EXPLAINABLE ARTIFICIAL INTELLIGENCE THROUGH HEALTHCARE DIAGNOSTICS

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EXPLAINABLE ARTIFICIAL INTELLIGENCE THROUGH HEALTHCARE DIAGNOSTICS

Abstract:

As artificial intelligence (AI) continues to revolutionize healthcare diagnostics, there is a growing need for transparency and interpretability in the decision-making processes of AI models. This paper explores the concept of Explainable Artificial Intelligence (XAI) in the context of healthcare diagnostics, focusing on its crucial role in enhancing both the accuracy of diagnoses and the trust between healthcare professionals, patients, and AI systems. We delve into various techniques and methodologies that contribute to creating transparent and interpretable AI models, ensuring that the reasoning behind diagnostic outcomes is accessible and understandable to non-experts. By incorporating XAI into healthcare diagnostics, this research aims to foster a collaborative and informed approach, empowering clinicians and patients to make well-informed decisions based on AI-assisted insights. Through real-world case studies and practical applications, we highlight the benefits of explainability in improving the acceptance and adoption of AI technologies in healthcare, ultimately contributing to more reliable, ethical, and patient-centric diagnostic practices.

CHAPTER ONE

INTRODUCTION

1.1 Background of the Study

Artificial Intelligence (AI) has emerged as a transformative force across various industries, revolutionizing the way tasks are performed, decisions are made, and information is processed. In recent years, the integration of AI into healthcare diagnostics has garnered significant attention, offering the potential to enhance accuracy, efficiency, and accessibility in medical decision-making processes. One crucial aspect of this integration is the development of Explainable Artificial Intelligence (XAI) systems, which aim to provide transparent and interpretable insights into the decision-making process of AI algorithms, especially in critical domains like healthcare diagnostics.

Currently, artificial intelligence, which is widely applied in several domains, can perform well and quickly. This is the result of the continuous development and optimization of machine learning algorithms to solve many problems, including in the healthcare field, making the use of AI in medical imaging one of the most important scientific interests. However, AI based on deep learning algorithms is not transparent, making clinicians uncertain about the signs of diagnosis. The key question then is how one can provide convincing evidence of the responses. However, there exists a gap between AI models and human understanding, currently known as “black-box” transparency. For this reason, many research works focus on simplifying the AI models for better understanding by clinicians, in order to improve confidence in the use of AI models. For example, the Defense Advanced Research Projects Agency (DARPA) of the United States developed the explainable AI (XAI) model in 2015. Later, in 2021, a trust AI project showed that the XAI can be used in interdisciplinary types of application problems, including psychology, statistics, and computer science, and may provide explanations that increase the trust of users.

Explainable Artificial Intelligence (XAI) has become a significant topic in recent years because these models can make AI systems trustworthy, compliant, effective, and robust. XAI refers to the techniques and methods to build AI applications that end users can understand and interpret. The end users can be domain experts, data scientists, or even individuals without academic knowledge about AI. Great success of deep learning (DL) alongside its widespread deployment in real-world applications has ignited the desire for interpreting the rationale behind its decisions. Generally, users favour transparent AI models which can be interpreted or explained clearly. Before moving on, two conceptually different terms need to be clarified. Interpretability refers to providing human-understandable rules that define a system’s decision-making mechanism. In comparison, explainability concerns creating a humancomprehensible interface for disentangling the internal AI decision-making function (1). The importance of AI explainability can be discussed from different viewpoints (1-4). First, explaining machine learning (ML) models is vital for verifying sensitive models such as those related to the human healthcare system. Medical experts need to ensure the models are trained correctly and the parameters on which they are dependent are consistent with their knowledge. For instance, if the post-hoc analysis results of an ML model conclude that sneezing is a sign of cancer, the medical doctor can immediately imply that the ML model is not trustworthy. Secondly, complex ML models such as deep neural networks are usually trained on very high-dimensional data and encapsulate salient features. Explaining these trained models will provide insightful information for experts in various fields of study such as Physics, Mathematics, and Chemistry. Using this information, scientists are able to discover new natural rules, obtain better observation about fundamental questions, and facilitate the advancement of research in these fields. Third, our everyday life is becoming more and more dependent on AI models in various senses. For example, many kinds of paperwork processing are handled by AI solutions and rejected applicants need to know the rejection reasons. Fourth, Neuroscience can benefit tremendously from AI Explainability to test and explain different hypotheses about the interaction between neurons in the brain and answer questions about computational activities and the learning mechanism of the brain. AI explainability solutions based on post-hoc modelling, and analysis for ML models deciphering can be divided into model agnostic and model-specific methods. Model-agnostic approaches are general purpose and can be applied to almost all ML models regardless of their structure and training mechanism. One of the robust agnostic approaches is sensitivity analysis (SA) which attempts to reveal the contribution and impact of input factors on output prediction by changing input values and observing the amount of variation caused in the output (5). These methods indicate the sensitivity level of the output on each of the input variables based on different statistical features such as variance (6), derivative (7), and density (8). Sensitivity analysis can be applied globally or locally (9). Local SA methods rely on local perturbation of input values and measure the sensitivity based on the amount of variation of the output values. In the global SA techniques, the total possible values of input parameters are subject to change (10).

