Page 158 - AIH-2-3

Artificial Intelligence in Health Explainable solutions from AI for HSSs

Conflict of interest doi: 10.1186/s12911-019-0804-1

The authors declare they have no competing interests. 6. Musen M. The protégé project: A look back and a look
forward. AI Matters. 2015;1(4):4-12.
Author contributions doi: 10.1145/2757001.2757003

Conceptualization: Valeriya Gribova 7. Mortensen J, Minty E, Januszyk M, et al. Using the wisdom
Investigation: All authors of the crowds to find critical errors in biomedical ontologies:
Methodology: All authors A study of SNOMED CT. J Am Med Inform Assoc.
Software: Elena Shalfeeva 2015;22(3):640-648.
Writing – original draft: All authors doi: 10.1136/amiajnl-2014-002901
Writing – review & editing: Elena Shalfeeva
8. Loh HW, Ooi CP, Seoni S, Barua PD, Molinari F, Acharya UR.
Ethics approval and consent to participate Application of explainable artificial intelligence for
healthcare: A systematic review of the last decade (2011-
Not applicable. 2022). Comput Methods Programs Biomed. 2022;226:107161.
doi: 10.1016/j.cmpb.2022.107161
Consent for publication
9. Yang G, Ye Q, Xia J. Unbox the black-box for the medical
Not applicable. explainable AI via multi-modal and multi-centre data
fusion: A mini-review, two showcases and beyond. Inf
Availability of data Fusion. 2022;77:29-52.
Some software and information components for the study doi: 10.48550/arXiv.2102.01998
can be obtained by contacting the authors at shalf@iacp. 10. Gartner Identifies the Top Strategic Technology Trends for 2021.
dvo.ru.
STAMFORD, Conn; 2020. Available from: https://www.gartner.
Further disclosure com/en/newsroom/press-releases/2020-10-19-gartner-
identifies-the-top-strategic-technology-trends-for-2021
Some of the findings have been presented in the preprint 11. Pressman RS. Architectural Design, Software Engineering:
(https://doi.org/10.21203/rs.3.rs-814383/v1) deposited in Practitioner’s Approach. 7 ed. New York, NY, USA:
th
the preprint server “Research Square.” McGraw-Hill; 2010. p. 242-275.
References 12. Islam M, Katiyar V. Development of a software maintenance
cost estimation model: 4 GL perspective. Int J Techn Res
th
1. Peleg M. Computer-interpretable clinical guidelines: Appl. 2014;2(6):65-68.
A methodological review. J Biomed Inform. 2013;46(4):744-763.
13. Izurieta C, Bieman JM. A multiple case study of design
doi: 10.1016/j.jbi.2013.06.009 pattern decay, grime, and rot in evolving software systems.
2. Young O, Shahar Y, Liel Y, et al. Runtime application Software Qual J. 2013;21(2):289-323.
of Hybrid-Asbru clinical guidelines. J Biomed Inform. doi: 10.1007/s11219-012-9175-x
2007;40(5):507-526.
14. Breivold HP, Crnkovic I, Eriksson PJ. Analyzing Software
doi: 10.1016/j.jbi.2006.12.004 Evolvability. COMPSAC 2008: 32 Annual IEEE International
nd
3. Novais P, Oliveira T, Satoh K, Neves J. The role of ontologies Computer Software and Applications Conference. Turku,
and decision frameworks in computer-interpretable Finland; 2008. p. 327-330.
guideline execution. In: Nalepa G, Baumeister J, editors. 15. Finn VK. About Data Mining. Artificial Intelligence News.
Synergies between Knowledge Engineering and Software (In Russ.); 2004. p. 3-18. Available from: https://masters.
Engineering. Advances in Intelligent Systems and Computing. donntu.ru/2006/kita/balabanov/library/articles/art010.pdf
Vol. 626. Cham: Springer; 2018. p. 197-216.
16. Weissler EH, Naumann T, Andersson T, et al. The role of
4. Deng C, Ji X, Rainey C, Zhang J, Lu W. Integrating machine learning in clinical research: Transforming the
machine learning with human knowledge. iScience. future of evidence generation. Trials. 2021;22:537.
2020;23(11):101656.
doi: 10.1186/s13063-021-05489-x
doi: 10.1016/j.isci.2020.101656
17. Lin Z, Cheng YT, Cheung BMY. Machine learning
5. Müller L, Gangadharaiah R, Klein SC, et al. An open access algorithms identify hypokalaemia risk in people with
medical knowledge base for community driven diagnostic hypertension in the United States National Health and
decision support system development. BMC Med Inform Nutrition Examination Survey 1999-2018. Ann Med.
Decis Mak. 2019;19:93. 2023;55(1):2209336.

Volume 2 Issue 3 (2025) 152 doi: 10.36922/aih.5736