International Journal of Science, Engineering and TechnologyAuthors: Samarth Nayak, Anurag Sharma, Tanishq Raj, Suvi Yadav, Professor Keerthi Mohan
Abstract: The increasing complexity of the healthcare and legal sectors stems from the growing number of fraudulent insurance claims, diagnostic inaccuracies, and the rising incidence of malpractice litigation. These issues have serious implications for patient trust, financial stability, and legal accountability These challenges demand innovative solutions that go beyond traditional, manual approaches, which are often time-consuming, error-prone, and inefficient at scale. Recent advancements in Artificial Intelligence (AI) have offered promising tools to address these persistent issues. This paper presents a comprehensive review of recent literature focused on the application of Machine Learning (ML), Deep Learning (DL), and Natural Language Processing (NLP) in healthcare and legal contexts. Specifically, ML models like Random Forest and XGBoost have demonstrated effectiveness in detecting abnormal patterns in insurance claims, helping to reduce fraud. Similarly, DL techniques, particularly Convolutional Neural Networks (CNNs), have significantly enhanced diagnostic accuracy in fields such as radiology and pathology. Meanwhile, NLP models like BERT have enabled efficient extraction and interpretation of complex legal language, thus streamlining legal analytics and document processing. Despite these technological advancements, notable gaps still persist in the literature. There is a limited number of interdisciplinary systems that integrate healthcare and legal AI applications cohesively. Additionally, the majority of studies lack real-time implementation capabilities and fail to emphasize critical aspects like explainability, fairness, and compliance with legal and ethical standards. This review not only synthesizes key findings but also critically analyses existing limitations and proposes future directions. These include developing real-time AI systems, enhancing interpretability of black-box models, and fostering collaboration between AI developers, medical professionals, and legal experts. Such advancements could revolutionize the way these high-stakes sectors operate, ensuring better outcomes for both patients and institutions. This paper reviews and synthesizes recent research across these three areas to identify how AI is being applied, where it is succeeding, and where further work is needed. These include building interdisciplinary AI frameworks, deploying explainable and ethically aligned models, and enabling real-time intelligent systems that are both robust and compliant. The convergence of AI with healthcare and legal expertise holds the potential to revolutionize both industries by enhancing decision-making accuracy, reducing administrative burden, and ultimately improving patient and institutional outcomes.
