Revolutionising Tumour Diagnosis: How Clinical Application of Artificial Intelligence and Machine Learning Enhances Accuracy and Efficiency
subject: Artificial intelligence; Machine learning; Tumour diagnosis
description: This research paper examines the transformative influence of Artificial Intelligence (AI) and Machine Learning (ML) on tumour diagnosis within clinical settings. The advent of AI and ML technologies has revolutionised the field of oncology, offering the unprecedented potential for more accurate, timely, and personalised cancer detection. By leveraging vast datasets of medical images, genomic information, and patient records, these intelligent systems enable the early identification of tumours, classification of cancer types, and prediction of patient outcomes with remarkable precision. This paper delves into the mechanisms through which AI and ML algorithms analyse complex data, highlighting their ability to detect subtle patterns and anomalies that may escape human perception. Moreover, we examine the successful integration of these technologies into clinical workflows, their potential to reduce diagnostic errors, and the implications for patient care and outcomes. As AI and ML continue to emerge, the synergy between technology and clinical expertise promises to enhance tumour diagnosis, ultimately contributing to more effective and personalised cancer treatments. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
creator: Malathy V.; Pasupulla A.P.; Srivastava D.; Hymavathi J.; Pandian P.M.; Acharjee P.B.
source: Smart Innovation, Systems and Technologies, Vol-390, pp. 141-151.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2024-01-01
rights: Restricted Access
relation: ISSN: 21903018; ISBN: 978-981972715-5
format: Online
language: English
type: Conference paper
coverage: Malathy V., School of Engineering, SR University, Telangana, Warangal, India; Pasupulla A.P., Oral and Maxillofacial Pathology, School of Medicine, College of Health Sciences, Wachemo University, Hosanna, Ethiopia; Srivastava D., Department of Biotechnology and Chemical Engineering, Manipal University, Jaipur, India; Hymavathi J., Department of Computer Science and Engineering, KLEF, Andhra Pradesh, Vaddeswaram, India; Pandian P.M., Department of Chemistry, Saveetha Engineering College, Chennai, Thandalam, India; Acharjee P.B., Computer Science, CHRIST University Pune Lavasa Campus, Lavasa, India