1 Department of Engineering, Faculty of Engineering, Mathematics and Computer Science, University of Wolverhampton, UK.
2 Department of Computer Science, Maharaja Surajmal Institute of Technology, New Delhi – India.
3 Department of Marketing and Sales, University School of Management, University of Lille, France.
4 Division of Clinical Research, Zydus Pharmaceutical Division of Clinical Research and Development, New Jersey, USA.
5 Department of Sciences, University of Delhi, New Delhi, India.
Research Article
World Journal of Advanced Research and Reviews, 2024, 23(03), 707-720
Received on 20 July 2024; revised on 30 August 2024; accepted on 02 September 2024
Personalised healthcare, underpinned by a deep understanding of individual patient variability, demands innovative solutions. Machine Learning (ML) models offer a promising avenue to achieve this by facilitating the development of enhanced Digital Twins (DTs). This research proposes a novel framework for creating DTs tailored to individual patients, considering not only physical attributes but also the intricate interplay of social, and biological factors. By capturing this comprehensive patient profile, ML-powered DTs have the potential to revolutionise healthcare by enabling predictive, preventive, and personalised care strategies, ultimately leading to improved patient outcomes and the development of a marketable, trust-worthy product. Current healthcare solutions lack personalisation, often failing to consider individual differences in disease presentation and response to treatment. Traditional DTs in healthcare often adopt a disease-centric or organ-specific approach, thereby restricting their capacity to deliver comprehensive, personalised care. To address this limitation, we propose a holistic Artificial Intelligence (AI) framework centered on ML models. Initially focusing on diabetes, our research aims to enhance diagnosis, treatment, and predictive capabilities through personalised insights, thereby optimising patient outcomes and care management. The benefits identified with our ML model are early disease prevention and risk stratification, optimised treatment planning and therapy selection, enhanced patient-physician communication and shared decision-making, reduced healthcare costs and improved resource allocation. Our models are designed to optimise patient care while prioritising safety and societal benefit. To ensure this, we have conducted a thorough assessment of potential ethical implications. Key challenges identified include data privacy and security, algorithmic bias, diagnostic accuracy, data interoperability and standardisation, integration with existing healthcare systems, ethical management of sensitive patient data, refinement of ML methodologies, addressing legal and ethical AI challenges, and suggesting robust ethical guidelines. A comprehensive evaluation of accuracy, reliability, and associated risks will be conducted prior to full-scale integration into the healthcare ecosystem to establish a robust ethical framework for our research models.
Personal Digital Twins (PDT); Digital Twin (DT); Machine Learning (ML), Artificial Intelligence (AI), Base Diabetes Management using PDTs; Ethical implications.
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Kiran Gulia, Izhar Ahmad Hamdan, Nilay Datta, Yuvraj Gupta, Phool Kumar, Amita Yadav, Sandeep Mitten and Rajiv Kumar. Machine learning models for personalised healthcare on marketable generative-AI with ethical implications. World Journal of Advanced Research and Reviews, 2024, 23(3), 707-720. Article DOI: https://doi.org/10.30574/wjarr.2024.23.3.2660