Artificial Intelligence-Based Technological Advancements in Clinical Healthcare Applications: A Systematic Review

Mishra, Shivansh; Abbas, Mohamed; Jindal, Kanak; Narayan, Jyotindra; Dwivedy, Santosha K.

doi:10.1007/978-981-16-9455-4_11

Cited by 4 publications

(2 citation statements)

References 76 publications

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“…mitigate this issue (Mishra et al, 2022). Additionally, determining the optimal architecture and hyperparameters for LSTM models can be challenging, as the performance may vary based on factors such as the number of layers, hidden units, and sequence lengths.…”

Section: Long Short-term Memorymentioning

confidence: 99%

Trends in using deep learning algorithms in biomedical prediction systems

Wang,

Liu,

Wang

2023

Front. Neurosci.

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In the domain of using DL-based methods in medical and healthcare prediction systems, the utilization of state-of-the-art deep learning (DL) methodologies assumes paramount significance. DL has attained remarkable achievements across diverse domains, rendering its efficacy particularly noteworthy in this context. The integration of DL with health and medical prediction systems enables real-time analysis of vast and intricate datasets, yielding insights that significantly enhance healthcare outcomes and operational efficiency in the industry. This comprehensive literature review systematically investigates the latest DL solutions for the challenges encountered in medical healthcare, with a specific emphasis on DL applications in the medical domain. By categorizing cutting-edge DL approaches into distinct categories, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), long short-term memory (LSTM) models, support vector machine (SVM), and hybrid models, this study delves into their underlying principles, merits, limitations, methodologies, simulation environments, and datasets. Notably, the majority of the scrutinized articles were published in 2022, underscoring the contemporaneous nature of the research. Moreover, this review accentuates the forefront advancements in DL techniques and their practical applications within the realm of medical prediction systems, while simultaneously addressing the challenges that hinder the widespread implementation of DL in image segmentation within the medical healthcare domains. These discerned insights serve as compelling impetuses for future studies aimed at the progressive advancement of using DL-based methods in medical and health prediction systems. The evaluation metrics employed across the reviewed articles encompass a broad spectrum of features, encompassing accuracy, precision, specificity, F-score, adoptability, adaptability, and scalability.

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Section: Long Short-term Memorymentioning

confidence: 99%

Trends in using deep learning algorithms in biomedical prediction systems

Wang,

Liu,

Wang

2023

Front. Neurosci.

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“…Artificial intelligence technology can provide various support for road routing and traffic congestion management, and can effectively support intelligent transportation systems (22). Artificial intelligence can also be applied to several fields in the medical field, such as neural disease prediction and modeling, bioinformatics, surgery, physical rehabilitation, medical robot and hospital clinical data management (23). The most basic is the grassroots medical institutions, which are the first line of defense for the health of grass-roots residents.…”

Section: Related Workmentioning

confidence: 99%

Application of random forest based on semi-automatic parameter adjustment for optimization of anti-breast cancer drugs

Zhang

Kong

et al. 2022

Front. Oncol.

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The optimization of drug properties in the process of cancer drug development is very important to save research and development time and cost. In order to make the anti-breast cancer drug candidates with good biological activity, this paper collected 1974 compounds, firstly, the top 20 molecular descriptors that have the most influence on biological activity were screened by using XGBoost-based data feature selection; secondly, on this basis, take pIC50 values as feature data and use a variety of machine learning algorithms to compare, soas to select a most suitable algorithm to predict the IC50 and pIC50 values. It is preliminarily found that the effects of Random Forest, XGBoost and Gradient-enhanced algorithms are good and have little difference, and the Support vector machine is the worst. Then, using the Semi-automatic parameter adjustment method to adjust the parameters of Random Forest, XGBoost and Gradient-enhanced algorithms to find the optimal parameters. It is found that the Random Forest algorithm has high accuracy and excellent anti over fitting, and the algorithm is stable. Its prediction accuracy is 0.745. Finally, the accuracy of the results is verified by training the model with the preliminarily selected data, which provides an innovative solution for the optimization of the properties of anti- breast cancer drugs, and can provide better support for the early research and development of anti-breast cancer drugs.

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Glove-Net: Enhancing Grasp Classification with Multisensory Data and Deep Learning Approach

Pratap,

Narayan,

Hatta

et al. 2024

Sensors

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Grasp classification is pivotal for understanding human interactions with objects, with wide-ranging applications in robotics, prosthetics, and rehabilitation. This study introduces a novel methodology utilizing a multisensory data glove to capture intricate grasp dynamics, including finger posture bending angles and fingertip forces. Our dataset comprises data collected from 10 participants engaging in grasp trials with 24 objects using the YCB object set. We evaluate classification performance under three scenarios: utilizing grasp posture alone, utilizing grasp force alone, and combining both modalities. We propose Glove-Net, a hybrid CNN-BiLSTM architecture for classifying grasp patterns within our dataset, aiming to harness the unique advantages offered by both CNNs and BiLSTM networks. This model seamlessly integrates CNNs’ spatial feature extraction capabilities with the temporal sequence learning strengths inherent in BiLSTM networks, effectively addressing the intricate dependencies present within our grasping data. Our study includes findings from an extensive ablation study aimed at optimizing model configurations and hyperparameters. We quantify and compare the classification accuracy across these scenarios: CNN achieved 88.09%, 69.38%, and 93.51% testing accuracies for posture-only, force-only, and combined data, respectively. LSTM exhibited accuracies of 86.02%, 70.52%, and 92.19% for the same scenarios. Notably, the hybrid CNN-BiLSTM proposed model demonstrated superior performance with accuracies of 90.83%, 73.12%, and 98.75% across the respective scenarios. Through rigorous numerical experimentation, our results underscore the significance of multimodal grasp classification and highlight the efficacy of the proposed hybrid Glove-Net architectures in leveraging multisensory data for precise grasp recognition. These insights advance understanding of human–machine interaction and hold promise for diverse real-world applications.

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Artificial Intelligence-Based Technological Advancements in Clinical Healthcare Applications: A Systematic Review

Cited by 4 publications

References 76 publications

Trends in using deep learning algorithms in biomedical prediction systems

Trends in using deep learning algorithms in biomedical prediction systems

Application of random forest based on semi-automatic parameter adjustment for optimization of anti-breast cancer drugs

Glove-Net: Enhancing Grasp Classification with Multisensory Data and Deep Learning Approach

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