.5116/ijme.5ee0.ab48
21. McCoy LG, Nagaraj S, Morgado F, Harish V, Das S, Celi LA. What do medical students actually need to know about artificial intelligence?. NPJ Digit Med. 2020; 3:86. Published 2020 Jun 19. doi:10.1038/s41746-020-0294-7
22. Wartman SA, Combs CD. Medical Education Must Move From the Information Age to the Age of Artificial Intelligence. Acad Med. 2018;93(8):1107-1109. doi:10.1097/ACM.0000000000002044
23. Behrends M, Steffens S, Marschollek M. The Implementation of Medical Informatics in the National Competence Based Catalogue of Learning Objectives for Undergraduate Medical Education (NKLM). Stud Health Technol Inform. 2017; 243:18-22.
24. Machleid F, Kaczmarczyk R, Johann D, et al. Perceptions of Digital Health Education Among European Medical Students: Mixed Methods Survey. J Med Internet Res. 2020;22(8): e19827. Published 2020 Aug 14. doi:10.2196/19827
25. Walpole S, Taylor P, Banerjee A. Health informatics in UK Medical Education: an online survey of current practice. JRSM Open. 2016;8(1):2054270416682674. Published 2016 Dec 1.
doi:10.1177/2054270416682674
26. Edirippulige S, Brooks P, Carati C, et al. It's important, but not important enough: eHealth as a curriculum priority in medical education in Australia. J Telemed Telecare. 2018;24(10):697-702.
doi:10.1177/1357633X18793282
27. Samarasekera DD, Ooi S, Yeo SP, Hooi SC. Medical education in Singapore. Med Teach. 2015;37(8):707-713. doi:10.3109/0142159X.2015.1009026)
28. Stathonikos N, van Varsseveld NC, Vink A, et al. Digital pathology in the time of corona. J Clin Pathol. 2020;73(11):706-712. doi:10.1136/jclinpath-2020-206845
29. Cimadamore A, Lopez-Beltran A, Scarpelli M, Cheng L, Montironi R. Digital pathology and COVID-19 and future crises: pathologists can safely diagnose cases from home using a consumer monitor and a mini PC. J Clin Pathol. 2020;73(11):695-696. doi:10.1136/jclinpath-2020-206943
30. Browning L, Fryer E, Roskell D, et al. Role of digital pathology in diagnostic histopathology in the response to COVID-19: results from a survey of experience in a UK tertiary referral hospital. J Clin Pathol. 2021;74(2):129-132. doi:10.1136/jclinpath-2020-206786
31. Hanna MG, Reuter VE, Ardon O, et al. Validation of a digital pathology system including remote review during the COVID-19 pandemic. Mod Pathol. 2020;33(11):2115-2127. doi:10.1038/s41379-020-0601-5
32. Saxena, R., & Crum, R. (2020, June 26). Learning Digitally in a Pandemic. The Pathologist. Retrieved from https://thepathologist.com/outside-the-lab/learning-digitally-in-a-pandemic) 33. Huisman A. Digital pathology for education. Stud Health Technol Inform. 2012; 179:68-71.
34. Leifer Z. The use of virtual microscopy and a wiki in pathology education: Tracking student use, involvement, and response. J Pathol Inform. 2015; 6:30. Published 2015 Jun 3. doi:10.4103/2153- 3539.158063
35. Khatskevich K, Oh YS, Ruiz D, et al. Virtual Microscopy Tagging and Its Benefits for Students, Faculty, and Inter professional Programs Alike. Cureus. 2022;14(8): e27860. Published 2022 Aug 10. doi:10.7759/cureus.27860
36. Lee BC, Hsieh ST, Chang YL, et al. A Web-Based Virtual Microscopy Platform for Improving
Academic Performance in Histology and Pathology Laboratory Courses: A Pilot Study. Anat Sci Educ. 2020;13(6):743-758. doi:10.1002/ase.1940
37. Birkness-Gartman JE, White MJ, Salimian KJ, Voltaggio L. Web-based pathology modules with virtual slides are effective for teaching introductory gastrointestinal pathology concepts. Acad Pathol. 2022;9(1):100059. Published 2022 Nov 25. doi: 10.1016/j.acpath.2022.100059
38. Krupinski EA, Tillack AA, Richter L, et al. Eye-movement study and human performance using telepathology virtual slides: implications for medical education and differences with experience. Hum Pathol. 2006;37(12):1543-1556. doi: 10.1016/j.humpath.2006.08.024)
39. Cancer. US Department of Health and Services. Accessed October 21, 2021. Available from: https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga.
