American Journal of Advanced Multidisciplinary Research and Innovation

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The Role of Artificial Intelligence in Modern Healthcare Diagnosis and Clinical Decision Support

Author(s) Jennifer Tour Chayes
Country United States
Abstract Artificial Intelligence (AI) has emerged as one of the most transformative technologies in modern healthcare, revolutionising disease diagnosis, clinical decision support, medical imaging, personalised treatment planning, and hospital management. The increasing availability of electronic health records (EHRs), medical imaging, wearable devices, and real-time patient monitoring systems has enabled AI algorithms to analyse complex healthcare data with remarkable speed and accuracy. Traditional diagnostic approaches often face challenges such as delayed diagnosis, diagnostic variability, increasing patient loads, and shortages of healthcare professionals. AI-driven clinical decision support systems (CDSS) provide healthcare practitioners with intelligent recommendations, predictive analytics, and evidence-based insights that improve diagnostic precision and patient outcomes.
This study presents a comprehensive analysis of the role of Artificial Intelligence in modern healthcare diagnosis and clinical decision support. A qualitative analytical research methodology based on secondary data is employed to examine the applications of machine learning, deep learning, natural language processing (NLP), computer vision, and explainable artificial intelligence (XAI) in healthcare. The study investigates AI-assisted disease diagnosis, medical image interpretation, predictive analytics, personalised medicine, remote patient monitoring, and intelligent hospital management.
The findings indicate that AI significantly enhances diagnostic accuracy, reduces medical errors, supports early disease detection, improves clinical workflow efficiency, and facilitates personalised healthcare delivery. AI-powered systems demonstrate high performance in analysing radiological images, pathology slides, cardiovascular signals, and genomic information. Clinical decision support systems assist physicians by integrating patient data with evidence-based medical knowledge to recommend diagnostic and treatment options while reducing cognitive workload.
Despite these benefits, challenges remain regarding data privacy, algorithm transparency, regulatory compliance, interoperability, ethical concerns, bias in AI models, and clinician acceptance. Future research should focus on explainable AI, federated learning, multimodal healthcare analytics, digital twins for personalised medicine, and trustworthy AI governance frameworks.
The study concludes that Artificial Intelligence has become an indispensable component of modern healthcare diagnosis and clinical decision support, offering significant opportunities to improve healthcare quality, operational efficiency, patient safety, and global healthcare accessibility.
Keywords Artificial Intelligence, Healthcare, Clinical Decision Support, Medical Diagnosis, Machine Learning, Deep Learning, Medical Imaging, Explainable AI, Digital Health.
Field Engineering
Published In Volume 1, Issue 5, September-October 2019
Published On 2019-09-16
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