Stress-Testing of Multimodal Models in Medical Image-Based Report Generation

• Type: Article in International Conference Proceedings Book
• Date: 2025

Authors

Carvalhido, F (Author) Other; Henrique Lopes Cardoso (Author) FEUP; Cerqueira, V (Author) FEUP

Conference proceedings International

Title: AAAI-25, Sponsored by the Association for the Advancement of Artificial Intelligence, February 25 - March 4, 2025, Philadelphia, PA, USA

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

Philadelphia, 25 February 2025 through 4 March 2025

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Scopus - 0 Citations

Other information

Authenticus ID: P-018-HHM

DOI: 10.1609/aaai.v39i28.35203

Abstract (EN): Multimodal models, namely vision-language models, present unique possibilities through the seamless integration of different information mediums for data generation. These models mostly act as a black-box, making them lack transparency and explicability. Reliable results require accountable and trustworthy Artificial Intelligence (AI), namely when in use for critical tasks, such as the automatic generation of medical imaging reports for healthcare diagnosis. By exploring stress-testing techniques, multimodal generative models can become more transparent by disclosing their shortcomings, further supporting their responsible usage in the medical field. Copyright © 2025, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 1