In at the deep end: Psychosocial aspects of developing autonomy in histopathology training
Published on: 10th July, 2018
OCLC Number/Unique Identifier: 7795981289
Medical postgraduate trainees are given increasing levels of responsibility during training in the apprenticeship-model of training [1-3]. Responsibility is said to be a key driver of deep learning and understanding [4-7]. Trainees with greater levels of responsibility for decision making have higher levels of motivation to learn compared with trainees who self-assess as having less autonomy [5]. The Royal College of Pathologists (RCPath) indicate that ‘graded responsibility’ is part of training histopathologists and provided a framework for implementation with increasingly complex specimens suitable for reporting by more senior trainees [8,9].
Artificial Intelligence in the healthcare of older people
Published on: 20th March, 2020
OCLC Number/Unique Identifier: 8559314473
Clinical applications of Artificial Intelligence (AI) in healthcare are relatively rare. The high expectations in relation to data analysis influencing general healthcare have not materialized, with few exceptions, and then predominantly in the field of rare diseases, oncology and pathology, and interpretation of laboratory results. While electronic health records, introduced over the last decade or so in the UK have increased access to medical and treatment histories of patients, diagnoses, medications, treatment plans, immunization dates, allergies, radiology images, laboratory and test results, these have potential for evidence-based tools that providers can use to make decisions about a patient’s care, as well as streamline workflow. In the following text, we review the advances achieved using machine learning and deep learning technology, as well as robot use and telemedicine in the healthcare of older people.
Key points:
1. Artificial Intelligence use is extensively explored in prevention, diagnosis, novel drug designs and after-care.
2. AI studies on older adults include a small number of patients and lack reproducibility needed for their wider clinical use in different clinical settings and larger populations.
3. Telemedicine and robot assisted technology are well received by older service users.
4. Ethical concerns need to be resolved prior to wider AI use in routine clinical setting.
A hybrid deep learning model to forecast air quality data based on COVID-19 outbreak in Mashhad, Iran
Published on: 1st July, 2022
OCLC Number/Unique Identifier: 9555475715
The SARS-CoV-2 (COVID-19) pandemic outbreak has led to some lockdowns and changed human mobility and lifestyle in this country. Mashhad, one of the most polluted cities in Iran has experienced critical air pollution conditions in recent years. In the present study, the potential relationships between air quality conditions (such as popular index and criteria air pollutant concentration) and COVID-19 cases and deaths were investigated in Mashhad, Iran. To do that, the Long Short-Term Memory (LSTM) based hybrid deep learning architecture was implemented on AQI, meteorological data (such as temperature, sea level pressure, dew points, and wind speed), traffic index and impact number of death, and active cases COVID-19 from March 2019 to March 2022 in Mashhad. The results reveal the LSTM model could predict the AQI accurately. The lower error between the real and predicted AQI, including MSE, MSLE, and MAE is 0.0153, 0.0058, and 0.1043, respectively. Also, the cosine similarity between predicted AQI and real amounts of it is 1. Moreover, in the first peak of the pandemic (Aug 2021), we have the minimum amount of AQI. Meanwhile, by increasing the number of active cases and death and by starting lockdown, because the traffic is decreased, the air quality is good and the amount of AQI related to PM2.5 is 54.68. Furthermore, the decrease the active cases and death in pandemic causes a significant increase in AQI, which is 123.52 in Nov 2021, due to a decline in lockdowns, resumption of human activities, and probable temperature inversions.
Advances in deep learning-based cancer outcome prediction using multi-omics data
Published on: 1st May, 2023
Cancer prognosis reflects a complex biological process measured by multiple types of omics data. Deep learning frameworks have been proposed to integrate multi-omics data and predict patient outcomes in different cancer types, potentially revolutionizing cancer prognosis with superior performance. This minireview summarizes the advances in the strategies for multi-omics data integration and the performance of different deep learning models in prognosis prediction of diverse cancer types using multi-omics data published in the past 18 months. The challenges and limitations of deep learning models for predicting cancer outcomes based on multi-omics data are discussed.
COVID-19 detection and classification: key AI challenges and recommendations for the way forward
Published on: 25th May, 2023
Coronavirus disease (COVID-19) is a viral pneumonia that is found in China and has spread globally. Early diagnosis is important for effective and timely treatment. Thus, many ongoing studies attempt to solve key COVID-19 problems such as workload classification, detection, and differentiation from other pneumonia and healthy lungs using different imaging modalities. Researchers have identified some limitations in the deployment of deep learning methods to detect COVID-19, but there are still unmet challenges to be addressed. The use of binary classifiers or building classifiers based on only a few classes is some of the limitations that most of the existing research on the COVID-19 classification problem suffers from. Additionally, most prior studies have focused on model or ensemble models that depend on a flat single-feature imaging modality without using any clinical information or benefiting from the hierarchical structure of pneumonia, which leads to clinical challenges, and evaluated their systems using a small public dataset. Additionally, reliance on diagnostic processes based on CT as the main imaging modality, ignoring chest X-rays. Radiologists, computer scientists, and physicians all need to come to an understanding of these interdisciplinary issues. This article first highlights the challenges of deep learning deployment for COVID-19 detection using a literature review and document analysis. Second, it provides six key recommendations that could assist future researchers in this field in improving the diagnostic process for COVID-19. However, there is a need for a collective effort from all of them to consider the provided recommendations to effectively solve these issues.
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