AI and IoT in Healthcare

Singla, Sonia

doi:10.1007/978-3-030-37526-3_1

Cited by 10 publications

(5 citation statements)

References 19 publications

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“…In today's world, artificial intelligence (AI) progress in NLP and computer vision (CV) enable systems to automatically analyze texts, images, and audio data. This progress enhances efficiency and accuracy across various sectors like healthcare [39], finance [40], and education [41], facilitating faster and more precise decision making. Ranaldi et al [42] questions the direction of AI progress, particularly the shift towards auto-epistemic logic [43] by statistical learners by exploring fundamental issues in AI analysis, including symbolic structure and strategic reference points, and traces the evolution of knowledge representation.…”

Section: Discussionmentioning

confidence: 99%

Image Text Extraction and Natural Language Processing of Unstructured Data from Medical Reports

Malashin,

Masich,

Tynchenko

et al. 2024

MAKE

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This study presents an integrated approach for automatically extracting and structuring information from medical reports, captured as scanned documents or photographs, through a combination of image recognition and natural language processing (NLP) techniques like named entity recognition (NER). The primary aim was to develop an adaptive model for efficient text extraction from medical report images. This involved utilizing a genetic algorithm (GA) to fine-tune optical character recognition (OCR) hyperparameters, ensuring maximal text extraction length, followed by NER processing to categorize the extracted information into required entities, adjusting parameters if entities were not correctly extracted based on manual annotations. Despite the diverse formats of medical report images in the dataset, all in Russian, this serves as a conceptual example of information extraction (IE) that can be easily extended to other languages.

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Section: Discussionmentioning

confidence: 99%

Image Text Extraction and Natural Language Processing of Unstructured Data from Medical Reports

Malashin,

Masich,

Tynchenko

et al. 2024

MAKE

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“…The application of AI in healthcare is diverse and multifaceted, ranging from medical imaging analysis and clinical decision support to personalized medicine and remote monitoring. By harnessing the power of AI technologies, healthcare organizations can deliver more efficient, effective, and personalized care, ultimately leading to better health outcomes for individuals and populations alike (28) . However, the successful integration of AI into healthcare requires addressing challenges such as data privacy, regulatory compliance, and ethical considerations to ensure that AI technologies are deployed responsibly and ethically.…”

Section: Drug Discovery and Development: Ai Accelerates The Drug Disc...mentioning

confidence: 99%

Revolutionizing Innovations and Impact of Artificial Intelligence in Healthcare

et al. 2024

IJFMR

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Artificial Intelligence (AI) is revolutionizing the healthcare sector by offering innovative solutions to various challenges. This review explores the applications and benefits of AI in healthcare including AI techniques, machine learning, natural language processing, and computer vision, which are being utilized to enhance medical diagnostics, treatment planning, patient care, and administrative processes. One significant application of AI in healthcare is medical imaging analysis. Machine learning algorithms can analyze medical images such as X-rays, MRIs, and CT scans with high accuracy, aiding in early detection and diagnosis of diseases like cancer and neurological disorders. Additionally, AI-powered predictive analytics enable healthcare providers to forecast patient outcomes and identify individuals at risk of developing certain conditions, allowing for proactive intervention and personalized treatment plans. Furthermore, AI-driven virtual health assistants and chabot’s provide patients with instant access to medical information, advice, and support, improving healthcare accessibility and patient engagement. Natural language processing algorithms enable these systems to understand and respond to patients' queries and concerns effectively. In clinical decision support systems, AI algorithms analyze vast amounts of patient data, including medical records, genetic information, and real-time physiological data, to assist healthcare professionals in making informed decisions about diagnosis and treatment strategies. Moreover, AI-driven robotic surgery systems enhance surgical precision, reduce errors, and shorten recovery times. Despite the numerous benefits, challenges such as data privacy concerns, regulatory compliance, and the need for interdisciplinary collaboration remain. However, with ongoing advancements in AI technology and increased adoption by healthcare organizations, the potential for AI to transform healthcare delivery, improve patient outcomes, and reduce costs is substantial. Collaborative efforts between AI developers, healthcare providers, policymakers, and regulators are essential to harnessing the full potential of AI in healthcare while ensuring ethical and responsible use.

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“…The study highlights the need of addressing multi-dimensional challenges connected with AI clinical tools and provides useful insights for research and practice in the field of AI-based CDS. Anticipating and addressing these concerns is critical to developing acceptability and supporting the efficient use of AI-based devices as AI continues to advance in the healthcare industry [22].…”

Section: Personalized Treatment Plansmentioning

confidence: 99%

“…It continuously learns from a variety of patient populations, adding to a body of collective knowledge. The understanding of drug safety and efficacy across various demographic groups is fostered by this cumulative learning, which improves general healthcare practices [22].…”

Section: Personalized Treatment Plansmentioning

confidence: 99%

“…Healthcare professionals are better equipped to make treatment decisions with a greater emphasis on patient safety because to the integration of real-time monitoring and predictive modeling. Future developments in side effect control could lead to less negative reactions and more positive outcomes as AI technologies advance [22].…”

Section: Early Identification Of Potential Side Effectsmentioning

confidence: 99%

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Artificial Intelligence in Healthcare Delivery and Control Side Effects

Jawaid

2023

Preprint

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AI and IoT in Healthcare

Cited by 10 publications

References 19 publications

Image Text Extraction and Natural Language Processing of Unstructured Data from Medical Reports

Image Text Extraction and Natural Language Processing of Unstructured Data from Medical Reports

Revolutionizing Innovations and Impact of Artificial Intelligence in Healthcare

Artificial Intelligence in Healthcare Delivery and Control Side Effects

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