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Cited by in F6Publishing
For: Bruckert S, Finzel B, Schmid U. The Next Generation of Medical Decision Support: A Roadmap Toward Transparent Expert Companions. Front Artif Intell 2020;3:507973. [PMID: 33733193 DOI: 10.3389/frai.2020.507973] [Cited by in Crossref: 4] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
Number Citing Articles
1 Harada Y, Katsukura S, Kawamura R, Shimizu T. Effects of a Differential Diagnosis List of Artificial Intelligence on Differential Diagnoses by Physicians: An Exploratory Analysis of Data from a Randomized Controlled Study. Int J Environ Res Public Health 2021;18:5562. [PMID: 34070958 DOI: 10.3390/ijerph18115562] [Reference Citation Analysis]
2 Antoniadi AM, Du Y, Guendouz Y, Wei L, Mazo C, Becker BA, Mooney C. Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review. Applied Sciences 2021;11:5088. [DOI: 10.3390/app11115088] [Cited by in Crossref: 9] [Cited by in F6Publishing: 1] [Article Influence: 9.0] [Reference Citation Analysis]
3 Prakash S, Unnikrishnan V, Pryss R, Kraft R, Schobel J, Hannemann R, Langguth B, Schlee W, Spiliopoulou M. Interactive System for Similarity-Based Inspection and Assessment of the Well-Being of mHealth Users. Entropy (Basel) 2021;23:1695. [PMID: 34946001 DOI: 10.3390/e23121695] [Reference Citation Analysis]
4 Holzinger A, Dehmer M, Emmert-streib F, Cucchiara R, Augenstein I, Ser JD, Samek W, Jurisica I, Díaz-rodríguez N. Information fusion as an integrative cross-cutting enabler to achieve robust, explainable, and trustworthy medical artificial intelligence. Information Fusion 2022;79:263-78. [DOI: 10.1016/j.inffus.2021.10.007] [Cited by in Crossref: 9] [Cited by in F6Publishing: 6] [Article Influence: 9.0] [Reference Citation Analysis]