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Digital and Personalized Healthcare System for COVID-19 and Future Pandemics
Published in Ram Shringar Raw, Vishal Jain, Sanjoy Das, Meenakshi Sharma, Pandemic Detection and Analysis Through Smart Computing Technologies, 2022
Precision healthcare and preventive approach lead to precision medicine. Dynamic algorithms developed through IoT and AI-based technologies can bring post-COVID times into clinical practice (Figure 3.4). The comprehensive healthcare system reforms, high-performance computing (HPC), biological datasets, and implements precision medicine pathways with multidimensional data curated from refining unstructured data. A personalized healthcare system requires precision medicine to predict potential therapy further and optimize based on pattern recognition.
Complexity: Cloud 9, Caryl Churchill (1979)
Published in Ewan Jeffrey, David Jeffrey, Enhancing Compassion in End-of-Life Care Through Drama, 2021
Assumptions and pattern recognition may be an integral part of reaching a clinical diagnosis, but they can lead to mistakes if extended to the other (arguably more important) parts of a patient’s life. When assumptions are made about a patient on the basis of a doctor’s prejudices and values then stereotyping becomes likely.
Magnetic Resonance Imaging
Published in Shoogo Ueno, Bioimaging, 2020
Image processing using information-processing technology represented by artificial intelligence (AI) has had a significant impact on MRI. Automatic detection of aneurysms using surface extraction and other techniques has been conducted for a long time. Recently, the use of neural networks has made pattern recognition possible for a wider range of subjects. For example, automatic detection of lesions is now possible, and it is becoming useful in reducing the risk for radiologists overlooking lesions while interpreting large images on an everyday basis. In addition, techniques have also been reported that enable a neural network to learn the relationship of images including a lot of noise to images not including noise and generate images that have reduced noise. The scope of AI application is expected to become increasingly broad, encompassing even application to functional MRI image analysis.
Lung cancer breath tests
Published in Expert Review of Respiratory Medicine, 2019
To face these challenges, Haick and colleagues and others have integrated heterogeneous micro- and nano-technologies, into autonomous smart systems for tomorrow’s medical diagnosis and follow-up applications in lung cancer [10,11]. In this smart diagnostic tool, an interaction between the disease-specific VOCs profile in the tested breath sample and a miniaturized array of cross-reactive, highly sensitive nanomaterial-based chemical sensors are recorded, stored and pre-processed by integrated miniature on-chip electronics, and then transferred via the internet to an external server for remote statistical analysis of the collected signal. Statistical pattern recognition methods are then applied to the received data, while also considering previous measurements and other clinical data of the same patient that has been stored on the system. Upon completion of the analysis and in case the results are positive, a clinical report is sent back to the designated receiver.
Neuro-Ophthalmic Literature Review
Published in Neuro-Ophthalmology, 2018
David Bellows, Noel Chan, John Chen, Hui-Chen Cheng, Panitha Jindahra, Peter MacIntosh, Axel Petzold, Michael Vaphiades, Konrad P. Weber
The possibilities of Artitifical Intelligience (AI) for improved pattern recognition of medical images has substantially improved over the past decades thanks to deep learning (machine learning) approaches. This study ethically elegantly networked a highly skilled human team with complementary clinical and computer capacities. Building on this expertise only 877 manually segmented and classified retinal optical coherence tomography (OCT) images were necessary to train a set of five complementary algorithms. Following human training this 5-headed classification network became capable of classifying OCT images into ten diseases with high accuracy. High accuracy in this study meant that AI, relying only on a few milliseconds of retinal image pattern recognition, performed equally good as did expert clinicians having access to a whole range of information from the patients symptoms and signs. In fact, when it came to the all crucial question of how urgently a patient should be referred, AI outperformed humans. Data on the accuracy of referral suggestions were based on 14,884 OCT scans (7,621 patients) and Receiver Operator characteristics (ROC) which approach near perfection. Importantly the algorithms could readily be trained to accurately perform on a whole range of OCT images from different manufactures. Another novelty is that the included OCT viewer permits for visualisation of AI classified pathologies (and imaging artefacts) overlaid to the OCT B-scans. This level of insight into the AI dependent classification process answers the call of many readers into ‘accountability’ of AI.
Medical school in 2029
Published in Medical Teacher, 2018
During these key changes in medical education, one regularly asked the question where does a medical school belong in a health care system? If we refer to Figure 1, provided by Tom Aretz, we see that traditional medical schools trained graduates to work in all four quadrants. But, data on task shifting suggested that highly predictable care, based on pattern recognition and rules/algorithm-based decisions (ambulatory care for common problems such as common backpain, routine surgical care for knees replacements, skin biopsies for suspicious lesions, or reading cytology smears from PAP smears) could be done well by narrowly trained people with far less education than physicians. There was even evidence that some tasks were done better by computers capable of machine learning/artificial intelligence than by humans who got bored or overlooked obvious abnormalities. The evidence showed that where physicians functioned best is when there is low predictability and high complexity. This was what the new medical school curriculum focused on and where we are today.