*Proceeding Paper* **Improving Medical Data Annotation Including Humans in the Machine Learning Loop †**

**José Bobes-Bascarán \*, Eduardo Mosqueira-Rey and David Alonso-Ríos**

Centro de Investigación en TIC (CITIC), Universidade da Coruña, Elviña, 15071 A Coruña, Spain; eduardo.mosqueira@udc.es (E.M.-R.); david.alonso@udc.es (D.A-R.)

**\*** Correspondence: jose.bobes@udc.es

† Presented at the 4th XoveTIC Conference, A Coruña, Spain, 7–8 October 2021.

**Abstract:** At present, the great majority of Artificial Intelligence (AI) systems require the participation of humans in their development, tuning, and maintenance. Particularly, Machine Learning (ML) systems could greatly benefit from their expertise or knowledge. Thus, there is an increasing interest around how humans interact with those systems to obtain the best performance for both the AI system and the humans involved. Several approaches have been studied and proposed in the literature that can be gathered under the umbrella term of Human-in-the-Loop Machine Learning. The application of those techniques to the health informatics environment could provide a great value on prognosis and diagnosis tasks contributing to develop a better health service for Cancer related diseases.

**Keywords:** Human-in-the-Loop Machine Learning; Interactive Machine Learning; Machine Teaching; Iterative Machine Teaching; Active Learning
