Machine Learning Applications in Medical Information Science for Automated Diagnosis and Treatment Plans

Mohan Garg, Prabhjot Kaur, Joginder Joginder, Manashree Mane, Kunal Meher, Tapasmini Sahoo and Vundela Swathi

Seminars in Medical Writing and Education, 2024, vol. 3, 515

Abstract: Machine learning (ML) simplifies diagnostic and treatment planning automation in medical computer science. This is profoundly altering the healthcare industry. Thanks to rapid development in machine learning algorithms and their capacity to evaluate large, complicated information, medical decision-making has become far more accurate and simplified. By searching for trends in patient data including medical records, diagnostic images, and genetic information that might not be clear-cut for human specialists, machine learning models might assist clinic-based clinicians in These models not only enable doctors to identify issues early on but also enable them to create tailored treatment strategies for every patient's requirement. In managing repetitious tasks, forecasting how illnesses would worsen, and recommending therapies, ML techniques like supervised learning, unsupervised learning, and deep learning have also demonstrated astonishing outcomes. Dealing with chronic illnesses, cancer, and emergency care scenarios calls specifically for these abilities. Furthermore improving decision support systems driven by ML helps to reduce medical errors and maximise the resources of healthcare systems. Including machine learning models into medical information systems might help to improve patient outcomes, simplify tasks, and reduce costs as healthcare keeps becoming digital. However, societal issues, data security, and the necessity of legal structures have to be considered to guarantee that ML technologies are applied responsibly in the medical field.

Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:dbk:medicw:v:3:y:2024:i::p:515:id:515

DOI: 10.56294/mw2024515

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