40. Cancer Imaging Archive. 2021. Frederick Nat. Lab for Cancer Research, USA. Available from:
www.cancerimagingarchive.net.
41. Digital Pathology Association. 2021. USA. Available from:
https://digitalpathologyassociation.org/whole-slide-imaging-repository
42. Azimi Khatibani SE, Tabatabai S. COVID-19 Impact on Modern Virtual Pathology Education:
Challenges and Opportunities. Iran J Pathol. 2021;16(4):439-443. doi:10.30699/IJP.20201.525144.2589
43. Meirelles AL, Kurc T, Saltz J, Teodoro G. Effective active learning in digital pathology: A case study in tumor infiltrating lymphocytes. Comput Methods Programs Biomed. 2022; 220:106828. doi:10.1016/j.cmpb.2022.106828
44. Kanthan R, Senger JL. The impact of specially designed digital games-based learning in undergraduate pathology and medical education. Arch Pathol Lab Med. 2011;135(1):135-142. doi:10.5858/2009-0698-OAR1.1
45. Ghaznavi F, Evans A, Madabhushi A, Feldman M. Digital imaging in pathology: whole-slide imaging and beyond. Annu Rev Pathol. 2013; 8:331-359. doi:10.1146/annurev-pathol-011811-
120902,
46. Clay MR, Fisher KE. Bioinformatics Education in Pathology Training: Current Scope and Future Direction. Cancer Inform. 2017; 16:1176935117703389. Published 2017 Apr 10. doi:10.1177/1176935117703389
47. Garcia CA, Baron JM, Beckwith BA, et al. Environmental components and methods for engaging pathology residents in informatics training. J Pathol Inform. 2015;6: 42. Published 2015 Jun 29.
48. Patel A, Balis UGJ, Cheng J, et al. Contemporary Whole Slide Imaging Devices and Their
Applications within the Modern Pathology Department: A Selected Hardware Review. J Pathol Inform. 2021; 12:50. Published 2021 Dec 9. doi:10.4103/jpi.jpi_66_21
49. Aeffner F, Zarella MD, Buchbinder N, et al. Introduction to Digital Image Analysis in Whole-slide Imaging: A White Paper from the Digital Pathology Association [published correction appears in J Pathol Inform. 2019 Apr 24; 10:15]. J Pathol Inform. 2019; 10:9. Published 2019 Mar 8. doi:10.4103/jpi.jpi_82_18
50. Fraggetta F, L'Imperio V, Ameisen D, et al. Best Practice Recommendations for the Implementation of a Digital Pathology Workflow in the Anatomic Pathology Laboratory by the European Society of Digital and Integrative Pathology (ESDIP). Diagnostics (Basel). 2021;11(11):2167. Published 2021 Nov 22. doi:10.3390/diagnostics11112167)
51. Litjens G, Kooi T, Bejnordi BE, et al. A survey on deep learning in medical image analysis. Med Image Anal. 2017;42: 60-88. doi:10.1016/j.media.2017.07.005
52. Giordano C, Brennan M, Mohamed B, Rashidi P, Modave F, Tighe P. Accessing Artificial
Intelligence for Clinical Decision-Making. Front Digit Health. 2021; 3:645232. Published 2021 Jun 25. doi:10.3389/fdgth.2021.645232
53. Reverberi C, Rigon T, Solari A, et al. Experimental evidence of effective human-AI collaboration in medical decision-making. Sci Rep. 2022;12(1):14952. Published 2022 Sep 2. doi:10.1038/s41598-022-18751-2
54. Char DS, Shah NH, Magnus D. Implementing Machine Learning in Health Care - Addressing Ethical Challenges. N Engl J Med. 2018;378(11):981-983. doi:10.1056/NEJMp1714229
55. Chen Y, Clayton EW, Novak LL, Anders S, Malin B. Human-Centered Design to Address Biases in Artificial Intelligence. J Med Internet Res. 2023;25:e43251. Published 2023 Mar 24. doi:10.2196/43251
2. Weinstein RS, Graham AR, Richter LC, et al. Overview of telepathology, virtual microscopy, and whole slide imaging: prospects for the future. Hum Pathol. 2009;40(8):1057-1069. doi:10.1016/j.humpath.2009.04.006
3. Pantanowitz, L., Valenstein, P. N., Evans, A. J., Kaplan, K. J., Pfeifer, J. D., Wilbur, D. C., Collins, L. C., & Colgan, T. J. (2011). Review of the current state of whole slide imaging in pathology.
