Digital Pathology Implementation in Cancer Diagnostics: Towards Informed Decision-making

Overview

Journal Front Digit Health

Specialty Medical Informatics

Date 2024 Jun 14

PMID 38873358

Authors

Oksana Sulaieva

Oleksandr Dudin

Olena Koshyk

Mariia Panko

Nazarii Kobyliak

Affiliations

Soon will be listed here.

Abstract

Digital pathology (DP) has become a part of the cancer healthcare system, creating additional value for cancer patients. DP implementation in clinical practice provides plenty of benefits but also harbors hidden ethical challenges affecting physician-patient relationships. This paper addresses the ethical obligation to transform the physician-patient relationship for informed and responsible decision-making when using artificial intelligence (AI)-based tools for cancer diagnostics. DP application allows to improve the performance of the Human-AI Team shifting focus from AI challenges towards the Augmented Human Intelligence (AHI) benefits. AHI enhances analytical sensitivity and empowers pathologists to deliver accurate diagnoses and assess predictive biomarkers for further personalized treatment of cancer patients. At the same time, patients' right to know about using AI tools, their accuracy, strengths and limitations, measures for privacy protection, acceptance of privacy concerns and legal protection defines the duty of physicians to provide the relevant information about AHI-based solutions to patients and the community for building transparency, understanding and trust, respecting patients' autonomy and empowering informed decision-making in oncology.

References

Redrup Hill E, Mitchell C, Brigden T, Hall A . Ethical and legal considerations influencing human involvement in the implementation of artificial intelligence in a clinical pathway: A multi-stakeholder perspective. Front Digit Health. 2023; 5:1139210. PMC: 10043985. DOI: 10.3389/fdgth.2023.1139210. View

Tizhoosh H, Pantanowitz L . Artificial Intelligence and Digital Pathology: Challenges and Opportunities. J Pathol Inform. 2019; 9:38. PMC: 6289004. DOI: 10.4103/jpi.jpi_53_18. View

McGenity C, Treanor D . Guidelines for clinical trials using artificial intelligence - SPIRIT-AI and CONSORT-AI. J Pathol. 2020; 253(1):14-16. DOI: 10.1002/path.5565. View

Jiang Y, Yang M, Wang S, Li X, Sun Y . Emerging role of deep learning-based artificial intelligence in tumor pathology. Cancer Commun (Lond). 2020; 40(4):154-166. PMC: 7170661. DOI: 10.1002/cac2.12012. View

Char D, Shah N, Magnus D . Implementing Machine Learning in Health Care - Addressing Ethical Challenges. N Engl J Med. 2018; 378(11):981-983. PMC: 5962261. DOI: 10.1056/NEJMp1714229. View

Cheng X, Lin X, Shen X, Zarifis A, Mou J . The dark sides of AI. Electron Mark. 2022; 32(1):11-15. PMC: 8862697. DOI: 10.1007/s12525-022-00531-5. View

Mohlman J, Leventhal S, Hansen T, Kohan J, Pascucci V, Salama M . Improving Augmented Human Intelligence to Distinguish Burkitt Lymphoma From Diffuse Large B-Cell Lymphoma Cases. Am J Clin Pathol. 2020; 153(6):743-759. DOI: 10.1093/ajcp/aqaa001. View

Jarrahi M, Davoudi V, Haeri M . The key to an effective AI-powered digital pathology: Establishing a symbiotic workflow between pathologists and machine. J Pathol Inform. 2023; 13:100156. PMC: 9808012. DOI: 10.1016/j.jpi.2022.100156. View

Betmouni S . Diagnostic digital pathology implementation: Learning from the digital health experience. Digit Health. 2021; 7:20552076211020240. PMC: 8216403. DOI: 10.1177/20552076211020240. View

10.

Rashidi H, Tran N, Betts E, Howell L, Green R . Artificial Intelligence and Machine Learning in Pathology: The Present Landscape of Supervised Methods. Acad Pathol. 2019; 6:2374289519873088. PMC: 6727099. DOI: 10.1177/2374289519873088. View

11.

Beauchamp T, Childress J . : Marking Its Fortieth Anniversary. Am J Bioeth. 2019; 19(11):9-12. DOI: 10.1080/15265161.2019.1665402. View

12.

Garcia-Rojo M, De Mena D, Muriel-Cueto P, Atienza-Cuevas L, Dominguez-Gomez M, Bueno G . New European Union Regulations Related to Whole Slide Image Scanners and Image Analysis Software. J Pathol Inform. 2019; 10:2. PMC: 6369630. DOI: 10.4103/jpi.jpi_33_18. View

13.

Morley J, Machado C, Burr C, Cowls J, Joshi I, Taddeo M . The ethics of AI in health care: A mapping review. Soc Sci Med. 2020; 260:113172. DOI: 10.1016/j.socscimed.2020.113172. View

14.

Thorstenson S, Molin J, Lundstrom C . Implementation of large-scale routine diagnostics using whole slide imaging in Sweden: Digital pathology experiences 2006-2013. J Pathol Inform. 2014; 5(1):14. PMC: 4023034. DOI: 10.4103/2153-3539.129452. View

15.

Kiran N, Sapna F, Kiran F, Kumar D, Raja F, Shiwlani S . Digital Pathology: Transforming Diagnosis in the Digital Age. Cureus. 2023; 15(9):e44620. PMC: 10547926. DOI: 10.7759/cureus.44620. View

16.

Sulaieva O, Falalyeyeva T, Kobyliak N, Pellicano R, Dudin O . Precision oncology: ethical challenges and justification. Minerva Med. 2022; 113(4):603-605. DOI: 10.23736/S0026-4806.22.08063-6. View

17.

Kelly C, Karthikesalingam A, Suleyman M, Corrado G, King D . Key challenges for delivering clinical impact with artificial intelligence. BMC Med. 2019; 17(1):195. PMC: 6821018. DOI: 10.1186/s12916-019-1426-2. View

18.

Pell R, Oien K, Robinson M, Pitman H, Rajpoot N, Rittscher J . The use of digital pathology and image analysis in clinical trials. J Pathol Clin Res. 2019; 5(2):81-90. PMC: 6463857. DOI: 10.1002/cjp2.127. View

19.

Kearney S, Lowe A, Lennerz J, Parwani A, Bui M, Wack K . Bridging the Gap: The Critical Role of Regulatory Affairs and Clinical Affairs in the Total Product Life Cycle of Pathology Imaging Devices and Software. Front Med (Lausanne). 2021; 8:765385. PMC: 8635712. DOI: 10.3389/fmed.2021.765385. View

20.

Ahmed Z, Mohamed K, Zeeshan S, Dong X . Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine. Database (Oxford). 2020; 2020. PMC: 7078068. DOI: 10.1093/database/baaa010. View