Search

Explainability is often critical to the acceptable implementation of artificial intelligence (AI). Nowhere is this more important than healthcare where decision-making directly impacts patients and trust in AI systems is essential. This trust is often built on the explanations and interpretations the AI provides. Despite significant advancements in AI interpretability, there remains the need for clear guidelines on when and to what extent explanations are necessary in the medical context. We propose a novel categorization system with four distinct classes of explanation necessity, guiding the level of explanation required: patient or sample (local) level, cohort or dataset (global) level, or both levels. We introduce a mathematical formulation that distinguishes these categories and offers a practical framework for researchers to determine the necessity and depth of explanations required in medical AI applications. Three key factors are considered: the robustness of the evaluation protocol, the variability of expert observations, and the representation dimensionality of the application. In this perspective, we address the question: When does an AI medical application need to be explained, and at what level of detail?

In the last decade, computer vision has witnessed the establishment of various training and learning approaches. Techniques like adversarial learning, contrastive learning, diffusion denoising learning, and ordinary reconstruction learning have become standard, representing state-of-the-art methods extensively employed for fully training or pre-training networks across various vision tasks. The exploration of fine-tuning approaches has emerged as a current focal point, addressing the need for efficient model tuning with reduced GPU memory usage and time costs while enhancing overall performance, as exemplified by methodologies like low-rank adaptation (LoRA). Key questions arise: which pre-training technique yields optimal results - adversarial, contrastive, reconstruction, or diffusion denoising? How does the performance of these approaches vary as the complexity of fine-tuning is adjusted? This study aims to elucidate the advantages of pre-training techniques and fine-tuning strategies to enhance the learning process of neural networks in independent identical distribution (IID) cohorts. We underscore the significance of fine-tuning by examining various cases, including full tuning, decoder tuning, top-level tuning, and fine-tuning of linear parameters using LoRA. Systematic summaries of model performance and efficiency are presented, leveraging metrics such as accuracy, time cost, and memory efficiency. To empirically demonstrate our findings, we focus on a multi-task segmentation-classification challenge involving the paracingulate sulcus (PCS) using different 3D Convolutional Neural Network (CNN) architectures by using the TOP-OSLO cohort comprising 596 subjects.

The significant features identified in a representative subset of the dataset during the learning process of an artificial intelligence model are referred to as a 'global' explanation. Three-dimensional (3D) global explanations are crucial in neuroimaging where a complex representational space demands more than basic two-dimensional interpretations. Curently, studies in the literature lack accurate, low-complexity, and 3D global explanations in neuroimaging and beyond. To fill this gap, we develop a novel explainable artificial intelligence (XAI) 3D-Framework that provides robust, faithful, and low-complexity global explanations. We evaluated our framework on various 3D deep learning networks trained, validated, and tested on a well-annotated cohort of 596 MRI images. The focus of detection was on the presence or absence of the paracingulate sulcus, a highly variable feature of brain topology associated with symptoms of psychosis. Our proposed 3D-Framework outperformed traditional XAI methods in terms of faithfulness for global explanations. As a result, these explanations uncovered new patterns that not only enhance the credibility and reliability of the training process but also reveal the broader developmental landscape of the human cortex. Our XAI 3D-Framework proposes for the first time, a way to utilize global explanations to discover the context in which detection of specific features are embedded, opening our understanding of normative brain development and atypical trajectories that can lead to the emergence of mental illness.