2022

• (22 November 2022)     KnowLab is awarded £5,000 from UCL Global Engagement Funding.
  
  The funding is to extend and deepen our collaboration with iris.ai - a Norway based start-up behind the award-winning AI engine for scientific text understanding. The funded project is titled - Towards self-updatable knowledge base for evidence based medicine - join force with iris.ai (Norway) and beyond.

• (27 October 2022) The Alan Turing Heath Equity Interest Group - https://www.turing.ac.uk/research/interest-groups/health-equity, co-organised by KnowLab and colleagues, is now official online!
  
  In an era where AI is expected to improve our daily life - particularly in health, “How can we ensure that developments and applications of data science and AI improve everyone’s health?” This is a pressing and very challenging question. Please join forces with a multidisciplinary group to form a formidable synergistic force tackling one of the biggest challenges of AI in medicine.

• (22 October 2022) Dr Hang Dong’s great perspective piece on automated coding using NLP and knowledge-driven approaches has now been published on npj Digital Medicine. The work illustrates how NLP and AI can help improve the efficiency of clinical coding in healthcare - i.e., assign ICD/SNOMED codes to hospital visits, which currently is a very inefficient/erroneous process in NHS and, for that matter, in many other health systems across the world.

• (5 October 2022) Study on The Impact of Inconsistent Human Annotations on AI driven Clinical Decision Making.
  
  Annotation inconsistencies commonly occur when even highly experienced clinical experts annotate the same phenomenon (e.g., medical image, diagnostics, or prognostic status), due to inherent expert bias, judgements, and slips, among other factors. While their existence is relatively well-known, the implications of such inconsistencies are largely understudied in real-world settings.
  
  Aneeta Sylolypavan did her MSc with us addressing this hugely important research question using real-world ICU datasets with annotated data from 11 ICU consultants. The results suggest that (a) there may not always be a “super expert” in acute clinical settings; and (b) standard consensus seeking (such as majority vote) consistently leads to suboptimal models. Further analysis, however, suggests that assessing annotation learnability and using only ‘learnable’ annotated datasets for determining consensus achieves optimal models in most cases.
  
  The manuscript is now under review with npj Digital Medicine and preprint is available at doi:10.21203/rs.3.rs-1937575/v1.

• (18 August 2022) Our systematic review titled “Artificial intelligence models for predicting cardiovascular diseases in people with type 2 diabetes - a systematic review”, led by Minhong Wang, has been accepted by Intelligence-Based Medicine. This study identified and reviewed existing AI models for predicting risk of cardiovascular diseases in people with type 2 diabetes. We found that compared to risk scores developed using conventional methods, AI approaches have the potential to achieve more accurate predictions than risk scores developed using conventional methods. However, none of the reviewed models is directly reusable or reproducible, due to incomplete reporting and lack of transparency. Clinically, none of the AI models includes interventions that may affect risks such as medications and lifestyle changes. There were no indications in the studies on whether the prediction models might be able to adapt to include these factors.

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