Journal of pathology informatics, 2, 36. https://doi.org/10.4103/2153-3539.83746
4. Hanna MG, Ardon O, Reuter VE, et al. Integrating digital pathology into clinical practice
[published correction appears in Mod Pathol. 2021 Oct 13;:] [published correction appears in Mod Pathol. 2021 Nov 9;:]. Mod Pathol. 2022;35(2):152-164. doi:10.1038/s41379-021-00929-0
5. Williams BJ, Bottoms D, Treanor D. Future-proofing pathology: the case for clinical adoption of digital pathology. J Clin Pathol. 2017;70(12):1010-1018. doi:10.1136/jclinpath-2017-204644
6. Romero Lauro G, Cable W, Lesniak A, et al. Digital pathology consultations-a new era in digital imaging, challenges and practical applications. J Digit Imaging. 2013;26(4):668-677. doi:10.1007/s10278-013-9572-0
7. Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks [published correction appears in Nature. 2017 Jun 28;546(7660):686]. Nature. 2017;542(7639):115-118. doi:10.1038/nature21056
8. Cruz-Roa A, Gilmore H, Basavanhally A, et al. Accurate and reproducible invasive breast cancer detection in whole-slide images: A Deep Learning approach for quantifying tumor extent. Sci Rep. 2017;7: 46450. Published 2017 Apr 18. doi:10.1038/srep46450
9. Campanella G, Hanna MG, Geneslaw L, et al. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nat Med. 2019;25(8):1301-1309. doi:10.1038/s41591-019-0508-1
10. Ehteshami Bejnordi B, Veta M, Johannes van Diest P, et al. Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer. JAMA. 2017;318(22):2199-2210. doi:10.1001/jama.2017.14585
11. Bhargava R, Madabhushi A. Emerging Themes in Image Informatics and Molecular Analysis for
Digital Pathology. Annu Rev Biomed Eng. 2016; 18:387-412. doi:10.1146/annurev-bioeng- 112415-114722
12. Yu KH, Zhang C, Berry GJ, et al. Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features. Nat Commun. 2016; 7:12474. Published 2016 Aug 16. doi:10.1038/ncomms12474
13. Rajkomar A, Dean J, Kohane I. Machine Learning in Medicine. N Engl J Med. 2019;380(14):1347-1358. doi:10.1056/NEJMra1814259
14. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44-56. doi:10.1038/s41591-018-0300-7
15. Obermeyer Z, Emanuel EJ. Predicting the Future - Big Data, Machine Learning, and Clinical Medicine. N Engl J Med. 2016;375(13):1216-1219. doi:10.1056/NEJMp1606181
16. Hassell LA, Absar SF, Chauhan C, et al. Pathology Education Powered by Virtual and Digital Transformation: Now and the Future. Arch Pathol Lab Med. 2023;147(4):474-491. doi:10.5858/arpa.2021-0473-RA
17. Aungst TD, Patel R. Integrating Digital Health into the Curriculum-Considerations on the Current Landscape and Future Developments. J Med Educ Curric Dev. 2020; 7:2382120519901275. Published 2020 Jan 20. doi:10.1177/2382120519901275
18. Hamilton PW, Wang Y, McCullough SJ. Virtual microscopy and digital pathology in training and education. APMIS. 2012;120(4):305-315. doi:10.1111/j.1600-0463.2011. 02869.x
19. Reeves S, Perrier L, Goldman J, Freeth D, Zwarenstein M. Interprofessional education: effects on professional practice and healthcare outcomes (update). Cochrane Database Syst Rev. 2013;2013(3):CD002213. Published 2013 Mar 28. doi:10.1002/14651858.CD002213.pub3
20. van Woezik TET, Oosterman JP, Reuzel RPB, van der Wilt GJ, Koksma JJ. Practice-based learning: an appropriate means to acquire the attitude and skills for evidence-based medicine. Int J Med Educ. 2020; 11:140-145. Published 2020 Jul 24. doi:10.5116/ijme.5ee0.ab48
21. McCoy LG, Nagaraj S, Morgado F, Harish V, Das S, Celi LA. What do medical students actually need to know about artificial intelligence?. NPJ Digit Med. 2020; 3:86. Published 2020 Jun 19. doi:10.1038/s41746-020-0294-7
22. Wartman SA, Combs CD. Medical Education Must Move From the Information Age to the Age of Artificial Intelligence. Acad Med. 2018;93(8):1107-1109. doi:10.1097/ACM.0000000000002044
23. Behrends M, Steffens S, Marschollek M. The Implementation of Medical Informatics in the National Competence Based Catalogue of Learning Objectives for Undergraduate Medical Education (NKLM). Stud Health Technol Inform. 2017; 243:18-22.
