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Deep Learning and Multimodal Artificial Neural Network Architectures for Disease Diagnosis and Clinical Applications
Published in Om Prakash Jena, Bharat Bhushan, Nitin Rakesh, Parma Nand Astya, Yousef Farhaoui, Machine Learning and Deep Learning in Efficacy Improvement of Healthcare Systems, 2022
Different technologies were introduced and adopted in the medical field, thereby resulting in expanded automation. Artificial intelligence (AI), or machine intelligence, has been identified as the most important technology for medical diagnosis and clinical applications [1]. It can solve various medical challenges at different levels of difficulty in complex medical operations. Checking for anomalies and suggesting proper medical intervention is one of the benefits of AI in clinical applications. AI presents rapid innovations in clinical areas and evaluates information and clinical reports. If a digital database is used to store patients’ data, it can be utilized for further diagnosis, and an appropriate software can be developed to automate the various operations in a hospital. It facilitates more informed decisions regarding the patient and also provides excellent services accordingly.
Biomedical Engineering and Informatics Using Artificial Intelligence
Published in Saravanan Krishnan, Ramesh Kesavan, B. Surendiran, G. S. Mahalakshmi, Handbook of Artificial Intelligence in Biomedical Engineering, 2021
Medical imaging is a collection of techniques that are used to create visual representations of a body interior for clinical analysis and medical intervention. Medical imaging plays an important role in medical diagnosis, treatment, and medical applications, which seeks to reveal internal structures hidden by the skin and bones for diagnosing and treating disease. In medicine, AI is used to identify diagnosis and give therapy recommendations. In medical diagnosis, artificial neural networks (ANNs) is used to get the result of the diagnosis. ANN provides an extraordinary level of achievement in the medical field. ANN has been applied to various areas in medicine like disease diagnosis, biochemical analysis, image analysis, etc. In recent years, medical image processing uses ANNs for analyzing medical images. The main components of medical image processing that heavily depend on ANNs are medical image object detection and recognition, medical image segmentation, and medical image preprocessing. The various AI imaging technologies help to examine various factors of the human body using radiography, MRI, nuclear medicine, ultrasound imaging, tomography, cardiograph, and so on (Smita et al., 2012).
Resources of Strength: An Exnovation of Hidden Competences to Preserve Patient Safety
Published in Emma Rowley, Justin Waring, A Socio-cultural Perspective on Patient Safety, 2017
In medical settings ‘diagnosis’ generally refers to the identification of a disease. Yet diagnosis is also geared to the hazards of medical intervention, and the identification of these problems is at the heart of error prevention. By broadening the analytical scope we can include dynamic diagnosis of how things are going as they are (also when they are going well). This form of diagnosis is about recognition of the overall task structure, the ability ‘to read‘ the conduct of co-participants and the identification of opportunities for actions. Although practices are often critically and pervasively augmented with this ‘positive mode’ of diagnostic work, it is often invisible and has therefore been under-theorized in medical and nursing literature. By illuminating this kind of work in relation to teamwork, opportunities for strengthening the safety of critical care practices are opened up (Mesman 2010).
Skin disease migration segmentation network based on multi-scale channel attention
Published in Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2023
Bin Yu, Long Yu, Shengwei Tian, Weidong Wu, Zhang Dezhi, Xiaojing Kang
The rise of medical imaging has played a vital role in medical diagnosis. Skin cancer is one of the most common cancers in the world. According to the 2019 Annual Report of the American Cancer Society, there were 104,350 new skin cancer patients in the United States in 1933 and 11,650 deaths. Among them, melanoma is the deadliest skin cancer, accounting for 92.46% and 62.06% of new cases and deaths from skin cancer (Zhang et al. 2022). But in fact, because the pigmentation lesions occur on the surface of the skin, melanoma can be detected in advance through visual inspection by experts. If skin damage is detected in the early stage of the disease and treated, then skin damage such as melanoma can have a higher cure rate (Codella et al. 2018).
An automated hybrid attention based deep convolutional capsule with weighted autoencoder approach for skin cancer classification
Published in The Imaging Science Journal, 2023
Skin cancer develops when a mutation occurs in the DNA of skin cells, and these skin cells tend to grow out of control, with the massive formation of cancer cells. Early diagnosis can help patients seek treatment and have a greater chance of recovery. But, many complications emerge in collecting diverse information such as position, skin lines, etc. Because of its non-invasiveness, much research has been conducted to present an accurate diagnosis approach. Segmentation is a widely carried process in skin cancer classification, but domain knowledge of diseases is highly required. Artificial intelligence (AI) based ML and DL techniques recently gained much attention towards researchers, especially in medical applications.
Detecting susceptible communities and individuals in hospital contact networks: a model based on social network analysis
Published in Connection Science, 2023
Yixuan Yang, Sony Peng, Sophort Siet, Sadriddinov Ilkhomjon, Phonexay Vilakone, Seok-Hoon Kim, Doo-Soon Park
AI-based healthcare refers to the use of AI technologies in the healthcare industry. The goal of integrating AI into healthcare is to enhance the efficiency and effectiveness of healthcare delivery and improve patient outcomes. AI can be used to analyse large amounts of medical data to identify patterns and make predictions, assist in medical diagnosis and treatment planning, monitor patient health and alert healthcare providers to potential issues, and automate routine tasks to improve efficiency. The use of AI-based data analysis can help healthcare providers and researchers to identify patterns and insights within the data that may be difficult or impossible to detect using traditional statistical methods.