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To consistently assess a patient’s internal and external wellness and diagnose chronic conditions like cancer, Alzheimer’s disease, and cardiovascular disease, wearable sensing devices are being used. Wearable technologies and networking websites have become incredibly common in the medical sector in recent times. The condition of a patient’s healt...
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... Furthermore, to improve the reliability of the results, it is recommended to use a larger dataset that is divided using cross-validation to simulate external validity. The findings should also be applied to various clinical applications and illnesses, including different types of cancers [79][80][81][82]. Moreover, it is essential to contemplate why disease diagnostics performed in clinical settings have not been extensively integrated with scientific evidence. ...
Artificial intelligence (AI) is rapidly advancing and significantly impacting clinical care and treatment. Machine learning and deep learning, as core digital AI technologies, are being extensively applied to support diagnosis and treatment. With the progress of digital health-care technologies such as AI, bioprinting, robotics, and nanotechnology, the health-care landscape is transforming. Digitization in health-care offers various opportunities, including reducing human error rates, improving clinical outcomes, and monitoring longitudinal data. AI techniques, ranging from learning algorithms to deep learning, play a critical role in several health-care domains, such as the development of new health-care systems, improvement of patient information and records, and treatment of various ailments. AI has emerged as a powerful scientific tool, capable of processing and analyzing vast amounts of data to support decision-making. Numerous studies have demonstrated that AI can perform on par with or outperform humans in crucial medical tasks, including disease detection. However, despite its potential to revolutionize health care, ethical considerations must be carefully addressed before implementing AI systems and making informed decisions about their usage. Researchers have utilized various AI-based approaches, including deep and machine learning models, to identify diseases that require early diagnosis, such as skin, liver, heart, and Alzheimer’s diseases. Consequently, related work presents different methods for disease diagnosis along with their respective levels of accuracy, including the Boltzmann machine, K nearest neighbor, support vector machine, decision tree, logistic regression, fuzzy logic, and artificial neural network. While AI holds immense promise, it is likely to take decades before it completely replaces humans in various medical operations.
... Furthermore, to improve the reliability of the results, it is recommended to use a larger dataset that is divided using cross-validation to simulate external validity. The findings should also be applied to various clinical applications and illnesses, including different types of cancers [79][80][81][82]. Moreover, it is essential to contemplate why disease diagnostics performed in clinical settings have not been extensively integrated with scientific evidence. ...
Artificial intelligence (AI) is rapidly advancing and significantly impacting clinical care and treatment. Machine learning and deep learning, as core
digital AI technologies, are being extensively applied to support diagnosis and treatment. With the progress of digital health-care technologies such
as AI, bioprinting, robotics, and nanotechnology, the health-care landscape is transforming. Digitization in health-care offers various opportunities,
including reducing human error rates, improving clinical outcomes, and monitoring longitudinal data. AI techniques, ranging from learning
algorithms to deep learning, play a critical role in several health-care domains, such as the development of new health-care systems, improvement of
patient information and records, and treatment of various ailments. AI has emerged as a powerful scientific tool, capable of processing and analyzing
vast amounts of data to support decision-making. Numerous studies have demonstrated that AI can perform on par with or outperform humans in
crucial medical tasks, including disease detection. However, despite its potential to revolutionize health care, ethical considerations must be carefully
addressed before implementing AI systems and making informed decisions about their usage. Researchers have utilized various AI-based approaches,
including deep and machine learning models, to identify diseases that require early diagnosis, such as skin, liver, heart, and Alzheimer’s diseases.
Consequently, related work presents different methods for disease diagnosis along with their respective levels of accuracy, including the Boltzmann
machine, K nearest neighbor, support vector machine, decision tree, logistic regression, fuzzy logic, and artificial neural network. While AI holds
immense promise, it is likely to take decades before it completely replaces humans in various medical operations.
... Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: ...
[This retracts the article DOI: 10.1155/2022/4334852.].