24. Machleid F, Kaczmarczyk R, Johann D, et al. Perceptions of Digital Health Education Among European Medical Students: Mixed Methods Survey. J Med Internet Res. 2020;22(8): e19827. Published 2020 Aug 14. doi:10.2196/19827
25. Walpole S, Taylor P, Banerjee A. Health informatics in UK Medical Education: an online survey of current practice. JRSM Open. 2016;8(1):2054270416682674. Published 2016 Dec 1.
doi:10.1177/2054270416682674
26. Edirippulige S, Brooks P, Carati C, et al. It's important, but not important enough: eHealth as a curriculum priority in medical education in Australia. J Telemed Telecare. 2018;24(10):697-702.
doi:10.1177/1357633X18793282
27. Samarasekera DD, Ooi S, Yeo SP, Hooi SC. Medical education in Singapore. Med Teach. 2015;37(8):707-713. doi:10.3109/0142159X.2015.1009026)
28. Stathonikos N, van Varsseveld NC, Vink A, et al. Digital pathology in the time of corona. J Clin Pathol. 2020;73(11):706-712. doi:10.1136/jclinpath-2020-206845
29. Cimadamore A, Lopez-Beltran A, Scarpelli M, Cheng L, Montironi R. Digital pathology and COVID-19 and future crises: pathologists can safely diagnose cases from home using a consumer monitor and a mini PC. J Clin Pathol. 2020;73(11):695-696. doi:10.1136/jclinpath-2020-206943
30. Browning L, Fryer E, Roskell D, et al. Role of digital pathology in diagnostic histopathology in the response to COVID-19: results from a survey of experience in a UK tertiary referral hospital. J Clin Pathol. 2021;74(2):129-132. doi:10.1136/jclinpath-2020-206786
31. Hanna MG, Reuter VE, Ardon O, et al. Validation of a digital pathology system including remote review during the COVID-19 pandemic. Mod Pathol. 2020;33(11):2115-2127. doi:10.1038/s41379-020-0601-5
32. Saxena, R., & Crum, R. (2020, June 26). Learning Digitally in a Pandemic. The Pathologist. Retrieved from https://thepathologist.com/outside-the-lab/learning-digitally-in-a-pandemic) 33. Huisman A. Digital pathology for education. Stud Health Technol Inform. 2012; 179:68-71.
34. Leifer Z. The use of virtual microscopy and a wiki in pathology education: Tracking student use, involvement, and response. J Pathol Inform. 2015; 6:30. Published 2015 Jun 3. doi:10.4103/2153- 3539.158063
35. Khatskevich K, Oh YS, Ruiz D, et al. Virtual Microscopy Tagging and Its Benefits for Students, Faculty, and Inter professional Programs Alike. Cureus. 2022;14(8): e27860. Published 2022 Aug 10. doi:10.7759/cureus.27860
36. Lee BC, Hsieh ST, Chang YL, et al. A Web-Based Virtual Microscopy Platform for Improving
Academic Performance in Histology and Pathology Laboratory Courses: A Pilot Study. Anat Sci Educ. 2020;13(6):743-758. doi:10.1002/ase.1940
37. Birkness-Gartman JE, White MJ, Salimian KJ, Voltaggio L. Web-based pathology modules with virtual slides are effective for teaching introductory gastrointestinal pathology concepts. Acad Pathol. 2022;9(1):100059. Published 2022 Nov 25. doi: 10.1016/j.acpath.2022.100059
38. Krupinski EA, Tillack AA, Richter L, et al. Eye-movement study and human performance using telepathology virtual slides: implications for medical education and differences with experience. Hum Pathol. 2006;37(12):1543-1556. doi: 10.1016/j.humpath.2006.08.024)
39. Cancer. US Department of Health and Services. Accessed October 21, 2021. Available from: https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga.
40. Cancer Imaging Archive. 2021. Frederick Nat. Lab for Cancer Research, USA. Available from:
www.cancerimagingarchive.net.
