Essential properties and explanation effectiveness of explainable artificial intelligence in healthcare: A systematic review

Jung, Jinsun; Lee, Hyungbok; Jung, Hyunggu; Kim, Hyeoneui

doi:10.1016/j.heliyon.2023.e16110

Cited by 25 publications

(3 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Explainable artificial intelligence (XAI) addresses the black-box nature of artificial intelligence. One systematic review [ 25 ] assessed the state of XAI in healthcare, noting limited research, diverse stakeholder perspectives, and the need for standardized evaluation methods. Zhang et al [ 26 ] explored the growing potential of XAI in medical diagnosis and surgery by examining recent trends, conducting a survey, presenting a breast cancer case study, and highlighting its promising prospects.…”

Section: Methodsmentioning

confidence: 99%

Automated detection of otosclerosis with interpretable deep learning using temporal bone computed tomography images

Wang,

Song,

Lin

et al. 2024

Heliyon

View full text Add to dashboard Cite

Section: Methodsmentioning

confidence: 99%

Automated detection of otosclerosis with interpretable deep learning using temporal bone computed tomography images

Wang,

Song,

Lin

et al. 2024

Heliyon

View full text Add to dashboard Cite

“…Furthermore, it is a challenge to assess the quality/effectiveness of explainability. A previous systematic review reported various methods for assessing explainable AI effectiveness, with few established methods [ 51 ]. Establishing standardized approaches to measure the effectiveness of explainable AI might increase its integration into clinical settings and act as a tool of communication among clinicians, researchers, and developers [ 28 ].…”

Section: Challenges In Implementation Into Clinical Settingsmentioning

confidence: 99%

Explainable artificial intelligence in emergency medicine: an overview

Okada,

Ning,

Ong

2023

Clin Exp Emerg Med

View full text Add to dashboard Cite

Artificial intelligence (AI) and machine learning (ML) have potential to revolutionize emergency medical care by enhancing triage systems, improving diagnostic accuracy, refining prognostication, and optimizing various aspects of clinical care. However, as clinicians often lack AI expertise, they might perceive AI as a “black box,” leading to trust issues. To address this, “explainable AI,” which teaches AI functionalities to end-users, is important. This review presents the definitions, importance, and role of explainable AI, as well as potential challenges in emergency medicine. First, we introduce the terms explainability, interpretability, and transparency of AI models. These terms sound similar but have different roles in discussion of AI. Second, we indicate that explainable AI is required in clinical settings for reasons of justification, control, improvement, and discovery and provide examples. Third, we describe three major categories of explainability: pre-modeling explainability, interpretable models, and post-modeling explainability and present examples (especially for post-modeling explainability), such as visualization, simplification, text justification, and feature relevance. Last, we show the challenges of implementing AI and ML models in clinical settings and highlight the importance of collaboration between clinicians, developers, and researchers. This paper summarizes the concept of “explainable AI” for emergency medicine clinicians. This review may help clinicians understand explainable AI in emergency contexts.

show abstract

“…This review will also provide some future directions. It will not attempt to give a full overview of the current literature on this topic or explain in detail which methods exist to explain AI algorithms, as several excellent reviews on this topic already exist [12][13][14][15]. First, we will go through some central concepts of XAI.…”

Section: Introductionmentioning

confidence: 99%

Explainable Artificial Intelligence (XAI): Concepts and Challenges in Healthcare

Hulsen

2023

View full text Add to dashboard Cite

Artificial Intelligence (AI) describes computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and language translation. Examples of AI techniques are machine learning, neural networks, and deep learning. AI can be applied in many different areas, such as econometrics, biometry, e-commerce, and the automotive industry. In recent years, AI has found its way into healthcare as well, helping doctors make better decisions (“clinical decision support”), localizing tumors in magnetic resonance images, reading and analyzing reports written by radiologists and pathologists, and much more. However, AI has one big risk: it can be perceived as a “black box”, limiting trust in its reliability, which is a very big issue in an area in which a decision can mean life or death. As a result, the term Explainable Artificial Intelligence (XAI) has been gaining momentum. XAI tries to ensure that AI algorithms (and the resulting decisions) can be understood by humans. In this narrative review, we will have a look at some central concepts in XAI, describe several challenges around XAI in healthcare, and discuss whether it can really help healthcare to advance, for example, by increasing understanding and trust. Finally, alternatives to increase trust in AI are discussed, as well as future research possibilities in the area of XAI.

show abstract

Essential properties and explanation effectiveness of explainable artificial intelligence in healthcare: A systematic review

Cited by 25 publications

References 45 publications

Automated detection of otosclerosis with interpretable deep learning using temporal bone computed tomography images

Automated detection of otosclerosis with interpretable deep learning using temporal bone computed tomography images

Explainable artificial intelligence in emergency medicine: an overview

Explainable Artificial Intelligence (XAI): Concepts and Challenges in Healthcare

Contact Info

Product

Resources

About