![]() Simultaneously, they could make significant hazards regarding inappropriate patient risk assessment, diagnostic inaccuracy, healing recommendations, privacy breaches, and other harms (Gouda et al. At the same time, AI applications have an enormous ability to work on patient outcomes. The AI commune must build an integrated best practice method for execution and safeguarding by incorporating active best practices of principled inclusivity, software growth, implementation science, and individual–workstation interaction. The challenges in the operational dynamism of AI technologies in healthcare systems are immeasurable despite the information that this is one of the most vital expansion areas in biomedical research (Kumar et al. However, numerous frameworks and principles facilitate summation and accomplish adequate data quantity for AI (Vasal et al. ![]() This enlargement in health care data struggles with the lack of well-organized mechanisms for integrating and reconciling these data ahead of their current silos. 2020 Kumar 2020), and exponential client state of information, have made a data-rich medical care biological system. Trends, such as the charge for putting away and directing realities, information collection through electronic well-being records (Minaee et al. 2018).ĪI algorithms must be trained on population-representative information to accomplish presentation levels essential for adaptable “accomplishment”. Researchers have effectively used deep learning classifications in diagnostic approaches to computing links between the built environment and obesity frequency (Bhatt et al. AI can also help to recognize the precise demographics or environmental areas where the frequency of illness or high-risk behaviors exists. 2017), and medical service experts for data creation and suggestions as well as disclosure of data for shared evaluation building. The models used are not limited to computerization, such as providing patients, “family” (Musleh et al. The presence of computerized reasoning (AI) as a method for improved medical services offers unprecedented occasions to recuperate patient and clinical group results, decrease costs, etc. The AI techniques are also most efficient in identifying the diagnosis of different types of diseases. AI methods from machine learning to deep learning assume a crucial function in numerous well-being-related domains, including improving new clinical systems, patient information and records, and treating various illnesses (Usyal et al. Digitized healthcare presents numerous opportunities for reducing human errors, improving clinical outcomes, tracking data over time, etc. Healthcare is shaping up in front of our eyes with advances in digital healthcare technologies such as artificial intelligence (AI), 3D printing, robotics, nanotechnology, etc. Based on the study of different articles on disease diagnosis, the results are also compared using various quality parameters such as prediction rate, accuracy, sensitivity, specificity, the area under curve precision, recall, and F1-score. ![]() Preferred reporting items for systematic reviews and Meta-Analysis guidelines are used to select the articles published up to October 2020 on the Web of Science, Scopus, Google Scholar, PubMed, Excerpta Medical Database, and Psychology Information for early prediction of distinct kinds of diseases using artificial intelligence-based techniques. We conducted an extensive survey including the used medical imaging dataset and their feature extraction and classification process for predictions. This article covers the comprehensive survey based on artificial intelligence techniques to diagnose numerous diseases such as Alzheimer, cancer, diabetes, chronic heart disease, tuberculosis, stroke and cerebrovascular, hypertension, skin, and liver disease. Furthermore, artificial intelligence primarily enhanced the infirmary experience and sped up preparing patients to continue their rehabilitation at home. Numerous medical data sources are required to perfectly diagnose diseases using artificial intelligence techniques, such as ultrasound, magnetic resonance imaging, mammography, genomics, computed tomography scan, etc. Artificial intelligence techniques ranging from machine learning to deep learning are prevalent in healthcare for disease diagnosis, drug discovery, and patient risk identification. Artificial intelligence can assist providers in a variety of patient care and intelligent health systems.
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