In contrast to model-agnostic approaches, model-specific methods can only be utilized for specific ML models. For example, many explainability mechanisms are developed to analyze trained deep neural networks. These approaches are known as deep network understanding and visualization (11). Activation Maximization (12), DeConvNet (13), inversion (14), deepDream (15), feature visualization analysis (16), and DeepLift (17) are some of the popular methods from this category which attempt to find the contribution of neurons of convolutional neural networks on their final decision through optimization and backpropagation. Moreover, specific approaches have been proposed for explaining Graph Neural Networks (18) and Recurrent Neural Networks (19).

Explainability solutions leverage various criteria such as trustworthiness, transferability, causality, and interactivity. Trustworthiness refers to the idea of seeking a simple model which makes an equal decision upon meeting a specific condition. Transferability explains the generalization capacity of a complex model and the reusable scenarios. In causality approach, the emphasis is towards finding cause and effect relationships (i.e. correlation) between variables. Some authors believe that for accurate interpretation of a model, the reasons behind its decisions must be uncovered. In this regard, counterfactual is a promising strategy to find the features contributing to a specific outcome (20). Another factor used in some AI explainability techniques is the model’s ability to engage with endusers. Recent studies have focused on developing hybrid approaches which result in transparent models with representation power of existing black box architectures such as DL. Contextual Explanation Network (CEN) (21), Self-Explaining Neural Network (SENN) (22), BagNet (23), and TabNet (24) are typical examples of transparent models. The end-users’ interest in understanding the reasons behind the decisions made by ML models demands further research. Model transparency is especially important in safety-critical applications such as medical domain. Therefore, our focus is on XAI in the healthcare domain and its challenges. The primary goals of our study are listed below:

(i) XAI methods identification and categorization

(ii) XAI literature review with special focus on healthcare domain (iii) Ascertainment of XAI challenges and problems in healthcare.

1.2 Statement of the Problem

While AI algorithms exhibit impressive diagnostic capabilities, their adoption in the healthcare sector faces challenges related to transparency and interpretability. The “black box” nature of complex AI models raises concerns about trust, accountability, and ethical considerations, especially when applied to healthcare diagnostics where the stakes are high. Understanding and explaining how AI arrives at specific diagnostic decisions become paramount for clinicians, patients, and policymakers. Therefore, the need for Explainable Artificial Intelligence in healthcare diagnostics becomes evident.

1.3 Objectives of the Study

The primary objectives of this research are as follows:

To investigate the current landscape of AI applications in healthcare diagnostics.

To explore the challenges associated with the lack of explainability in AI-based diagnostic systems.

To assess the significance of Explainable Artificial Intelligence in enhancing transparency and trust in healthcare diagnostics.

To examine existing methodologies and frameworks for implementing XAI in healthcare settings.

To propose recommendations for the effective integration of Explainable Artificial Intelligence in healthcare diagnostics.

1.4 Research Questions

To achieve the objectives outlined above, the following research questions will guide the study:

What is the current state of AI applications in healthcare diagnostics?

What challenges are associated with the lack of explainability in AI-based diagnostic systems?

How can Explainable Artificial Intelligence enhance transparency and trust in healthcare diagnostics?

What methodologies and frameworks are currently used for implementing XAI in healthcare settings?

1.5 Significance of the Study

This study holds significance for various stakeholders in the healthcare ecosystem. For healthcare practitioners, the research will provide insights into how AI can be effectively utilized in diagnostics with a focus on transparency. Patients will benefit from a clearer understanding of the diagnostic processes, fostering trust and informed decision-making. Policymakers and regulatory bodies can leverage the findings to formulate guidelines that ensure ethical and responsible use of AI in healthcare.

1.6 Scope and Limitations

This study focuses on the application of Explainable Artificial Intelligence specifically in the context of healthcare diagnostics. The research will explore existing challenges, methodologies, and frameworks related to XAI in this domain. However, it is essential to acknowledge the limitations inherent in the rapidly evolving field of AI and healthcare, including the dynamic nature of technologies and the potential bias in existing datasets.

1.7 Organization of the Thesis

This thesis is organized into several chapters to provide a structured presentation of the research. Chapter Two will review the relevant literature on AI in healthcare diagnostics and the need for explainability. Chapter Three will discuss the methodologies employed in this study. Chapter Four presents the findings and analysis, while Chapter Five concludes the research, providing recommendations for the integration of Explainable Artificial Intelligence in healthcare diagnostics.

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