When I say Socratic questioning.

In this article, Ng et al. define the core concepts of Socratic questioning and how it can be appropriately applied in clinical education.

Knowledge, perceptions and behaviours of endoscopists towards the use of artificial intelligence-aided colonoscopy.

BACKGROUND: Recent developments in artificial intelligence (AI) systems have enabled advancements in endoscopy. Deep learning systems, using convolutional neural networks, have allowed for real-time AI-aided detection of polyps with higher sensitivity than the average endoscopist. However, not all endoscopists welcome the advent of AI systems. METHODS: We conducted a survey on the knowledge of AI, perceptions of AI in medicine, and behaviours regarding use of AI-aided colonoscopy, in a single centre 2 months after the implementation of Medtronic's GI Genius in colonoscopy. We obtained a response rate of 66.7% (16/24) amongst consultant-grade endoscopists. Fisher's exact test was used to calculate the significance of correlations. RESULTS: Knowledge of AI varied widely amongst endoscopists. Most endoscopists were optimistic about AI's capabilities in performing objective administrative and clinical tasks, but reserved about AI providing personalised, empathetic care. 68.8% (n = 11) of endoscopists agreed or strongly agreed that GI Genius should be used as an adjunct in colonoscopy. In analysing the 31.3% (n = 5) of endoscopists who disagreed or were ambivalent about its use, there was no significant correlation with their knowledge or perceptions of AI, but a significant number did not enjoy using the programme (p-value = 0.0128) and did not think it improved the quality of colonoscopy (p-value = 0.033). CONCLUSIONS: Acceptance of AI-aided colonoscopy systems is more related to the endoscopist's experience with using the programme, rather than general knowledge or perceptions towards AI. Uptake of such systems will rely greatly on how the device is delivered to the end user.

Natural language processing in urology: Automated extraction of clinical information from histopathology reports of uro-oncology procedures.

Objectives: We aimed to automate routine extraction of clinically relevant unstructured information from uro-oncological histopathology reports by applying rule-based and machine learning (ML)/deep learning (DL) methods to develop an oncology focused natural language processing (NLP) algorithm. Methods: Our algorithm employs a combination of a rule-based approach and support vector machines/neural networks (BioBert/Clinical BERT), and is optimised for accuracy. We randomly extracted 5772 uro-oncological histology reports from 2008 to 2018 from electronic health records (EHRs) and split the data into training and validation datasets in an 80:20 ratio. The training dataset was annotated by medical professionals and reviewed by cancer registrars. The validation dataset was annotated by cancer registrars and defined as the gold standard with which the algorithm outcomes were compared. The accuracy of NLP-parsed data was matched against these human annotation results. We defined an accuracy rate of >95% as "acceptable" by professional human extraction, as per our cancer registry definition. Results: There were 11 extraction variables in 268 free-text reports. We achieved an accuracy rate of between 61.2% and 99.0% using our algorithm. Of the 11 data fields, a total of 8 data fields met the acceptable accuracy standard, while another 3 data fields had an accuracy rate between 61.2% and 89.7%. Noticeably, the rule-based approach was shown to be more effective and robust in extracting variables of interest. On the other hand, ML/DL models had poorer predictive performances due to highly imbalanced data distribution and variable writing styles between different reports and data used for domain-specific pre-trained models. Conclusion: We designed an NLP algorithm that can automate clinical information extraction accurately from histopathology reports with an overall average micro accuracy of 93.3%.

The imitation game: a review of the use of artificial intelligence in colonoscopy, and endoscopists' perceptions thereof.

The development of deep learning systems in artificial intelligence (AI) has enabled advances in endoscopy, and AI-aided colonoscopy has recently been ushered into clinical practice as a clinical decision-support tool. This has enabled real-time AI-aided detection of polyps with a higher sensitivity than the average endoscopist, and evidence to support its use has been promising thus far. This review article provides a summary of currently published data relating to AI-aided colonoscopy, discusses current clinical applications, and introduces ongoing research directions. We also explore endoscopists' perceptions and attitudes toward the use of this technology, and discuss factors influencing its uptake in clinical practice.

Medium-Term Survival After Unrepaired Ruptured Abdominal Aortic Aneurysm.

PURPOSE: Ruptured abdominal aortic aneurysm (AAA) is almost always considered fatal without open surgical or endovascular repair. We report a case that has defied this norm and explore the possible factors involved in this exceedingly rare outcome. CASE REPORT: An 87 year old gentleman presented with an acute ruptured AAA with left retroperitoneal hematoma. He was counseled for emergent repair, but opted for conservative management instead. He has remained well at the time of writing, 13 months from the rupture, with clinical resolution of symptoms along with radiological resolution of the hematoma. CONCLUSION: Timely repair remains the mainstay of management for ruptured AAA, although this rare case highlights that it is possible for ruptured AAA to seal spontaneously with patient surviving up to 13 months. We have sought to hypothesize the factors in this case that may have contributed to prolonged survival following untreated ruptured AAA. CLINICAL IMPACT STATEMENT: While the overwhelming evidence is that a ruptured AAA left unrepaired is fatal, our case report illustrates a rare case that shows it is possible for ruptured AAA to seal spontaneously, with patient surviving up to 13 months. We seek to hypothesize the factors that may contribute to such prolonged survival.

Impact of Knowledge and Attitudes on Lifestyle Practices in Preventing Type 2 Diabetes Mellitus.

INTRODUCTION: Diabetes mellitus is a major public health issue in Singapore. To shape healthcare policies for the primary prevention of diabetes, it is crucial to understand Singaporeans' knowledge, attitudes and practices related to diabetes and its prevention. This study aimed to assess the knowledge, attitudes and lifestyles of individuals without diabetes. MATERIALS AND METHODS: A cross-sectional household survey was performed between 31 January to 3 February 2019 to examine knowledge, attitudes and practices related to diabetes. Inclusion criteria of the participants included: 1) Singaporeans/permanent residents, 2) between 30 to 64 years old, and 3) who did not have a diagnosis of diabetes. Logistic and linear regression models were used to analyse the association of knowledge and attitudes with physical activity and diet habits, respectively. RESULTS: Among 806 participants, 72.2% did not meet the Health Promotion Board's physical activity recommendation. Physical activity was associated with better diabetes knowledge (odds ratio [OR] 5.38, 95% confidence interval [CI] = 1.65-17.53, P = 0.049), stronger beliefs in diabetes prevention (OR 3.36, 95% CI = 1.02-11.12, P = 0.047) and lower levels of worry about diabetes (OR 0.41, 95% CI 0.17-1.00, P = 0.049). Neither knowledge nor beliefs or worries about diabetes was associated with diet. CONCLUSION: There is a need to reinforce the importance of physical activity and healthy diet in preventing diabetes. Although improving the knowledge level of diabetes may increase physical activity of the population, it is unlikely to improve dietary choices without effective behavior change interventions.