2023

• (11 September 2023)    New grant! KnowLab is awarded £649,218 by MRC for Quantifying and Mitigating Bias affecting and induced by AI in Medicine!
  
  Artificial Intelligence (AI) has demonstrated exciting potential in improving healthcare. However, these technologies come with a big caveat. They do not work effectively for minority groups. A recent study published in Science shows a widely used AI tool in the US concludes Black patients are healthier than equally sick Whites. Using this tool, a health system would favour White people when allocating resources, such as hospital beds. AI models like this would do more harm than good for health equity. Funded by Medical Research Council, KnowLab is leading a 30-month research project focusing on using data science and machine learning to quantify and mitigate data embedded and AI induced bias and inequality. Clearly, this is a challenge too grand to be tackled by a single institute. We will be working closely with BHF Data Science Centre, University of Edinburgh, University of Birmingham, Nanjing Medical University (China), and wider communities including Health Dat Research UK, the Alan Turing Institute and beyond.
  
  Check the Project Page at UKRI

• (23 July 2023) Hard exudate plays an important role in grading diabetic retinopathy (DR) as a critical indicator. Therefore, the accurate segmentation of hard exudates is of clinical importance. However, the percentage of hard exudates in the whole fundus image is relatively small, and their shapes are often irregular and the contrasts are usually not high enough. Hence, they are prone to misclassifications e.g., misclassified as part of the optic disc structure or cotton wool spots, which results in the low segmentation accuracy and efficiency. This paper proposes a novel neural network RMCA U-net to accurately segmentation hard exudate in fundus images. The network features a U-shape framework combined with a residual structure to obtain the subtle features of hard exudate. A multi-scale feature fusion (MSFF) module and an improved channel attention (CA) module are designed and involved to effectively segmentation sparse small lesions. The proposed method in this paper has been trained and evaluated on three data sets - IDRID, Kaggle and one local data set. Experiments are shown and indicate that RMCA U-net of this paper is superior to the other convolutional neural networks. The method in this paper is increased by 6% higher in PR-MAP than U-net on the IDRID dataset, increased by 10% in Recall than U-net on the Kaggle dataset and increased by 20% in F1-score than U-net on the local dataset.
  
  Read it at DOI:10.1016/j.eswa.2023.120987

• (15 July 2023) This paper presents our contribution to the RadSum23 shared task organized as part of the BioNLP 2023. We compared state-of-the-art generative language models in generating high-quality summaries from radiology reports. A two-stage fine-tuning approach was introduced for utilizing knowledge learnt from different datasets. We evaluated the performance of our method using a variety of metrics, including BLEU, ROUGE, bertscore, CheXbert, and RadGraph. Our results revealed the potentials of different models in summarizing radiology reports and demonstrated the effectiveness of the two-stage fine-tuning approach. We also discussed the limitations and future directions of our work, highlighting the need for better understanding the architecture design’s effect and optimal way of fine-tuning accordingly in automatic clinical summarizations.
  
  Read it at DOI:10.18653/v1/2023.bionlp-1.54

• (5 May 2023) Ontology-driven and weakly supervised rare disease identification from clinical notes published on BMC Medical Informatics and Decision Making
  
  Superb work from Dr Hong Dong and colleagues, demonstrating how weak supervised NLP + Ontology techniques can greatly facilitate the identification of rare disease mentions from electronic health records with >90% accuracy. This uses training data that need no human annotations!
  
  Read it at DOI:10.1186/s12911-023-02181-9

• (5 May 2023) New paper titled Prediction of disease comorbidity using explainable artificial intelligence and machine learning techniques - A systematic review published on International Journal of Medical Informatics
  
  Mohanad M. Alsaleh - a PhD student at UCL - did a great systematic review on explainable AI methods for predicting comorbidity from electronic health records. It finds “(a) The use of explainable artificial intelligence (XAI) can improve predictions of comorbidities by providing a transparent understanding of the reasoning behind predictions and helping healthcare providers make informed decisions. (b) There is a great potential to uncover novel disease associations and better understand the mechanisms of diseases by integrating genetic and electronic health record (EHR) data, leading to improved quality of care and earlier diagnoses. (c) The use of AI in healthcare can improve patient outcomes and reduce healthcare costs by identifying disease risks and making personalised treatment plans.”
  
  Read it at DOI:10.1016/j.ijmedinf.2023.105088

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