Trust for the "Doctor in the Loop"

22 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

What do we need to build explainable AI systems for the medical domain? (2017, background): “Explainable-AI calls for conﬁdence, safety, security, privacy, ethics, fairness and trust (Kieseberg et al., 2016), and puts usability on the research agenda, too Miller et al. (2017).”
Towards interactive Machine Learning (iML): Applying Ant Colony Algorithms to Solve the Traveling Salesman Problem with the Human-in-the-Loop Approach (2016, unknown): “in health informatics, the inclusion of a “doctor-into-the-loop” [12,13] can play a significant role in support of solving hard problems, particularly in combination with a large number of human agents (crowdsourcing).”
A glass-box interactive machine learning approach for solving NP-hard problems with the human-in-the-loop (2017, background): “…integration of a ”human-into-the-loop” may have many practical applications, as e.g. in health informatics, the inclusion of a “doctor-into-the-loop” (Kieseberg et al., 2016a), (Kieseberg et al., 2016b) can play a signiﬁcant role in support of solving hard problems, particularly in combination…”
Towards the Augmented Pathologist: Challenges of Explainable-AI in Digital Pathology (2017, background): “Thus, one vital aspect lies in gaining trust of the doctor in the loop (Kieseberg et al., 2016b,a) i.e. assuring that the information that was presented as starting point for giving expertise is not changed afterwards.”