Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_117_библиотеки_им_акад_М_И_Перельмана

1 Introduction

Explainable AI, often referred to as XAI, represents an emerging eld within arti­cial intelligence (AI), intending to develop machine learning models [1] that are characterized by transparency and ease of comprehension. Conventional models can be immensely challenging to decipher, often resembling what is colloquially termed a “black box” [1]. In contrast, XAI models furnish a rationale behind their predictions or decisions, offering valuable insights into the underlying decision­making process [2]. It becomes especially pertinent in industries where real-time applications are crucial, such as healthcare, IT, nance, and agriculture, given that erroneous decisions can carry signicant ramications. XAI achieves this objective through various mechanisms, including decision trees, saliency maps, and attention mechanisms, all of which serve to visualize the decision-making process. These tools effectively highlight the pivotal features that inuence the model’s output. By employing XAI, users can grasp the contributing factors and pinpoint potential biases or errors within the model, enhancing its transparency and reliability.

In healthcare, the process of AI-driven decision-making [3–16] is intricate, rely­ing on many inputs, including medical records, patient histories, diagnoses, and expert insights. While machine learning models can provide valuable assistance, there is a growing demand for enhanced interpretability. Explainable articial intel­ligence (XAI) [4] addresses this need by offering visualizations and explanations that shed light on how AI models arrive at their decisions in the context of medicine. This infusion of transparency fosters trust. XAI serves as a conduit for medical professionals better to understand the rationale behind the models’ decisions, lead­ing to more precise and dependable clinical judgments. Additionally, XAI [3] is pivotal in helping healthcare experts uncover potential biases or errors within auto­mated systems, ultimately contributing to more resilient and accurate patient care decisions. It becomes precious in medical imaging, encompassing modalities such as CT scans and MRIs, where the sheer volume of data can overwhelm healthcare providers. Analyzing kidney organ pathology [4] and CT scans through the lens of XAI [3] introduces distinctive challenges that necessitate thoughtful consideration. Understanding the intricacy of AI techniques used in image analysis, which often involve deep learning models with numerous layers and parameters, is a signicant challenge. To incorporate XAI [3] into clinical procedures effectively, it is essential to develop methods that provide meaningful explanations while maintaining the

Furthermore, guided by in-depth diagnostic and prognostic insights, healthcare providers can ne-tune treatment regimens, ensuring top-notch patient care. Understanding the nuances of their diagnosis and prognosis equips patients and their families to make well-informed healthcare decisions. XAI extends from choos­ing suitable treatments to making pivotal choices about end-of-life care. Beyond individual care, accurate diagnostics and prognostics [4] bolster medical research, propelling the innovation of diagnostic tools [5], therapeutic approaches, and pre­ventive strategies. Such insights also inuence public health strategies, spotlighting disease patterns and facilitating crafting of targeted health interventions and poli­cies. Medical training and education are deeply intertwined with these precise insights, enabling the molding of competent healthcare professionals primed for delivering optimal patient care. In essence, the integrity of a healthcare system thrives on accurate disease diagnosis and prognosis, which form the bedrock for premium patient care and outcomes.

In the literature overview, Table1 discusses explainable AI and its potential in iden- tifying and predicting diseases. The medical eld should prioritize usability and clarity, as emphasized in the study by Holzinger etal. (2019) [1]. They found that “layer-wise relevance propagation (LRP)” is effective in distinguishing histopathol­ogy images in breast cancer detection. Al’Aref etal. (2020) explore machine learn­ing’s application in cardiac imagery for cardiovascular diseases and use SHAP values to predict breast cancer recurrence. Lundervold and Lundervold (2019) pro­vide an in-depth analysis of deep learning’s signicance in medical imagery, par­ticularly MRI.They highlight the use of Grad-CAM in detecting lung nodules in CT images, which aids in lung cancer detection. Lastly, Yoon etal. (2020) utilize LIME