Artificial Intelligence (AI) and Internet of Things (IoT) in Healthcare: Opportunities and Challenges

Thakare, Vaibhav; Khire, Gauri; Kumbhar, Manisha

doi:10.1149/10701.7941ecst

Cited by 16 publications

(3 citation statements)

References 17 publications

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“…The increasing usage of wearables in our daily lives and healthcare systems will generate enormous and different data sources, eventually increasing the need for improved digital health records and a data-driven medical knowledge base. Along with this growing trend, there are more worries about ethical issues such as data privacy and security when using these devices (Azodo et al, 2020;Martinez-Martin et al, 2021;Thakare et al, 2022).…”

Section: Healthcare Wearables and The Influential Factorsmentioning

confidence: 99%

Digitalization in omnichannel healthcare supply chain businesses: The role of smart wearable devices

Chang

Doan

et al. 2023

Journal of Business Research

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Section: Healthcare Wearables and The Influential Factorsmentioning

confidence: 99%

Digitalization in omnichannel healthcare supply chain businesses: The role of smart wearable devices

Chang

Doan

et al. 2023

Journal of Business Research

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“…These programmes use various health apps and devices to provide health screening and health management services as health factors for older adults. 1 – 5 As the primary health programme evaluation of AI·IoT-OAHPs, our most significant concern is the effect on older adults’ health promotion programmes. However, there is little evaluation of the effectiveness of health screening and health management for health promotion in older adults through AI·IoT-OAHPs.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of health factors on artificial intelligence and the internet of things-based older adults healthcare programmes

Kim,

Kim

2024

DIGITAL HEALTH

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Objective This study evaluates Artificial intelligence and the Internet of Things-based older adults' healthcare programmes (AI·IoT-OAHPs), which offer non-face-to-face and face-to-face health management to older adults for health promotion. Methods The study involved 146 participants, adults over 60 who had registered in AI·IoT-OAHPs. This study assessed the health factors as the outcome of pre- and post-health screening and health management through AI·IoT-OAHPs for six months. Results Preand post-health screening and management through AI·IoT-OAHPs were evaluated as significant outcomes in 14 health factors. Notably, the benefits of post-cognitive function showed a twofold increase in older female adults through AI·IoT-OAHPs. Adults over 70 showed a fourfold increase in post-walking days, a threefold in post-dietary practice, and a twofold in post-cognitive function in the post-effects compared with pre via AI·IoT-OAHPs. Conclusions AI·IoT-OAHPs seem to be an effective program in the realm of face-to-face and non-face-to-face AI·IoT-based older adults' healthcare initiatives in the era of COVID-19. Consequently, the study suggests that AI·IoT-OAHPs contribute to the upgrade in health promotion of older adults. In future studies, the effectiveness of AI·IoT-OAHPs can be evaluated as a continuous project every year in the short term and every two years in the long term.

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“…The Fourth Industrial Revolution, characterized by the integration of advanced technologies, has the potential to significantly impact the development of nations in the Global South, particularly in the fields of Biomedical Engineering, digital health, and the pharmaceutical industry (Ajmera & Jain, 2019;Bali & Bali, 2020;Celesti, Amft, & Villari, 2019; de Melo e Castro e Melo & Araújo, 2020;Jayanthi, Iyyanki, Mothkuri, & Vadakattu, 2020;Kim & Han, 2020;Mahomed, 2018;Pang, Yuan, Zhang, & Packirisamy, 2018;Xiong, 2021). These technologies, such as Artificial Intelligence (AI), the Internet of Things (IoT), and big data analytics, offer unique opportunities for improving healthcare access, delivery, and outcomes in resource-constrained settings (Banerjee, Chakraborty, Kumar, & Biswas, 2020;Kaur, Garg, & Gupta, 2021;Poyner & Sherratt, 2019;Shafqat et al, 2020;Thakare, Khire, & Kumbhar, 2022;Turcu & Turcu, 2013). In Biomedical Engineering, these advancements can enhance the design and development of medical devices, diagnostics, and prosthetics, providing affordable and tailored solutions to address specific healthcare needs (Aguado, Grim, Rosales, Watson-Capps, & Anseth, 2018;Ghosh, Ning, Wang, & Kong, 2018;Primiceri et al, 2018).…”

Section: Introduction 1motivation and Problem Characterizationmentioning

confidence: 99%

Evaluation of molecular docking by deep learning and Random Forests: A hybrid approach based on pseudo-convolutions

Ferreira,

Feitosa,

Gomes

et al. 2023

Preprint

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Purpose: Molecular docking prediction plays a pivotal role in intelligent drug design, offering significant advantages in the development of antivirus medications and vaccines. By accurately evaluating the interactions between drug molecules and target proteins, researchers can effectively expedite the discovery and development of vital pharmaceutical solutions, aiding in the mitigation of epidemics, pandemics, and the urgent need for improved vaccines. Methods: We propose an intelligent hybrid architecture for estimating molecular docking between two proteins using deep networks based on pseudo-convolutions and Random Forests. As input, we Evaluation of molecular docking by deep learning and Random Forests used the sequences of characters representing the bases of each protein. Through two layers of pseudo-convolutions, these sequences are reorganized and, finally, represented as co-occurrence matrices, concatenated into a feature vector. To validate our proposal, we used a combination of the public datasets Affinity Benchmark 3 and Negatome 2. Results: The experimental results revealed that the hybrid architecture , comprising two layers of pseudo-convolution followed by a decision layer with an ensemble of 25 Random Forests, yielded average accuracy of 94%, AUC of 93%, sensitivity of 94%, and specificity of 78%. This demonstrates the feasibility of achieving robust estimates of molecular docking without relying on 3D molecule modeling. Conclusion: These findings the potential of rapidly estimating protein affinity, providing valuable insights into drug interactions, molecular dynamics, and facilitating the intelligent design of pharmaceuticals, encompassing vaccines and antiviral drugs. These advancements play a critical role in cost reduction related to laboratory analysis and expediting the timely delivery of solutions to both the market and society at large, particularly in the context of epidemic outbreaks, pandemics, and the urgent demand for supplementary vaccines.

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Artificial Intelligence (AI) and Internet of Things (IoT) in Healthcare: Opportunities and Challenges

Cited by 16 publications

References 17 publications

Digitalization in omnichannel healthcare supply chain businesses: The role of smart wearable devices

Digitalization in omnichannel healthcare supply chain businesses: The role of smart wearable devices

Evaluation of health factors on artificial intelligence and the internet of things-based older adults healthcare programmes

Evaluation of molecular docking by deep learning and Random Forests: A hybrid approach based on pseudo-convolutions

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