Exploring the feasibility of an artificial intelligence based clinical decision support system for cutaneous melanoma detection in primary care – a mixed method study
Open accessAbstract
AI-based CDSS might play an important future role in cutaneous melanoma diagnostics, provided sufficient evidence of diagnostic accuracy and usability supporting its trustworthiness among the users.
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Ease of use; complex interfaces; and inconsistent performance, for example, due to false positives and negatives, add burden to the workflow, creating more work for clinicians, thereby impeding adoption.
“Ease of use; complex interfaces; and inconsistent performance, for example, due to false positives and negatives add burden to the workflow, creating more work for clinicians, thereby impeding adoption [42,52,57-59].”
From Barriers to and facilitators of artificial intelligence adoption in health care: scoping review (2024) · cites this paper as [58]
The Results sections on Ease of use and The importance of trust describe minor usability problems and say that trust in diagnostic guidance matters for adoption into the clinical workflow. The Discussion also links ease of use to time efficiency. The paper does not establish that complex interfaces or false positives and negatives created extra work for clinicians; respondents generally described this CDSS as easy to use and time-saving. (LLM, conf=0.86)
Experimental — accuracy not yet measuredFacilitators of trust include endorsement of the technology by experts and academically backed clinicians (including regulating bodies such as the government), evidence of output accuracy based on evaluation of AI, and positive opinions from trusted thought leaders in clinical fields.
“Facilitators of trust included the endorsement of the technology by experts as well as academically backed clinicians, including regulating bodies such as the government; evidence of output accuracy based on the evaluation of AI; and positive opinions from trusted thought leaders in the respective clinical fields [42,58,60].”
From Barriers to and facilitators of artificial intelligence adoption in health care: scoping review (2024) · cites this paper as [58]
The Results section, “Factors governing trust” (page unavailable), identifies clinical evidence of diagnostic accuracy, certification, and recommendations or positive opinions from dermatologists as factors that could increase trust. It does not identify government bodies or academically backed clinicians as endorsers, so that part of the claim is not supported by this source. (LLM, conf=0.85)
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- Authors
- Helenason, J., Ekström, C., Falk, M., & Papachristou, P.
- Affiliations
- AI Medical Technology, Stockholm, Sweden
- Linköping University (SE)
- Karolinska Institutet (SE)
- Corresponding author
- Papachristou, Panagiotis
- Venue
- Scandinavian Journal of Primary Health Care
- Publisher
- Informa UK Limited
- Published
- 2023-11-20
- Volume
- 42
- Issue
- 1
- Pages
- 51-60
- ISSN
- 0281-3432 (ISSN-L)
- 1502-7724
- PMC
- PMC10851794
- OpenAlex
- W4388834207
- Cited by
- 33
- References
- 41
- Citations by year
- 2026: 10 · 2025: 14 · 2024: 8 · 2023: 1
- FWCI
- 4.03
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Scandinavian Journal of Primary Health Care (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- KTH Publication Database DiVA (KTH Royal Institute of Technology) (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- artificial intelligence, clinical decision support systems, cutaneous melanoma, melanoma detection, primary care, diagnostic accuracy, usability, trustworthiness
- MeSH terms
- Artificial Intelligence, Feasibility Studies, Humans, Melanoma, Primary Health Care, Skin Neoplasms, Decision Support Systems, Clinical
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