A Systematic Review of Human–Computer Interaction and Explainable Artificial Intelligence in Healthcare With Artificial Intelligence Techniques

Nazar, Mobeen; Alam, Muhammad Mansoor; Yafi, Eiad; Su’ud, Mazliham Mohd

doi:10.1109/access.2021.3127881

Cited by 145 publications

(48 citation statements)

References 88 publications

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“…The concept of HCI refers to the interaction between the real world and augmented reality [ 67 ]. The human subject here is the patient whose interaction with the computer is recorded for the symptoms feature identification purposes.…”

Section: Future Of Explainability In Healthcarementioning

confidence: 99%

A Survey on Medical Explainable AI (XAI): Recent Progress, Explainability Approach, Human Interaction and Scoring System

Sheu

Pardeshi

2022

Sensors

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The emerging field of eXplainable AI (XAI) in the medical domain is considered to be of utmost importance. Meanwhile, incorporating explanations in the medical domain with respect to legal and ethical AI is necessary to understand detailed decisions, results, and current status of the patient’s conditions. Successively, we will be presenting a detailed survey for the medical XAI with the model enhancements, evaluation methods, significant overview of case studies with open box architecture, medical open datasets, and future improvements. Potential differences in AI and XAI methods are provided with the recent XAI methods stated as (i) local and global methods for preprocessing, (ii) knowledge base and distillation algorithms, and (iii) interpretable machine learning. XAI characteristics details with future healthcare explainability is included prominently, whereas the pre-requisite provides insights for the brainstorming sessions before beginning a medical XAI project. Practical case study determines the recent XAI progress leading to the advance developments within the medical field. Ultimately, this survey proposes critical ideas surrounding a user-in-the-loop approach, with an emphasis on human–machine collaboration, to better produce explainable solutions. The surrounding details of the XAI feedback system for human rating-based evaluation provides intelligible insights into a constructive method to produce human enforced explanation feedback. For a long time, XAI limitations of the ratings, scores and grading are present. Therefore, a novel XAI recommendation system and XAI scoring system are designed and approached from this work. Additionally, this paper encourages the importance of implementing explainable solutions into the high impact medical field.

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Section: Future Of Explainability In Healthcarementioning

confidence: 99%

A Survey on Medical Explainable AI (XAI): Recent Progress, Explainability Approach, Human Interaction and Scoring System

Sheu

Pardeshi

2022

Sensors

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“…The Processor fee is the most common parameter for estimating energy consumption. More sophisticated simulators require air-conditioning since this is a [11] major [66] contributor to the energy bill. The following simulators focus on energy consumption modeling [11].…”

Section: B Energy-aware Provisioningmentioning

confidence: 99%

A Systematic Survey of Simulation Tools for Cloud and Mobile Cloud Computing Paradigm

Shahid¹,

Faiz²,

Alam³

et al. 2022

JISR-C

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Cloud computing (CC) provides fast and on-demand access to virtually unlimited computing resources. Mobile cloud computing (MCC) is a new technology that brings CC and mobile devices together. MCC allows mobile devices access to cloud services. Describe the basic architectures for various MCC applications, as well as a brief comparison of cloud and MCC. As CC becomes more common, researchers in this field may need to conduct real-world experiments in their studies. Configuring and running these experiments in real-world cloud environments is costly. As a consequence, modeling and simulation approaches are suitable solutions that can be used to simulate cloud computing environments. Several simulation tools tailored to CC have been developed. The most powerful simulation methods in this field of study are described in this article. Among them are CloudSim, CloudSim Plus, CloudAnalyst, iFogSim, and CloudReports.

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“…The promise of explainable AI as “the answer” for HMT in healthcare has been a goal of extensive pursuits in the research literature. Interestingly, much of the literature on explainability echo the same system acceptance characteristics, validation, robustness, reliability, interpretability, transparency, etc., as are typical of any decision support system, but now aligned to ML algorithms [ 26 ]. Similarly, much of the literature also emphasizes model pairing—a interpretable “side” model that operates in conjunction with the predictive model [ 27 ] to support explainations.…”

Section: Background and Related Workmentioning

confidence: 99%

Providing Care: Intrinsic Human–Machine Teams and Data

Russell

Kumar

2022

Entropy

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Despite the many successes of artificial intelligence in healthcare applications where human–machine teaming is an intrinsic characteristic of the environment, there is little work that proposes methods for adapting quantitative health data-features with human expertise insights. A method for incorporating qualitative expert perspectives in machine learning training data is proposed. The method implements an entropy-based consensus construct that minimizes the challenges of qualitative-scale data such that they can be combined with quantitative measures in a critical clinical event (CCE) vector. Specifically, the CCE vector minimizes the effects where (a) the sample size is too small, (b) the data may not be normally distributed, or (c) The data are from Likert scales, which are ordinal, so parametric statistics cannot be used. The incorporation of human perspectives in machine learning training data provides encoding of human considerations in the subsequent machine learning model. This encoding provides a basis for increasing explainability, understandability, and ultimately trust in AI-based clinical decision support system (CDSS), thereby improving human–machine teaming concerns. A discussion of applying the CCE vector in a CDSS regime and implications for machine learning are also presented.

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A Systematic Review of Human–Computer Interaction and Explainable Artificial Intelligence in Healthcare With Artificial Intelligence Techniques

Cited by 145 publications

References 88 publications

A Survey on Medical Explainable AI (XAI): Recent Progress, Explainability Approach, Human Interaction and Scoring System

A Survey on Medical Explainable AI (XAI): Recent Progress, Explainability Approach, Human Interaction and Scoring System

A Systematic Survey of Simulation Tools for Cloud and Mobile Cloud Computing Paradigm

Providing Care: Intrinsic Human–Machine Teams and Data

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