E-Health Startups’ Framework for Value Creation and Capture: Some Insights from Systematic Review

Chakraborty, Imon; Ilavarasan, P. Vigneswara; Edirippulige, Sisira

doi:10.1007/978-981-19-2350-0_13

Proceedings of the International Conference on Cognitive and Intelligent Computing

2022

DOI: 10.1007/978-981-19-2350-0_13

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E-Health Startups’ Framework for Value Creation and Capture: Some Insights from Systematic Review

Imon Chakraborty

P. Vigneswara Ilavarasan

Sisira Edirippulige

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Machine Learning in E-Health and Digital Healthcare

Sethuramalingam,

G. Nadakinamani,

Sumathy

et al. 2023

Handbook of Research on AI and ML for Intelligent Machines and Systems

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Machine learning is revolutionizing healthcare by offering innovative solutions to complex challenges. This chapter explores the practical strategies, ethical considerations, and real-world applications of machine learning in the healthcare domain. It delves into data collection and management, model development, integration with existing systems, and the importance of interdisciplinary collaboration. The chapter also discusses the ethical dimensions of healthcare AI, such as data privacy, bias mitigation, and regulatory compliance. Real-world case studies highlight the impact of machine learning on early disease detection, drug discovery, and precision medicine. The chapter concludes by examining future trends, including emerging technologies like quantum computing, nanomedicine, and the growing role of AI in drug discovery and genomic medicine. As machine learning continues to reshape healthcare, understanding these practical strategies and ethical considerations is essential for optimizing patient care and advancing the healthcare industry.

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Machine Learning in E-Health and Digital Healthcare

Sethuramalingam,

G. Nadakinamani,

Sumathy

et al. 2023

Handbook of Research on AI and ML for Intelligent Machines and Systems

View full text Add to dashboard Cite

show abstract

Fog Computing-Integrated ML-Based Framework and Solutions for Intelligent Systems

Pitchai,

Guru,

Gandhi

et al. 2024

Advances in Systems Analysis, Software Engineering, and High Performance Computing

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The integration of fog computing and machine learning (ML) in digital healthcare has revolutionized patient care, operations, and personalized treatment. This chapter explores the potential of fog computing in telemedicine, remote monitoring, and personalized treatment. It highlights its role in addressing data processing challenges, enabling real-time data analytics, and ensuring secure transmission of medical information. Key case studies demonstrate how these integrated solutions are driving innovation in the healthcare industry. The combination of fog computing and ML offers a promising avenue for the future of digital healthcare, focusing on data-driven decision-making and precision medicine.

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E-Health Startups’ Framework for Value Creation and Capture: Some Insights from Systematic Review

Cited by 2 publications

References 14 publications

Machine Learning in E-Health and Digital Healthcare

Machine Learning in E-Health and Digital Healthcare

Fog Computing-Integrated ML-Based Framework and Solutions for Intelligent Systems

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