“I Wonder if my Years of Training and Expertise Will be Devalued by Machines”: Concerns About the Replacement of Medical Professionals by Artificial Intelligence

Rony, Moustaq Karim Khan; Parvin, Mst. Rina; Wahiduzzaman, Md; Debnath, Mitun; Bala, Shuvashish Das; Kayesh, Ibne

doi:10.1177/23779608241245220

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“I Wonder if my Years of Training and Expertise Will be Devalued by Machines”: Concerns About the Replacement of Medical Professionals by Artificial Intelligence

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Author(s): Moustaq Karim Khan Rony ¹ ^, ² ^, , Mst. Rina Parvin ³ , Md. Wahiduzzaman ⁴ , Mitun Debnath ⁵ , Shuvashish Das Bala ⁶ , Ibne Kayesh ² ^, ⁷

Publication date (Electronic): 7 April 2024

Journal: SAGE Open Nursing

Publisher: SAGE Publications

Keywords: artificial intelligence, healthcare workers, technologies, patient outcomes

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Abstract

Background

The rapid integration of artificial intelligence (AI) into healthcare has raised concerns among healthcare professionals about the potential displacement of human medical professionals by AI technologies. However, the apprehensions and perspectives of healthcare workers regarding the potential substitution of them with AI are unknown.

Objective

This qualitative research aimed to investigate healthcare workers’ concerns about artificial intelligence replacing medical professionals.

Methods

A descriptive and exploratory research design was employed, drawing upon the Technology Acceptance Model (TAM), Technology Threat Avoidance Theory, and Sociotechnical Systems Theory as theoretical frameworks. Participants were purposively sampled from various healthcare settings, representing a diverse range of roles and backgrounds. Data were collected through individual interviews and focus group discussions, followed by thematic analysis.

Results

The analysis revealed seven key themes reflecting healthcare workers’ concerns, including job security and economic concerns; trust and acceptance of AI; ethical and moral dilemmas; quality of patient care; workforce role redefinition and training; patient–provider relationships; healthcare policy and regulation.

Conclusions

This research underscores the multifaceted concerns of healthcare workers regarding the increasing role of AI in healthcare. Addressing job security, fostering trust, addressing ethical dilemmas, and redefining workforce roles are crucial factors to consider in the successful integration of AI into healthcare. Healthcare policy and regulation must be developed to guide this transformation while maintaining the quality of patient care and preserving patient–provider relationships. The study findings offer insights for policymakers and healthcare institutions to navigate the evolving landscape of AI in healthcare while addressing the concerns of healthcare professionals.

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Most cited references 69

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AI in health and medicine

Pranav Rajpurkar, Emma Chen, Oishi Banerjee … (2022)

Artificial intelligence (AI) is poised to broadly reshape medicine, potentially improving the experiences of both clinicians and patients. We discuss key findings from a 2-year weekly effort to track and share key developments in medical AI. We cover prospective studies and advances in medical image analysis, which have reduced the gap between research and deployment. We also address several promising avenues for novel medical AI research, including non-image data sources, unconventional problem formulations and human-AI collaboration. Finally, we consider serious technical and ethical challenges in issues spanning from data scarcity to racial bias. As these challenges are addressed, AI's potential may be realized, making healthcare more accurate, efficient and accessible for patients worldwide.

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The technology acceptance model: its past and its future in health care.

Richard J Holden, Ben-Tzion Karsh (2010)

Increasing interest in end users' reactions to health information technology (IT) has elevated the importance of theories that predict and explain health IT acceptance and use. This paper reviews the application of one such theory, the Technology Acceptance Model (TAM), to health care. We reviewed 16 data sets analyzed in over 20 studies of clinicians using health IT for patient care. Studies differed greatly in samples and settings, health ITs studied, research models, relationships tested, and construct operationalization. Certain TAM relationships were consistently found to be significant, whereas others were inconsistent. Several key relationships were infrequently assessed. Findings show that TAM predicts a substantial portion of the use or acceptance of health IT, but that the theory may benefit from several additions and modifications. Aside from improved study quality, standardization, and theoretically motivated additions to the model, an important future direction for TAM is to adapt the model specifically to the health care context, using beliefs elicitation methods.

