Abstract

Abstract

Abstract
In the healthcare context, artificial intelligence (AI) has the potential to power decision support systems and help health professionals in their clinical decisions. However, given its complexity, AI is usually seen as a black box that receives data and outputs a prediction. This behavior may jeopardize the adoption of this technology by the healthcare community, which values the existence of explanations to justify a clinical decision. Besides, the developers must have a strategy to assess and audit these systems to ensure their reproducibility and quality in production. The field of interpretable artificial intelligence emerged to study how these algorithms work and clarify their behavior. This chapter reviews several interpretability of AI algorithms for medical imaging, discussing their functioning, limitations, benefits, applications, and evaluation strategies. The chapter concludes with considerations that might contribute to bringing these methods closer to the daily routine of healthcare professionals. © 2025 Elsevier Inc. All rights reserved.

Abstract

Abstract
Keratitis is an inflammatory corneal condition responsible for 10% of visual impairment in low- and middle-income countries (LMICs), with bacteria, fungi, or amoeba as the most common infection etiologies. While an accurate and timely diagnosis is crucial for the selected treatment and the patients' sight outcomes, due to the high cost and limited availability of laboratory diagnostics in LMICs, diagnosis is often made by clinical observation alone, despite its lower accuracy. In this study, we investigate and compare different deep learning approaches to diagnose the source of infection: 1) three separate binary models for infection type predictions; 2) a multitask model with a shared backbone and three parallel classification layers (Multitask V1); and, 3) a multitask model with a shared backbone and a multi-head classification layer (Multitask V2). We used a private Brazilian cornea dataset to conduct the empirical evaluation. We achieved the best results with Multitask V2, with an area under the receiver operating characteristic curve (AUROC) confidence intervals of 0.7413-0.7740 (bacteria), 0.83950.8725 (fungi), and 0.9448-0.9616 (amoeba). A statistical analysis of the impact of patient features on models' performance revealed that sex significantly affects amoeba infection prediction, and age seems to affect fungi and bacteria predictions. © 2024 IEEE.

Abstract