On the use of patient-reported measures in digital medicine to increase healthcare resilience

Capriulo, Matteo; Pizzolla, Ilaria; Briganti, Giovanni

doi:10.1016/b978-0-443-21598-8.00019-1

Cited by 3 publications

(1 citation statement)

References 90 publications

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“…In turn, in the area of medicine, DTs offer opportunities ranging from research on mechanisms related to various diseases and isolating disease predictors to optimization of health outcomes [18]. However, the human body and its parts are considered to be more complex than objects in engineering and manufacturing [19,20]. For example, it has also been shown on the individual level that mathematical models can be calibrated based on patient-specific data to predict tumor response dynamics [21][22][23][24].…”

Section: Digital Twins In Cardiology-the Heart Digital Twinmentioning

confidence: 99%

Cardiac Healthcare Digital Twins Supported by Artificial Intelligence-Based Algorithms and Extended Reality—A Systematic Review

Rudnicka,

Proniewska,

Perkins

et al. 2024

Electronics

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Recently, significant efforts have been made to create Health Digital Twins (HDTs), Digital Twins for clinical applications. Heart modeling is one of the fastest-growing fields, which favors the effective application of HDTs. The clinical application of HDTs will be increasingly widespread in the future of healthcare services and has huge potential to form part of mainstream medicine. However, it requires the development of both models and algorithms for the analysis of medical data, and advances in Artificial Intelligence (AI)-based algorithms have already revolutionized image segmentation processes. Precise segmentation of lesions may contribute to an efficient diagnostics process and a more effective selection of targeted therapy. In this systematic review, a brief overview of recent achievements in HDT technologies in the field of cardiology, including interventional cardiology, was conducted. HDTs were studied taking into account the application of Extended Reality (XR) and AI, as well as data security, technical risks, and ethics-related issues. Special emphasis was put on automatic segmentation issues. In this study, 253 literature sources were taken into account. It appears that improvements in data processing will focus on automatic segmentation of medical imaging in addition to three-dimensional (3D) pictures to reconstruct the anatomy of the heart and torso that can be displayed in XR-based devices. This will contribute to the development of effective heart diagnostics. The combination of AI, XR, and an HDT-based solution will help to avoid technical errors and serve as a universal methodology in the development of personalized cardiology. Additionally, we describe potential applications, limitations, and further research directions.

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Section: Digital Twins In Cardiology-the Heart Digital Twinmentioning

confidence: 99%

Cardiac Healthcare Digital Twins Supported by Artificial Intelligence-Based Algorithms and Extended Reality—A Systematic Review

Rudnicka,

Proniewska,

Perkins

et al. 2024

Electronics

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Cognitive Digital Twins for Improving Security in IT-OT Enabled Healthcare Applications

Pirbhulal,

Chockalingam,

Abie

et al. 2024

Lecture Notes in Computer Science

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Industry 4.0

Shah,

Jhanjhi,

Ujjan

2024

Advances in Medical Technologies and Clinical Practice

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The primary objective of this chapter is to examine the AI applications for healthcare 4.0. Using a wide range of contemporary technologies, such as digitization, artificial intelligence, user response data (ergonomics), human psychology, the internet of things, machine learning, big data mining, and augmented reality, one of the great success stories of our day is healthcare. Worldwide life expectancy has increased due to the tremendous advancements in medical research. But when people live longer, healthcare systems must deal with more people needing their services, more money spent on them, and a staff that finds it more challenging to care for patients. A healthy, productive society depends heavily on the healthcare industry, making it one of the most critical industries in the larger big data environment. Artificial intelligence (AI), which builds on automation, has the potential to transform healthcare and assist in addressing some of the issues mentioned above. AI can support healthcare professionals, including physicians and nurses, in their day-to-day jobs. Artificial intelligence (AI) can improve patient outcomes by enhancing the quality of life and preventive care and producing more accurate diagnoses and treatment regimens. This book provides an overview of the most recent advancements in artificial intelligence (AI) applications in biomedicine, encompassing pharmaceutical processing, disease diagnosis, patient monitoring, biomedical information, and biomedical research. A summary of the most recent developments in the use of AI in healthcare is also provided, along with a road map for creating safe, dependable, and efficient AI systems and a discussion of potential future directions for AI-assisted healthcare systems. Numerous uses of AI exist in the medical field. Healthcare 4.0 has brought about a paradigm shift in the healthcare industry, drawing inspiration from Industry 4.0. Therefore, how the digital revolution in healthcare will affect the quality of medical care is still being determined. This study results will help the new researchers and healthcare institutions.

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On the use of patient-reported measures in digital medicine to increase healthcare resilience

Cited by 3 publications

References 90 publications

Cardiac Healthcare Digital Twins Supported by Artificial Intelligence-Based Algorithms and Extended Reality—A Systematic Review

Cardiac Healthcare Digital Twins Supported by Artificial Intelligence-Based Algorithms and Extended Reality—A Systematic Review

Cognitive Digital Twins for Improving Security in IT-OT Enabled Healthcare Applications

Industry 4.0

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