Assessing the Effectiveness of Textual Recommendations in KoopaML: A Comparative Study on Non-Expert Users' ML Pipeline Development

Pablo Antúnez-Muiños, Pablo Pérez-Sánchez, Andrea Vázquez-Ingelmo, Francisco José García-Peñalvo, Antonio Sánchez-Puente, Víctor Vicente-Palacios, Alicia García-Holgado, P. Ignacio Dorado-Díaz, Jesús Sampedro-Gómez, Ignacio Cruz-González and Pedro L. Sánchez
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Pablo Antúnez-Muiños: University Hospital of Salamanca, Spain
Pablo Pérez-Sánchez: University Hospital of Salamanca, Spain
Andrea Vázquez-Ingelmo: GRIAL Research Group, University of Salamanca, Spain
Francisco José García-Peñalvo: GRIAL Research Group, University of Salamanca, Spain
Antonio Sánchez-Puente: University Hospital of Salamanca, Spain
Víctor Vicente-Palacios: Philips Clinical Science, Spain
Alicia García-Holgado: GRIAL Research Group, University of Salamanca, Spain
P. Ignacio Dorado-Díaz: University of Salamanca, Spain
Jesús Sampedro-Gómez: CIBERCV, Spain
Ignacio Cruz-González: University Hospital of Salamanca, Spain
Pedro L. Sánchez: University of Salamanca, Spain & University Hospital of Salamanca, Spain

International Journal on Semantic Web and Information Systems (IJSWIS), 2024, vol. 20, issue 1, 1-21

Abstract: Artificial intelligence (AI) integration, notably in healthcare, has been significant, yet effective implementation in critical areas requires expertise. KoopaML, a previously developed visual platform, aims at bridging this gap, enabling users with limited AI knowledge to build ML pipelines. Its core is a heuristic-based ML task recommender, offering guidance and contextual explanations. The authors compared the use of KoopaML with two non-expert groups: one with the recommender system enabled and the other without. Results showed KoopaML's intuitiveness benefits all but emphasized that textual guidance doesn't substitute for fundamental ML understanding. This underscores the need for educational components in such tools, especially in critical fields like healthcare. The paper suggests future KoopaML enhancements include educational modules, making ML accessible, and ensuring users develop a solid AI foundation. This is crucial for quality outcomes in sectors like healthcare, leveraging AI's potential through enhanced non-expert user capability.

Date: 2024
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