A Review on Bayesian Deep Learning in Healthcare: Applications and Challenges
In the last decade, Deep Learning (DL) has revolutionized the use of artificial intelligence, and it has been deployed in different fields of healthcare applications such as image processing, natural language processing, and signal processing. DL models have also been intensely used in different tas...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9745083/ |
| _version_ | 1811272725893742592 |
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| author | Abdullah A. Abdullah Masoud M. Hassan Yaseen T. Mustafa |
| author_facet | Abdullah A. Abdullah Masoud M. Hassan Yaseen T. Mustafa |
| author_sort | Abdullah A. Abdullah |
| collection | DOAJ |
| description | In the last decade, Deep Learning (DL) has revolutionized the use of artificial intelligence, and it has been deployed in different fields of healthcare applications such as image processing, natural language processing, and signal processing. DL models have also been intensely used in different tasks of healthcare such as disease diagnostics and treatments. Deep learning techniques have surpassed other machine learning algorithms and proved to be the ultimate tools for many state-of-the-art applications. Despite all that success, classical deep learning has limitations and their models tend to be very confident about their predicted decisions because it does not know when it makes mistake. For the healthcare field, this limitation can have a negative impact on models predictions since almost all decisions regarding patients and diseases are sensitive. Therefore, Bayesian deep learning (BDL) has been developed to overcome these limitations. Unlike classical DL, BDL uses probability distributions for the model parameters, which makes it possible to estimate the whole uncertainties associated with the predicted outputs. In this regard, BDL offers a rigorous framework to quantify all sources of uncertainties in the model. This study reviews popular techniques of using Bayesian deep learning with their benefits and limitations. It also reviewed recent deep learning architecture such as Convolutional Neural Networks and Recurrent Neural Networks. In particular, the applications of Bayesian deep learning in healthcare have been discussed such as its use in medical imaging tasks, clinical signal processing, medical natural language processing, and electronic health records. Furthermore, this paper has covered the deployment of Bayesian deep learning for some of the widespread diseases. This paper has also discussed the fundamental research challenges and highlighted some research gaps in both the Bayesian deep learning and healthcare perspective. |
| first_indexed | 2024-04-12T22:45:30Z |
| format | Article |
| id | doaj.art-0c3880b56a5645628d2f8938744ae4b4 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T22:45:30Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-0c3880b56a5645628d2f8938744ae4b42022-12-22T03:13:31ZengIEEEIEEE Access2169-35362022-01-0110365383656210.1109/ACCESS.2022.31633849745083A Review on Bayesian Deep Learning in Healthcare: Applications and ChallengesAbdullah A. Abdullah0https://orcid.org/0000-0002-7963-0843Masoud M. Hassan1https://orcid.org/0000-0002-6513-2739Yaseen T. Mustafa2https://orcid.org/0000-0003-0634-3170Computer Science Department, Faculty of Science, University of Zakho, Duhok, IraqComputer Science Department, Faculty of Science, University of Zakho, Duhok, IraqComputer Science Department, College of Science, Nawroz University, Duhok, IraqIn the last decade, Deep Learning (DL) has revolutionized the use of artificial intelligence, and it has been deployed in different fields of healthcare applications such as image processing, natural language processing, and signal processing. DL models have also been intensely used in different tasks of healthcare such as disease diagnostics and treatments. Deep learning techniques have surpassed other machine learning algorithms and proved to be the ultimate tools for many state-of-the-art applications. Despite all that success, classical deep learning has limitations and their models tend to be very confident about their predicted decisions because it does not know when it makes mistake. For the healthcare field, this limitation can have a negative impact on models predictions since almost all decisions regarding patients and diseases are sensitive. Therefore, Bayesian deep learning (BDL) has been developed to overcome these limitations. Unlike classical DL, BDL uses probability distributions for the model parameters, which makes it possible to estimate the whole uncertainties associated with the predicted outputs. In this regard, BDL offers a rigorous framework to quantify all sources of uncertainties in the model. This study reviews popular techniques of using Bayesian deep learning with their benefits and limitations. It also reviewed recent deep learning architecture such as Convolutional Neural Networks and Recurrent Neural Networks. In particular, the applications of Bayesian deep learning in healthcare have been discussed such as its use in medical imaging tasks, clinical signal processing, medical natural language processing, and electronic health records. Furthermore, this paper has covered the deployment of Bayesian deep learning for some of the widespread diseases. This paper has also discussed the fundamental research challenges and highlighted some research gaps in both the Bayesian deep learning and healthcare perspective.https://ieeexplore.ieee.org/document/9745083/Bayesian deep learningBayesian neural networksdeep learninghealthcareMCMCMC-dropout |
| spellingShingle | Abdullah A. Abdullah Masoud M. Hassan Yaseen T. Mustafa A Review on Bayesian Deep Learning in Healthcare: Applications and Challenges IEEE Access Bayesian deep learning Bayesian neural networks deep learning healthcare MCMC MC-dropout |
| title | A Review on Bayesian Deep Learning in Healthcare: Applications and Challenges |
| title_full | A Review on Bayesian Deep Learning in Healthcare: Applications and Challenges |
| title_fullStr | A Review on Bayesian Deep Learning in Healthcare: Applications and Challenges |
| title_full_unstemmed | A Review on Bayesian Deep Learning in Healthcare: Applications and Challenges |
| title_short | A Review on Bayesian Deep Learning in Healthcare: Applications and Challenges |
| title_sort | review on bayesian deep learning in healthcare applications and challenges |
| topic | Bayesian deep learning Bayesian neural networks deep learning healthcare MCMC MC-dropout |
| url | https://ieeexplore.ieee.org/document/9745083/ |
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