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Abstract

The future of healthcare is a dynamic landscape characterized by rapid advancements, evolving patient needs, and transformative technologies. This chapter explores key trends and predictions shaping the industry. It covers the integration of AI, telemedicine, genomics, and patient empowerment. These shifts promise a healthcare ecosystem that is more efficient, accessible, and personalized than ever before. However, they also present challenges, including data privacy, ethical considerations, and equitable access. Navigating this evolving healthcare landscape will require a thoughtful balance of innovation and ethical practice, ensuring that the future of healthcare benefits all segments of society. The chapter aims to equip stakeholders with insights and strategies to navigate this complex landscape, advocating for a healthcare future that prioritizes patient-centricity while embracing technological progress in a way that is inclusive and beneficial for all.

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Most cited references 75

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Maternal and child undernutrition and overweight in low-income and middle-income countries

Robert E Black, Cesar G Victora, Susan P. Walker … (2013)

The Lancet, 382(9890), 427-451

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Dermatologist-level classification of skin cancer with deep neural networks

Andre Esteva, Brett Kuprel, Roberto Chacón-Novoa … (2017)

Skin cancer, the most common human malignancy, is primarily diagnosed visually, beginning with an initial clinical screening and followed potentially by dermoscopic analysis, a biopsy and histopathological examination. Automated classification of skin lesions using images is a challenging task owing to the fine-grained variability in the appearance of skin lesions. Deep convolutional neural networks (CNNs) show potential for general and highly variable tasks across many fine-grained object categories. Here we demonstrate classification of skin lesions using a single CNN, trained end-to-end from images directly, using only pixels and disease labels as inputs. We train a CNN using a dataset of 129,450 clinical images—two orders of magnitude larger than previous datasets—consisting of 2,032 different diseases. We test its performance against 21 board-certified dermatologists on biopsy-proven clinical images with two critical binary classification use cases: keratinocyte carcinomas versus benign seborrheic keratoses; and malignant melanomas versus benign nevi. The first case represents the identification of the most common cancers, the second represents the identification of the deadliest skin cancer. The CNN achieves performance on par with all tested experts across both tasks, demonstrating an artificial intelligence capable of classifying skin cancer with a level of competence comparable to dermatologists. Outfitted with deep neural networks, mobile devices can potentially extend the reach of dermatologists outside of the clinic. It is projected that 6.3 billion smartphone subscriptions will exist by the year 2021 (ref. 13) and can therefore potentially provide low-cost universal access to vital diagnostic care.

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High-performance medicine: the convergence of human and artificial intelligence

Eric Topol (2019)

The use of artificial intelligence, and the deep-learning subtype in particular, has been enabled by the use of labeled big data, along with markedly enhanced computing power and cloud storage, across all sectors. In medicine, this is beginning to have an impact at three levels: for clinicians, predominantly via rapid, accurate image interpretation; for health systems, by improving workflow and the potential for reducing medical errors; and for patients, by enabling them to process their own data to promote health. The current limitations, including bias, privacy and security, and lack of transparency, along with the future directions of these applications will be discussed in this article. Over time, marked improvements in accuracy, productivity, and workflow will likely be actualized, but whether that will be used to improve the patient-doctor relationship or facilitate its erosion remains to be seen.

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Shaik Aminabee: (View ORCID Profile)

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Publication date: February 23 2024

Pages: 240-262

DOI: 10.4018/979-8-3693-1214-8.ch012

SO-VID: e2ef0491-67bb-4d55-a1ee-3f1a9b753d67

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The Future of Healthcare and Patient-Centric Care

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Value-based Healthcare

Most cited references 75

Maternal and child undernutrition and overweight in low-income and middle-income countries

Dermatologist-level classification of skin cancer with deep neural networks

High-performance medicine: the convergence of human and artificial intelligence

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Cited by 13