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Artificial Intelligence for Precision Medicine
Published in Kamal Kumar Sharma, Akhil Gupta, Bandana Sharma, Suman Lata Tripathi, Intelligent Communication and Automation Systems, 2021
AI, a significant technology in the big data context, is broadly applied to several disciplines. Despite the lack of knowledge about the operating algorithms, AI is becoming a key element of everyday living. Many applications of AI involve voice, face or script recognition; natural language processing; robotics; and auto-driven cars. AI is steadily transforming medicine and influencing the decisions of healthcare stakeholders as well, being deployed in image-centred diagnosis in several clinical specialties, including radiology, pathology, ophthalmology and dermatology. At Aravind Eye Hospital in India, Google applied AI technology to detect retinal disorders from retina photos of diabetics. AI has likewise excelled in the genetic variants for sequencing the next-generation data [3].
Knowledge-Based Problem-Solving
Published in Satya Prakash Yadav, Dharmendra Prasad Mahato, Nguyen Thi Dieu Linh, Distributed Artificial Intelligence, 2020
Raj Kumar Goel, Shweta Vishnoi, Chandra Shekhar Yadav, Pankaj Tyagi
With megapixels of megapixel data to the results of X-rays, CAT scans, MRIs, and other test methods, combining high-resolution images can be a challenge even for the experienced clinician. Artificial intelligence has already shown that it can be a valuable partner for radiologists and pathologists to increase their productivity and improve their accuracy [24]. Advanced AI algorithms, especially deep learning, have shown remarkable advances in image recognition tasks. Numerous medical experts, including those in ophthalmology, dermatology, radiology, pathology, and neurology, rely on image-based diagnostics [25]. Historical picture databases are mostly stored by radiology departments in an image collection and communication system, which usually contains thousands of examples of training networks [26].
Modelling and analysis of skin pigmentation
Published in Ahmad Fadzil Mohamad Hani, Dileep Kumar, Optical Imaging for Biomedical and Clinical Applications, 2017
Ahmad Fadzil Mohamad Hani, Hermawan Nugroho, Norashikin Shamsudin, Suraiya H. Hussein
Assessment of skin colour changes is important in dermatology. The changes, however, are not linear and hard to be discerned visually. In dermatology, human skin colour is cross-referred with skin phototype (SPT). Fitzpatrick skin type (FST) is a subjective format for assessing SPTs I–VI in human, as shown in Table 4.3 [9]. The FST is commonly used as a predictor of skin cancer [10].
Study and implementation of automated system for detection of PCOS from ultrasound scan images using artificial intelligence
Published in The Imaging Science Journal, 2023
M. Sumathi, P. Chitra, S. Sheela, C. Ishwarya
Research has also revealed the still existing significant gap between the existing evidence and its use to earlier diagnosis and evidence-based therapy [9,10]. Regarding the diagnosis and treatment of PCOS, there are still information gaps in several medical specialties (such as obstetrics and gynaecology (OBGYN), medicine, paediatrics, and dermatology), and women with PCOS report considerable delays in the diagnosis [11]. Atherosclerosis and cardiovascular disease (CV) are linked to metabolic syndrome (MBS), a prevalent ailment associated with visceral obesity and insulin resistance (IR) [12,13]. MBS is highly prevalent, affecting 23.7% of Americans over the age of 20 [14]. Roy, D.G et al., investigated how several life evaluation elements affected people's quality of life (QoL). Together with the use of AI and statistical techniques, the role of data analytics in the monitoring and management of human quality of life has also been considered. It has aided in locating the variables that can boost human quality of life on the whole [15].
Big data analytics in medical engineering and healthcare: methods, advances and challenges
Published in Journal of Medical Engineering & Technology, 2020
Lidong Wang, Cheryl Ann Alexander
Artificial intelligence (AI) has been used for image analysis in dermatology, pathology and radiology with good accuracy and fast diagnostic speed. AI helps decrease medical errors, recommend precision therapies for complex diseases, optimise the care procedures of chronic illnesses and increase patient enrolment into clinical trials [6]. Unsupervised/supervised learning and reinforcement learning are machine learning methods. Unsupervised learning identifies hidden structures in unlabelled data. Supervised learning uses labelled data for training, creates a model and classifies new observations based on the model. Reinforcement learning uses a feedback mechanism to maximise the cumulative reward and improve results [7]. Text mining has been used in various industries, such as biomedical industry, but there are few applications in retail pharmacy demand planning although inaccurate prediction in retail pharmacy often happens [8]. Three technical (3 T) branches – intelligent agents, machine learning, and text mining – have been contributing to healthcare (see Table 1 [9]).
Physicians’ opinions about the causes of underreporting of occupational diseases
Published in Archives of Environmental & Occupational Health, 2020
Mehmet Erdem Alaguney, Ali Naci Yildiz, Ahmet Ugur Demir, Osman Alpaslan Ergor
Specialties that have a more frequent relation with occupational diseases (pulmonary medicine, public health, internal medicine, otorynolaryngology, neurology, dermatology, physical therapy, and rehabilitation) and OPs, general practitioners and family medicine specialists in Turkey were targeted in this study. To reach these physicians, a physician is selected from each specialty who is a member of his/her specialty society e-mail group. The email list of the family medicine physicians is received from Turkish Public Health Institute of Ministry of Health. The emails of OPs are derived from Society of OPs. The sum of all members of these email groups was approximately five thousand physicians. By the time of the data collection, there were no residency trained occupational medicine specialist in Turkey, for that reason specialty in occupational medicine is not included in the questionnaire.