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• W4387576704 abstract "According to National Institution for Transforming India (NITI Aayog), India's healthcare sector is taking off with an annual growth rate of around 22% since 2016. With a market close to 372 billion in 2022, the healthcare sector will be one of the largest employment sectors. While the frontline workers are promoting health infrastructure, technology is complementing their efforts. The healthcare industry is witnessing a golden period as advancements in medical imagery, data analysis, computational sciences, and robotics are streamlining complex medical procedures. Integrating technology in healthcare has not only made the entire system efficient but has also reduced dependency on physicians. Even though developed countries have world-class health infrastructure, the fact that doctors are human enough to make mistakes, cannot be ignored. Moreover, different doctors have different intelligence levels and therefore interpret medical records differently. They adopt unique approaches while treating the same disease, which might not always work. For all these challenges, artificial intelligence stands as a one-stop solution. It can learn from past results to produce an unbiased, balanced, and objective report without preconceived notions. Its capability to process large datasets and produce personalized results with high precision makes it the most optimized approach for solving these complex healthcare challenges. While healthcare infrastructure is not up to the standards everywhere, a simple yet rigorously trained artificially intelligent prediction software is efficient enough to diagnose diseases in the initial stages based on the symptoms. Today, deep learning is aiding as a tool to diagnose complex diseases such as diabetic retinopathy, with minimal medical imagery, thereby eradicating the requirements of tedious tests. Advanced image processing algorithms coupled with deep learning analysis techniques have made it possible to re-create low-resolution medical images and automate analysis to produce conclusions in real time. Neural networks are facilitating in performing detailed analysis of medical data produced through magnetic resonance imaging, cardiac tomography, electrocardiography, and other scanning technology, thus making it significantly convenient to diagnose cancer, cardiovascular diseases, retinal diseases, genetic disorders, etc.This chapter is an attempt to highlight the possible use cases of deep learning algorithms and techniques in the healthcare industry. Even though the contribution of other technologies such as the internet of things, robotics, smart medical devices, IT systems, blockchain, surgical equipment, electronic health record management systems, staffing management systems, hybrid operation theatres, kiosks, vending machines, and telehealth tools can never be neglected. Moreover, this chapter tries to focus on how deep learning algorithms are supplementing existing technologies to make them more efficient and widely used. In addition, enhancing the efficiency will not only reduce the burden on existing infrastructure but also reduce the expenditure by eliminating unnecessary biopsies. The World Health Organization's annual report of 2022 determines the devastating impact of COVID-19 worldwide due to poor infrastructure. Scientists believe that deploying digital solutions facilitated by deep learning technologies can prevent such collapses in healthcare facilities in the future." @default.
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• W4387576704 date "2023-10-10" @default.
• W4387576704 modified "2023-10-14" @default.
• W4387576704 title "Deep learning for streamlining medical image processing" @default.
• W4387576704 doi "https://doi.org/10.1049/pbhe059e_ch4" @default.
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