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Revolutionizing healthcare: the role of artificial intelligence in clinical practice

Shuroug A. Alowais, Sahar S. Alghamdi, Nada Alsuhebany … (2023)

Introduction Healthcare systems are complex and challenging for all stakeholders, but artificial intelligence (AI) has transformed various fields, including healthcare, with the potential to improve patient care and quality of life. Rapid AI advancements can revolutionize healthcare by integrating it into clinical practice. Reporting AI’s role in clinical practice is crucial for successful implementation by equipping healthcare providers with essential knowledge and tools. Research Significance This review article provides a comprehensive and up-to-date overview of the current state of AI in clinical practice, including its potential applications in disease diagnosis, treatment recommendations, and patient engagement. It also discusses the associated challenges, covering ethical and legal considerations and the need for human expertise. By doing so, it enhances understanding of AI’s significance in healthcare and supports healthcare organizations in effectively adopting AI technologies. Materials and Methods The current investigation analyzed the use of AI in the healthcare system with a comprehensive review of relevant indexed literature, such as PubMed/Medline, Scopus, and EMBASE, with no time constraints but limited to articles published in English. The focused question explores the impact of applying AI in healthcare settings and the potential outcomes of this application. Results Integrating AI into healthcare holds excellent potential for improving disease diagnosis, treatment selection, and clinical laboratory testing. AI tools can leverage large datasets and identify patterns to surpass human performance in several healthcare aspects. AI offers increased accuracy, reduced costs, and time savings while minimizing human errors. It can revolutionize personalized medicine, optimize medication dosages, enhance population health management, establish guidelines, provide virtual health assistants, support mental health care, improve patient education, and influence patient-physician trust. Conclusion AI can be used to diagnose diseases, develop personalized treatment plans, and assist clinicians with decision-making. Rather than simply automating tasks, AI is about developing technologies that can enhance patient care across healthcare settings. However, challenges related to data privacy, bias, and the need for human expertise must be addressed for the responsible and effective implementation of AI in healthcare.

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Author and article information

Journal

Journal ID (nlm-ta): SAGE Open Nurs

Journal ID (iso-abbrev): SAGE Open Nurs

Journal ID (publisher-id): SON

Journal ID (hwp): spson

Title: SAGE Open Nursing

Publisher: SAGE Publications (Sage CA: Los Angeles, CA )

ISSN (Electronic): 2377-9608

Publication date (Electronic): 7 April 2024

Publication date Collection: Jan-Dec 2024

Volume: 10

Electronic Location Identifier: 23779608241245220

Affiliations

[1 ]Ringgold 421966, universityMaster of Public Health, Bangladesh Open University; , Gazipur, Bangladesh

[2 ]Institute of Social Welfare and Research, University of Dhaka, Dhaka, Bangladesh

[3 ]Armed Forces Nursing Service, Major at Bangladesh Army (AFNS Officer), Combined Military Hospital, Dhaka, Bangladesh

[4 ]Ringgold 113074, universitySchool of Medical Sciences, Shahjalal University of Science and Technology; , Sylhet, Bangladesh

[5 ]Ringgold 247328, universityMaster of Public Health, National Institute of Preventive and Social Medicine; , Dhaka, Bangladesh

[6 ]College of Nursing, Ringgold 421876, universityInternational University of Business Agriculture and Technology; , Dhaka, Bangladesh

[7 ]Ringgold 295580, universityFaculty of Graduate Studies, University of Kelaniya; , Colombo, Sri Lanka

Author notes

[*]Moustaq Karim Khan Rony, Master of Public Health, Bangladesh Open University, Gazipur, Bangladesh. Email: mkkrony@ 123456yahoo.com

Author information

Moustaq Karim Khan Rony https://orcid.org/0000-0002-6905-0554

Ibne Kayesh https://orcid.org/0000-0001-9261-949X

Article

Publisher ID: 10.1177_23779608241245220

DOI: 10.1177/23779608241245220

PMC ID: 11003342

PubMed ID: 38596508

SO-VID: f1deb4c5-a710-4a95-a272-f018d4d1758f

License:

This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License ( https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page ( https://us.sagepub.com/en-us/nam/open-access-at-sage).

History

Date received : 10 November 2023

Date revision received : 8 March 2024

Date accepted : 15 March 2024

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typesetter ts19

cover-date January-December 2024

Keywords: artificial intelligence,healthcare workers,technologies,patient outcomes

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Keywords: artificial intelligence, healthcare workers, technologies, patient outcomes

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“I Wonder if my Years of Training and Expertise Will be Devalued by Machines”: Concerns About the Replacement of Medical Professionals by Artificial Intelligence

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Abstract

Background

Objective

Methods

Results

Conclusions

Related collections

Artificial Intelligence in Medicine

Most cited references 69

AI in health and medicine

The technology acceptance model: its past and its future in health care.

Revolutionizing healthcare: the role of artificial intelligence in clinical practice

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