Academic Journal
Artificial Intelligence in Clinical Decision Support: Challenges for Evaluating AI and Practical Implications: A Position Paper from the IMIA Technology Assessment & Quality Development in Health Informatics Working Group and the EFMI Working Group for Assessment of Health Information Systems
| Title: | Artificial Intelligence in Clinical Decision Support: Challenges for Evaluating AI and Practical Implications: A Position Paper from the IMIA Technology Assessment & Quality Development in Health Informatics Working Group and the EFMI Working Group for Assessment of Health Information Systems |
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| Authors: | Magrabi, F, Ammenwerth, E, McNair, JB, De Keizer, NF, Hyppönen, H, Nykänen, P, Rigby, M, Scott, PJ, Vehko, T, Wong, ZS, Georgiou, A |
| Contributors: | Informaatioteknologian ja viestinnän tiedekunta - Faculty of Information Technology and Communication Sciences, Tampere University |
| Source: | Yearb Med Inform Magrabi, F, Ammenwerth, E, McNair, J B, De Keizer, N F, Hyppönen, H, Nykänen, P, Rigby, M, Scott, P J, Vehko, T, Wong, Z S-Y & Georgiou, A 2019, 'Artificial Intelligence in Clinical Decision Support : Challenges for Evaluating AI and Practical Implications', Yearbook of Medical Informatics, vol. 28, no. 1, pp. 128-134. https://doi.org/10.1055/s-0039-1677903 Magrabi, F, Ammenwerth, E, McNair, J B, De Keizer, N F, Hyppönen, H, Nykänen, P, Rigby, M, Scott, P J, Vehko, T, Wong, Z S-Y & Georgiou, A 2019, ' Artificial Intelligence in clinical decision support : challenges for evaluating AI and practical implications ', IMIA Yearbook of Medical Informatics . https://doi.org/10.1055/s-0039-1677903 |
| Publisher Information: | Georg Thieme Verlag KG, 2019. |
| Publication Year: | 2019 |
| Subject Terms: | clinical decision support, QA75, Artificial intelligence, program evaluation, 02 engineering and technology, Decision Support Systems, Clinical, 3. Good health, Machine Learning, Program Evaluation/methods, 03 medical and health sciences, machine learning, evaluation studies, 0302 clinical medicine, Artificial Intelligence, Evaluation Studies as Topic, 0202 electrical engineering, electronic engineering, information engineering, Tietojenkäsittely ja informaatiotieteet - Computer and information sciences, 10. No inequality, Program Evaluation |
| Description: | Objectives: This paper draws attention to: i) key considerations for evaluating artificial intelligence (AI) enabled clinical decision support; and ii) challenges and practical implications of AI design, development, selection, use, and ongoing surveillance. Method: A narrative review of existing research and evaluation approaches along with expert perspectives drawn from the International Medical Informatics Association (IMIA) Working Group on Technology Assessment and Quality Development in Health Informatics and the European Federation for Medical Informatics (EFMI) Working Group for Assessment of Health Information Systems. Results: There is a rich history and tradition of evaluating AI in healthcare. While evaluators can learn from past efforts, and build on best practice evaluation frameworks and methodologies, questions remain about how to evaluate the safety and effectiveness of AI that dynamically harness vast amounts of genomic, biomarker, phenotype, electronic record, and care delivery data from across health systems. This paper first provides a historical perspective about the evaluation of AI in healthcare. It then examines key challenges of evaluating AI-enabled clinical decision support during design, development, selection, use, and ongoing surveillance. Practical aspects of evaluating AI in healthcare, including approaches to evaluation and indicators to monitor AI are also discussed. Conclusion: Commitment to rigorous initial and ongoing evaluation will be critical to ensuring the safe and effective integration of AI in complex sociotechnical settings. Specific enhancements that are required for the new generation of AI-enabled clinical decision support will emerge through practical application. |
| Document Type: | Article Other literature type |
| File Description: | application/pdf; fulltext |
| Language: | English |
| ISSN: | 2364-0502 0943-4747 |
| DOI: | 10.1055/s-0039-1677903 |
| Access URL: | http://www.thieme-connect.de/products/ejournals/pdf/10.1055/s-0039-1677903.pdf https://pubmed.ncbi.nlm.nih.gov/31022752 https://eprints.keele.ac.uk/6714/1/Magrabi.pdf https://researchportal.port.ac.uk/portal/en/publications/artificial-intelligence-in-clinical-decision-support(80537b5e-2859-43ae-b394-97cde2450ecb).html https://www.thieme-connect.de/products/ejournals/pdf/10.1055/s-0039-1677903.pdf https://researchers.mq.edu.au/en/publications/artificial-intelligence-in-clinical-decision-support-challenges-f https://www.thieme-connect.com/products/ejournals/pdf/10.1055/s-0039-1677903.pdf https://www.thieme-connect.de/products/ejournals/abstract/10.1055/s-0039-1677903 https://europepmc.org/article/MED/31022752 https://pure.amsterdamumc.nl/en/publications/3fcf4e48-3e02-4f1f-905b-4009dc5c0067 https://doi.org/10.1055/s-0039-1677903 https://researchportal.port.ac.uk/ws/files/13856362/Artificial_Intelligence_in_clinical_decision_support.pdf https://trepo.tuni.fi/handle/10024/116926 |
| Rights: | CC BY NC ND URL: http://creativecommons.org/licences/by-nc-nd/4.0 |
| Accession Number: | edsair.doi.dedup.....b0a6f9b8f4cef39d00081bb4099f25e8 |
| Database: | OpenAIRE |
| ISSN: | 23640502 09434747 |
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| DOI: | 10.1055/s-0039-1677903 |