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Artificial Intelligence in Medical Diagnostics and Healthcare

Authors:

Ms.Reshma R

Asst.Prof.,Department of Computer Science, Sri Krishna Arts and Science College, Coimbatore. Email- reshmar@skasc.ac.in

UG Student, Department of Computer Science, Sri Krishna Arts and Science College, Coimbatore. Email – eczhivince10@gmail.com

Abstract

Artificial Intelligence (AI) is revolutionizing healthcare by transforming traditional diagnostic processes, enhancing treatment planning, and improving overall patient care. AI-driven technologies, including machine learning (ML), deep learning (DL), natural language processing (NLP), and fuzzy logic, are being increasingly integrated into clinical settings to assist healthcare professionals in making more accurate and timely decisions. AI has demonstrated its potential to surpass human expertise in medical imaging interpretation, predictive analytics, and personalized medicine by analyzing large datasets, identifying patterns, and offering data-driven insights.

The integration of AI into medical diagnostics has led to significant advancements in disease detection, particularly in fields such as radiology, pathology, and genomics. AI-powered diagnostic tools can rapidly process and interpret imaging data, reducing diagnostic errors and enabling early detection of diseases such as cancer, cardiovascular disorders, and neurological conditions. Furthermore, AI is playing a critical role in decision support systems, allowing clinicians to develop tailored treatment plans based on individual patient characteristics, genetic information, and historical health records. By leveraging AI’s capabilities, healthcare providers can optimize medication dosages, predict patient responses to therapies, and improve overall treatment efficacy.

Beyond individual patient care, AI is also making strides in population health management through predictive analytics. AI algorithms can analyze vast amounts of patient data to identify at-risk populations, detect emerging disease outbreaks, and allocate healthcare resources efficiently. By integrating AI into epidemiology and public health surveillance, healthcare systems can proactively address potential health crises and enhance disease prevention efforts. However, while AI presents numerous benefits, its implementation also brings forth several challenges, including concerns regarding data privacy, algorithmic bias, ethical considerations, and the need for regulatory oversight.

This review explores the transformative impact of AI in medical diagnostics and healthcare, detailing its role in disease identification, treatment optimization, and population health management. It further examines the challenges and ethical implications associated with AI adoption in clinical practice. As AI technology continues to evolve, ongoing research and collaboration among healthcare professionals, AI developers, and policymakers will be essential to ensure the responsible and equitable integration of AI-driven solutions in healthcare. Future advancements must focus on refining AI algorithms, improving data security, and establishing standardized guidelines to maximize AI’s potential while safeguarding patient well-being.

KEYWORDS: Artificial Intelligence, Machine Learning, Medical Diagnostics, Predictive Analytics, Healthcare, Clinical Decision Support, Fuzzy Logic, Deep Learning, Personalized Medicine.

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