Artificial intelligence-based clinical decision support in modern medical physics: Selection, acceptance, commissioning, and quality assurance
Open accessAbstract
BACKGROUND: Recent advances in machine and deep learning based on an increased availability of clinical data have fueled renewed interest in computerized clinical decision support systems (CDSSs). CDSSs have shown great potential to improve healthcare, increase patient safety and reduce costs. However, the use of CDSSs is not without pitfalls, as an inadequate or faulty CDSS can potentially deteriorate the quality of healthcare and put patients at risk. In addition, the adoption of a CDSS might fail because its intended users ignore the output of the CDSS due to lack of trust, relevancy or actionability. AIM: In this article, we provide guidance based on literature for the different aspects involved in the adoption of a CDSS with a special focus on machine and deep learning based systems: selection, acceptance testing, commissioning, implementation and quality assurance. RESULTS: A rigorous selection process will help identify the CDSS that best fits the preferences and requirements of the local site. Acceptance testing will make sure that the selected CDSS fulfills the defined specifications and satisfies the safety requirements. The commissioning process will prepare the CDSS for safe clinical use at the local site. An effective implementation phase should result in an orderly roll out of the CDSS to the well-trained end-users whose expectations have been managed. And finally, quality assurance will make sure that the performance of the CDSS is maintained and that any issues are promptly identified and solved. CONCLUSION: We conclude that a systematic approach to the adoption of a CDSS will help avoid pitfalls, improve patient safety and increase the chances of success.
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Explainability of the support system can improve the acceptability of CDSS by clinicians, increase the chances of the complex AI systems' adoption and clinical feasibility of a novel CDSS.
“Therefore, explainability of the support system can improve the acceptability of CDSS by clinicians [48] increase the chances of the complex AI systems' adoption and clinical feasibility of a novel CDSS [105,121].”
From Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review (2021) · cites this paper as [105]
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- Authors
- Mahadevaiah, G., RV, P., Bermejo, I., Jaffray, D., Dekker, A., & Wee, L.
- Affiliations
- Philips Research India Bangalore 560045 India
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology Maastricht University Medical Centre Maastricht 6229 ET Netherlands
- University Health Network (CA)
- Maastricht University Medical Centre (NL)
- Venue
- Medical Physics
- Publisher
- Wiley
- Published
- 2020-05
- Volume
- 47
- Issue
- 5
- Pages
- e228-e235
- ISSN
- 0094-2405 (ISSN-L)
- 2473-4209
- PMC
- PMC7318221
- OpenAlex
- W3024513077
- Cited by
- 114
- References
- 63
- Citations by year
- 2026: 8 · 2025: 17 · 2024: 20 · 2023: 26 · 2022: 14 · 2021: 25 · 2020: 4
- FWCI
- 1.31
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Medical Physics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Research Publications (Maastricht University) (submittedVersion, cc-by)
- Research Publications (Maastricht University) (submittedVersion, other-oa)
- Funders
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek
- 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 › Medicine › Public Health, Environmental and Occupational Health › Technology and Human Factors in Education and Health
- Keywords
- clinical decision support systems, artificial intelligence, deep learning, machine learning, acceptance testing, commissioning, implementation, quality assurance, patient safety, trust in AI
- MeSH terms
- Artificial Intelligence, Humans, Physics, Quality Assurance, Health Care, Decision Support Systems, Clinical
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