Adoption of Machine Learning Systems for Medical Diagnostics in Clinics: Qualitative Interview Study
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Abstract
Background: Recently, machine learning (ML) has been transforming our daily lives by enabling intelligent voice assistants, personalized support for purchase decisions, and efficient credit card fraud detection. In addition to its everyday applications, ML holds the potential to improve medicine as well, especially with regard to diagnostics in clinics. In a world characterized by population growth, demographic change, and the global COVID-19 pandemic, ML systems offer the opportunity to make diagnostics more effective and efficient, leading to a high interest of clinics in such systems. However, despite the high potential of ML, only a few ML systems have been deployed in clinics yet, as their adoption process differs significantly from the integration of prior health information technologies given the specific characteristics of ML. Objective: This study aims to explore the factors that influence the adoption process of ML systems for medical diagnostics in clinics to foster the adoption of these systems in clinics. Furthermore, this study provides insight into how these factors can be used to determine the ML maturity score of clinics, which can be applied by practitioners to measure the clinic status quo in the adoption process of ML systems. Methods: To gain more insight into the adoption process of ML systems for medical diagnostics in clinics, we conducted a qualitative study by interviewing 22 selected medical experts from clinics and their suppliers with profound knowledge in the field of ML. We used a semistructured interview guideline, asked open-ended questions, and transcribed the interviews verbatim. To analyze the transcripts, we first used a content analysis approach based on the health care–specific framework of nonadoption, abandonment, scale-up, spread, and sustainability. Then, we drew on the results of the content analysis to create a maturity model for ML adoption in clinics according to an established development process. Results: With the help of the interviews, we were able to identify 13 ML-specific factors that influence the adoption process of ML systems in clinics. We categorized these factors according to 7 domains that form a holistic ML adoption framework for clinics. In addition, we created an applicable maturity model that could help practitioners assess their current state in the ML adoption process. Conclusions: Many clinics still face major problems in adopting ML systems for medical diagnostics; thus, they do not benefit from the potential of these systems. Therefore, both the ML adoption framework and the maturity model for ML systems in clinics can not only guide future research that seeks to explore the promises and challenges associated with ML systems in a medical setting but also be a practical reference point for clinicians.
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The shortcomings of the NASSS framework mentioned by the two studies included ethical principles.
“Two studies in this review mentioned the shortcomings of the NASSS framework [62,67], including ethical principles, which were addressed in the Planning and Evaluating Remote Consultation Services framework in 2021 [94].”
From The NASSS (Non-Adoption, Abandonment, Scale-Up, Spread and Sustainability) framework use over time: A scoping review (2025) · cites this paper as [67]
Two studies in this review mentioned the shortcomings of the NASSS framework.
“Two studies in this review mentioned the shortcomings of the NASSS framework [62,67], including ethical principles, which were addressed in the Planning and Evaluating Remote Consultation Services framework in 2021 [94].”
From The NASSS (Non-Adoption, Abandonment, Scale-Up, Spread and Sustainability) framework use over time: A scoping review (2025) · cites this paper as [67]
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- Authors
- Pumplun, L., Fecho, M., Wahl, N., Peters, F., & Buxmann, P.
- Affiliations
- Technische Universität Darmstadt (DE)
- Corresponding author
- Pumplun, Luisa
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2021-10-15
- Volume
- 23
- Issue
- 10
- Pages
- e29301
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC8556641
- OpenAlex
- W3207870808
- Cited by
- 57
- References
- 68
- Citations by year
- 2026: 13 · 2025: 16 · 2024: 17 · 2023: 6 · 2022: 5
- FWCI
- 2.02
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- TUbilio (Technical University of Darmstadt) (submittedVersion)
- Funders
- Bundesministerium für Bildung und Forschung
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- machine learning adoption, medical diagnostics, clinics, technology adoption, maturity model, adoption framework, abandonment, scale-up, spreads, sustainability, health information technology, medical experts, adoption factors
- MeSH terms
- Machine Learning, COVID-19, SARS-CoV-2, Humans, Qualitative Research, Pandemics
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