MEDICAL FIELD:Perspective of Artificial Intelligence in Disease Diagnosis: A Review of Current and Future Endeavours in the Medical Field.
Perspective of Artificial Intelligence in Disease Diagnosis: A Review of Current and Future Endeavours in the Medical Field
Artificial intelligence (AI) has demonstrated significant promise for the present and future diagnosis of diseases. At the moment, AI-powered diagnostic technologies can help physicians decipher medical pictures like X-rays, magnetic resonance imaging, and computed tomography scans, resulting in quicker and more precise diagnoses. In order to make a prospective diagnosis, AI algorithms may also examine patient information, symptoms, and medical background. The application of AI in disease diagnosis is anticipated to grow as the field develops. In the future, AI may be used to find patterns in enormous volumes of medical data, aiding in disease prediction and prevention before symptoms appear. Additionally, by combining genetic data, lifestyle data, and environmental variables, AI may help in the diagnosis of complicated diseases. It is crucial to remember that while AI can be a powerful tool, it cannot take the place of qualified medical personnel. Instead, AI ought to support and improve diagnostic procedures, enhancing patient care and healthcare results. Future research and the use of AI for disease diagnosis must take ethical issues, data protection, and ongoing model validation into account.
Medical diagnostics refers to the systematic evaluation of medical problems or diseases through the examination and interpretation of symptoms, medical history, and test results. The primary objective of medical diagnostics is to ascertain the underlying etiology of a medical condition and provide a precise diagnosis, thereby facilitating the administration of suitable therapeutic interventions. The diagnostic process may involve the utilization of several techniques, such as imaging modalities including X-rays, MRIs, and CT scans, as well as blood tests and biopsy procedures. The results of these tests assist healthcare providers in determining the optimal treatment approach for their patients. Medical diagnostics serve multiple purposes, including monitoring the evolution of a condition, assessing the effectiveness of therapy, detecting potential health concerns at an early stage, and facilitating the identification of medical disorders. One potential optimal intelligent approach that could potentially enhance diagnostic outcomes, drawing upon a range of findings from images, signals, textual representations, and other sources, is the use of multimodal patient data diversity. The utilization of multimodal data in healthcare practice enables healthcare providers to enhance their ability to effectively treat and manage chronic diseases by monitoring the progression of a condition over an extended period. Healthcare professionals utilizing Explainable XAI can identify possible health issues sooner, before they become significant and perhaps fatal, by leveraging multimodal medical data [37]. Additionally, AI-powered Clinical Decision Support Systems (CDSSs) might offer in-the-moment help and guidance to make better judgments on patient care. Healthcare practitioners may concentrate on more complicated patient care by using XAI tools to automate common procedures.