41. Digital Pathology Association. 2021. USA. Available from:
https://digitalpathologyassociation.org/whole-slide-imaging-repository
42. Azimi Khatibani SE, Tabatabai S. COVID-19 Impact on Modern Virtual Pathology Education:
Challenges and Opportunities. Iran J Pathol. 2021;16(4):439-443. doi:10.30699/IJP.20201.525144.2589
43. Meirelles AL, Kurc T, Saltz J, Teodoro G. Effective active learning in digital pathology: A case study in tumor infiltrating lymphocytes. Comput Methods Programs Biomed. 2022; 220:106828. doi:10.1016/j.cmpb.2022.106828
44. Kanthan R, Senger JL. The impact of specially designed digital games-based learning in undergraduate pathology and medical education. Arch Pathol Lab Med. 2011;135(1):135-142. doi:10.5858/2009-0698-OAR1.1
45. Ghaznavi F, Evans A, Madabhushi A, Feldman M. Digital imaging in pathology: whole-slide imaging and beyond. Annu Rev Pathol. 2013; 8:331-359. doi:10.1146/annurev-pathol-011811-
120902,
46. Clay MR, Fisher KE. Bioinformatics Education in Pathology Training: Current Scope and Future Direction. Cancer Inform. 2017; 16:1176935117703389. Published 2017 Apr 10. doi:10.1177/1176935117703389
47. Garcia CA, Baron JM, Beckwith BA, et al. Environmental components and methods for engaging pathology residents in informatics training. J Pathol Inform. 2015;6: 42. Published 2015 Jun 29.
48. Patel A, Balis UGJ, Cheng J, et al. Contemporary Whole Slide Imaging Devices and Their
Applications within the Modern Pathology Department: A Selected Hardware Review. J Pathol Inform. 2021; 12:50. Published 2021 Dec 9. doi:10.4103/jpi.jpi_66_21
49. Aeffner F, Zarella MD, Buchbinder N, et al. Introduction to Digital Image Analysis in Whole-slide Imaging: A White Paper from the Digital Pathology Association [published correction appears in J Pathol Inform. 2019 Apr 24; 10:15]. J Pathol Inform. 2019; 10:9. Published 2019 Mar 8. doi:10.4103/jpi.jpi_82_18
50. Fraggetta F, L'Imperio V, Ameisen D, et al. Best Practice Recommendations for the Implementation of a Digital Pathology Workflow in the Anatomic Pathology Laboratory by the European Society of Digital and Integrative Pathology (ESDIP). Diagnostics (Basel). 2021;11(11):2167. Published 2021 Nov 22. doi:10.3390/diagnostics11112167)
51. Litjens G, Kooi T, Bejnordi BE, et al. A survey on deep learning in medical image analysis. Med Image Anal. 2017;42: 60-88. doi:10.1016/j.media.2017.07.005
52. Giordano C, Brennan M, Mohamed B, Rashidi P, Modave F, Tighe P. Accessing Artificial
Intelligence for Clinical Decision-Making. Front Digit Health. 2021; 3:645232. Published 2021 Jun 25. doi:10.3389/fdgth.2021.645232
53. Reverberi C, Rigon T, Solari A, et al. Experimental evidence of effective human-AI collaboration in medical decision-making. Sci Rep. 2022;12(1):14952. Published 2022 Sep 2. doi:10.1038/s41598-022-18751-2
54. Char DS, Shah NH, Magnus D. Implementing Machine Learning in Health Care - Addressing Ethical Challenges. N Engl J Med. 2018;378(11):981-983. doi:10.1056/NEJMp1714229
55. Chen Y, Clayton EW, Novak LL, Anders S, Malin B. Human-Centered Design to Address Biases in Artificial Intelligence. J Med Internet Res. 2023;25:e43251. Published 2023 Mar 24. doi:10.2196/43251
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May 14, 2023
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How to Cite
Ritcha Saxena, Kevin Carnewale, & Kapil Sharma. (2023). DIGITAL PATHOLOGY AND AI: A PARADIGM SHIFT IN PATHOLOGY EDUCATION . Journal of Population Therapeutics and Clinical Pharmacology, 30(9), 454–462. https://doi.org/10.53555/jptcp.v30i4.2672
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Author Biographies
Ritcha Saxena
Department of Biomedical Sciences, University of Minnesota School of Medicine, 1035 University Dr, Duluth, MN 55812, United States
Kevin Carnewale
Department of Pathology, Des Moines College of Osteopathic Medicine, Des Moines University, Iowa, USA.
Kapil Sharma
Department of Information Technology, Delhi Technological University, New Delhi, India