Articles

Optimizing AI Model Inference on Serverless Cloud Platforms: A Scalable Approach

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The increasing prevalence of Artificial Intelligence (AI) and Machine Learning (ML) models across various industries has highlighted the critical need for efficient and scalable deployment strategies. Traditional deployment methods often struggle with adapting to fluctuating demands and maintaining cost-effectiveness. Serverless computing has emerged as a promising solution to address these challenges. This paper investigates the deployment of AI models within serverless architectures on Amazon Web Services (AWS), specifically focusing on AWS Lambda and Knative. The study analyzes the limitations of conventional deployment approaches and proposes innovative strategies leveraging the capabilities of serverless technologies. Furthermore, it presents a rigorous evaluation of the performance characteristics of these serverless deployment strategies, discusses crucial security and privacy considerations, incorporates illustrative real-world case studies, and outlines potential future research directions.

Safeguarding Patient Confidentiality in Telemedicine: A Systematic Review of Privacy and Security Risks, and Best Practices for Data Protection

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The COVID-19 pandemic accelerated telemedicine adoption, showcasing its potential in improving healthcare delivery. However, privacy and security risks pose challenges, impeding widespread acceptance. The aim is to investigate the integration of data analytics, data analysis, and data cleaning in telemedicine, focusing on patient data privacy and security, with the goal of proposing strategies to mitigate risks and uphold confidentiality. Utilizing a qualitative approach, privacy and security challenges in telemedicine were investigated. Multiple databases, including PubMed, Embase, and Cochrane Library, were searched from 2018-2023. Inclusion criteria involved English-language, peer-reviewed empirical studies focusing on telemedicine privacy and security. Out of 770 unique records screened, eight studies were included. Full-text review and risk of bias assessment were conducted using CASP tool.  Privacy and security, technology hurdles for providers, patient trust, professional training, physical assessment challenges, and disparities among special populations were identified. Environmental, technological, and operational factors contribute to privacy and security risks in telehealth. Technology challenges like restricted access to telehealth tools and poor internet hinder adoption. Data analytics in telemedicine facilitates healthcare transformation, addressing privacy and security while optimizing patient outcomes through advanced analytics techniques and structured data analytics lifecycles. The integration of data analytics in telemedicine shows promise for healthcare transformation by providing insights into patient behavior and policy impacts, while ensuring data privacy and security. Addressing barriers, accelerated by the COVID-19 pandemic, requires infrastructure enhancements and global research efforts for inclusive telehealth ecosystems.