How AI Helps in Early Detection of Diseases

With the potential to extract meaningful relationships from a data set, AI can be used in many clinical scenarios to diagnose, treat, and predict the outcome. 

FERMONT, CA: When it comes to making image-based medical diagnoses, artificial intelligence (AI) is on par with human experts. Any disease's early diagnosis is very effective in reducing disease compilations. It helps to decide the protocols of treatment. There are different protocols for diagnosis and treatment that prove that artificial intelligence is a health care boon. AI's purpose is to make computers more effective in solving healthcare problems by interpreting the data collected by the diagnosis of various diseases using machines. This method seems promising in cancer-to-eye disease diagnosis.    

An early breast cancer diagnosis is of paramount importance. AI has developed systems that interpret mammogram data efficiently, intuitively convert patient charts into diagnostic information, which predicts the risk of breast cancer accurately. These days, Triple Negative Breast Cancer Database Intelligent Systems and Genes to Systems Breast Cancer Database (G2SBC) are being used to identify breast cancer. G2SBC is a tool that integrates data into breast cancer cells on gene transcripts and protein altered. This application is a structural biology-oriented approach to breast cancer information integration. ANN is one of the best artificial intelligence techniques to use non-linear statistical data modeling tools for common data mining tasks.

Alzheimer's Disease (AD) is the leading cause of dementia, characterized by significant memory loss, multiple cognitive function impairment, and behavioral changes affecting a large global population. The application of AI can help in the early detection of this incurable disease. Using various automated systems and tools such as brain-computer interfaces (BCIs), arterial spin labeling-magnetic resonance imaging (ASL-MRI), electroencephalogram (EEG), positron emission tomography (PET), single-photon emission computed tomography (SPECT) scans and various algorithms helps minimize errors, early diagnosis and control disease progression. Using multiple AI algorithms in brain MRI scans provides a foundation for distinguishing between the early stages of Alzheimer's disease. Brain-computer interfaces (BCIs) help AD patients express simple thoughts by transmitting brain commands to an external device. Arterial Spin Labeling (ASL) imaging is a promising functional biomarker that creates maps of perfusion that recognize patterns of blood perfusion in different regions of the brain that help detect different stages of AD.

Managing diabetes and its complication is a challenging task, as several variables regulate the level of blood sugar. The use of AI in the diagnosis or monitoring of diabetes can improve the quality of life of the patient. The computer-assisted diagnosis, decision support systems, expert systems, and software implementation can help physicians mitigate the variance intra or inter-observer. AI technology enables high accuracy and high-speed analysis of tests.   

Early detection of various diseases by AI results in early initiation of the treatment, which delays the progression of the disease, improves the quality of life of the patient, and further decreases the economic burden involved in the management of healthcare. This intelligent AI approach provides the right direction for research in chronic disease diagnostics.

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