A Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When?

Bharati, Subrato; Mondal, M. Rubaiyat Hossain; Podder, Prajoy

doi:10.1109/tai.2023.3266418

Cited by 35 publications

(12 citation statements)

References 166 publications

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“…To create the method, the inception TL model was changed. In 2023, Bharati et al ( 30 ) addressed the XAI methods in healthcare. The study examined the current XAI trends and outlined the main moving directions of the field.…”

Section: Related Workmentioning

confidence: 99%

Prediction of the COVID disease using lung CT images by Deep Learning algorithm: DETS-optimized Resnet 101 classifier

Haennah,

Christopher,

King

2023

Front. Med.

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As a result of the COVID-19 (coronavirus) disease due to SARS-CoV2 becoming a pandemic, it has spread over the globe. It takes time to evaluate the results of the laboratory tests because of the rising number of cases each day. Therefore, there are restrictions in terms of both therapy and findings. A clinical decision-making system with predictive algorithms is needed to alleviate the pressure on healthcare systems via Deep Learning (DL) algorithms. With the use of DL and chest scans, this research intends to determine COVID-19 patients by utilizing the Transfer Learning (TL)-based Generative Adversarial Network (Pix 2 Pix-GAN). Moreover, the COVID-19 images are then classified as either positive or negative using a Duffing Equation Tuna Swarm (DETS)-optimized Resnet 101 classifier trained on synthetic and real images from the Kaggle lung CT Covid dataset. Implementation of the proposed technique is done using MATLAB simulations. Besides, is evaluated via accuracy, precision, F1-score, recall, and AUC. Experimental findings show that the proposed prediction model identifies COVID-19 patients with 97.2% accuracy, a recall of 95.9%, and a specificity of 95.5%, which suggests the proposed predictive model can be utilized to forecast COVID-19 infection by medical specialists for clinical prediction research and can be beneficial to them.

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Section: Related Workmentioning

confidence: 99%

Prediction of the COVID disease using lung CT images by Deep Learning algorithm: DETS-optimized Resnet 101 classifier

Haennah,

Christopher,

King

2023

Front. Med.

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“…For this reason, AI research is increasing its interests in trustworthy AI [ 1 ], a broad paradigm establishing how to properly design, develop and deploy real-world AI applications. Between its principles, transparency requires providing the user with an understanding of the autonomous decisions generated by the model: this topic is subject of eXplainable AI (XAI) research [ 2 , 3 ]. XAI comprehends a wide range of methodologies, which can be broadly categorized as post-hoc explanations of black box models and transparent-by-design techniques [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Explainable artificial intelligence for cough-related quality of life impairment prediction in asthmatic patients

Narteni,

Baiardini,

Braido

et al. 2024

PLoS ONE

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Explainable Artificial Intelligence (XAI) is becoming a disruptive trend in healthcare, allowing for transparency and interpretability of autonomous decision-making. In this study, we present an innovative application of a rule-based classification model to identify the main causes of chronic cough-related quality of life (QoL) impairment in a cohort of asthmatic patients. The proposed approach first involves the design of a suitable symptoms questionnaire and the subsequent analyses via XAI. Specifically, feature ranking, derived from statistically validated decision rules, helped in automatically identifying the main factors influencing an impaired QoL: pharynx/larynx and upper airways when asthma is under control, and asthma itself and digestive trait when asthma is not controlled. Moreover, the obtained if-then rules identified specific thresholds on the symptoms associated to the impaired QoL. These results, by finding priorities among symptoms, may prove helpful in supporting physicians in the choice of the most adequate diagnostic/therapeutic plan.

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“…To overcome these limitations, first statistical models such as Vector Autoregression (VAR) and finally Deep Learning (DL) based models enhanced predictivity power with non-linear formulations, also taking into account multivariate data [7]. However, these models are considered opaque in terms of explainability [8]. The development of post-hoc explainability tools to overcome such limitations, such as Shap values [9], partially solved the issues but still rely on the expertise of the developer and are not as well-defined for time series models.…”

Section: Introductionmentioning

confidence: 99%

CCLR-DL: A Novel Statistics and Deep Learning Hybrid Method for Feature Selection and Forecasting Healthcare Demand

Hernández Guillamet,

López Seguí,

Vidal Alaball

et al. 2024

Preprint

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Hybrid forecasting methods have emerged as a solution surpassing the limitations of both statistical and deep learning approaches. While the first emphasize the significance of variables, they often produce worse forecasting results when compared to newer techniques. In contrast, deep learning models remain enigmatic "black boxes" in terms of interpretability, although achieving better results in forecasting. This article introduces the Comprehensive Cross-Correlation and Lagged Linear Regression Deep Learning (CCLR-DL) framework, designed to harness the best of both approaches, enhancing forecasting accuracy while retaining model interpretability through a feature selection process. CCLR-DL blens cross-correlation analysis, lagged multiple linear regression and granger's causality procedures with deep learning architectures based on LSTM. In a practical demonstration, CCLR-DL was applied to a real database of clinical visits associated to diagnoses in Catalonia, Spain (tracking a population of 6.3 million patients during 10 years). Predicting visits enables the healthcare managers to be ready for future demand shifts. Results demonstrate a consistent and substantial improvement over standalone statistical and deep learning methods when predicting healthcare demand. This hybrid approach not only showcases its efficacy but also offers a promising solution to the challenge of balancing predictive accuracy with model explicability. In this context, this work aims to design and validate a method for feature selection and forecasting of multivariate high dimensional time series datasets not only to improve prediction accuracy but also to model transparency by identifying a subset of variables that improve predictions and G-cause the target variable.

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A Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When?

Cited by 35 publications

References 166 publications

Prediction of the COVID disease using lung CT images by Deep Learning algorithm: DETS-optimized Resnet 101 classifier

Prediction of the COVID disease using lung CT images by Deep Learning algorithm: DETS-optimized Resnet 101 classifier

Explainable artificial intelligence for cough-related quality of life impairment prediction in asthmatic patients

CCLR-DL: A Novel Statistics and Deep Learning Hybrid Method for Feature Selection and Forecasting Healthcare Demand

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