2024

• (28 October 2024) Invited talk at KnowLab - The world of non-clinical safety, artificial intelligence, and foundation models
  
  Speaker - Dr Arijit Patra
  
  Title - The world of non-clinical safety, artificial intelligence, and foundation models
  
  Abstract - There has been a significant amount of interest in artificial intelligence in the pharmaceutical industry, particularly with respect to drug discovery. While drug discovery has been established as a domain where algorithmic involvement can create substantial efficiency gains, other aspects of the pharmaceutical development process such as non-clinical and clinical safety are of critical importance towards creating patient value. This talk will explore the drug development process, the concepts of non-clinical safety with a particular focus on toxicologic pathology, and recent insights on building ML pipelines and foundation models therein.
  
  Bio - Dr Arijit Patra is a Senior Principal Scientist at UCB Biopharma UK. He holds a PhD in machine learning for healthcare imaging from the University of Oxford, where he was a Rhodes Scholar (India & Exeter, 2016). Prior to that, he completed a dual degree in Mechanical Engineering from the Indian Institute of Technology (IIT) at Kharagpur, India. He has also been associated with AstraZeneca, Shell, Microsoft Research and CSIR-South Africa at various points in his career and has been actively involved in the AI4SG (AI for Social Good) community. He has authored several publications around machine learning and medical imaging and is a reviewer for multiple peer reviewed venues such as NeurIPS, ICML, MICCAI and several journals. Arijit has been appointed as a Rising Leaders fellow of the Aspen Institute UK, and as an International Strategy Forum fellow in 2024.
  
  Date/Time - 13.30-14.30 14th November 2024

• (10 October 2024) Tutorial on Foundation Models For Medical Imaging - with MICCAI 2024, Morocco
  
  Generative AI and large-scale self-supervised foundation models are poised to have a profound impact on human decision making across occupations. Healthcare is one such area where such models have the capacity to impact patients, clinicians, and other care providers.
  
  As part of MICCAI2024, Yunsoo, Jinge and Honghan worked with colleagues from IBM, ÉTS Montréal, and Stanford University to organize a tutorial on Foundation Models For Medical Imaging. Yunsoo and the rest of KnowLab team took the lead on “the multimodal LLMs in medicine” section.
  
  Tutorial website at https://sites.google.com/view/miccai-2024-tutorial/home.

• (8 October 2024) Paper published on BMC Medical Informatics and Decision Making - A hybrid framework with large language models for rare disease phenotyping - at DOI:10.1186/s12911-024-02698-7
  
  Rare diseases pose significant challenges in diagnosis and treatment due to their low prevalence and heterogeneous clinical presentations. Unstructured clinical notes contain valuable information for identifying rare diseases, but manual curation is time-consuming and prone to subjectivity. This study aims to develop a hybrid approach combining dictionary-based natural language processing (NLP) tools with large language models (LLMs) to improve rare disease identification from unstructured clinical reports. The proposed hybrid approach demonstrates superior performance compared to traditional NLP systems and standalone LLMs. LLaMA3 and Phi3-mini achieve the highest F1 scores in rare disease identification. Few-shot prompting with 1-3 examples yields the best results, while knowledge-augmented generation shows limited improvement. Notably, the approach uncovers a significant number of potential rare disease cases not documented in structured diagnostic records, highlighting its ability to identify previously unrecognized patients.
  
  Read it at DOI:10.1186/s12911-024-02698-7.

• (3 August 2024) Paper accepted by the International Workshop on Trustworthy Artificial Intelligence for Healthcare 2024 - Human-in-the-Loop Chest X-Ray Diagnosis - Enhancing Large Multimodal Models with Eye Fixation Inputs - at DOI:10.1007/978-3-031-67751-9_6
  
  In the realm of artificial intelligence-assisted diagnostics, recent advances in foundational models have shown great promise, particularly in medical image computing. However, the current scope of human-computer interaction with these models is often limited to inputting images and text prompts. In this study, we propose a novel human-in-the-loop approach for chest X-ray diagnosis with a large language and vision assistant using eye fixation prompts. The eye fixation prompts contain the location and duration of a radiologist’s attention during chest X-ray analysis. This assistant interacts with a radiologist in two ways - diagnosis recommendations of possible diseases and diagnosis report confirmation. The results show the enhanced human-computer interaction with the eye fixation prompt significantly improves the accuracy of the large multimodal model’s performance in differential diagnosis and report confirmation. Fine-tuning with just 658 reports with fixation information further boosted the performance of the LLaVA-1.5, surpassing the previous state-of-the-art model LLaVA-ERR, which was trained on 17k MIMIC reports, by 5%. Our study highlights that this novel approach can better assist radiologists in clinical decision-making in a reciprocal interaction where the models also benefit from the domain expertise of radiologists.
  
  Read it at DOI:10.1007/978-3-031-67751-9_6.

• (1 August 2024) Paper accepted by the BMJ Medicine - Multimorbidity and adverse outcomes following emergency department attendance - population based cohort study - at DOI:10.1136/bmjmed-2023-000731
  
  As part of the NIHR funded AIM-CISC project, this collaborative work led by Edinburgh University colleagues aims to describe the effect of multimorbidity on adverse patient centred outcomes in people attending emergency department. Multimorbidity was defined as at least two conditions from the Elixhauser comorbidity index. Multivariable logistic or linear regression was used to assess associations of multimorbidity with 30 day mortality (primary outcome), hospital admission, reattendance at the emergency department within seven days, and time spent in emergency department (secondary outcomes). Primary analysis was stratified by age (<65v ≥65years). 451,291 people had 1,273,937 attendances to emergency departments during the study period. 43,504 (9.6%) had multimorbidity, and people with multimorbidity were older (median 73 v 43 years), more likely to arrive by emergency ambulance (57.8% v 23.7%), and more likely to be triaged as very urgent (23.5% v 9.2%) than people who do not have multimorbidity. After adjusting for other prognostic covariates, multimorbidity, compared with no multimorbidity, was associated with higher 30 day mortality (8.2% v 1.2%, adjusted odds ratio 1.81 (95% confidence interval (CI) 1.72 to 1.91)), higher rate of hospital admission (60.1% v 20.5%, 1.81 (1.76 to 1.86)), higher reattendance to an emergency department within seven days (7.8% v 3.5%, 1.41 (1.32 to 1.50)), and longer time spent in the department (adjusted coefficient 0.27h (95% CI 0.26 to 0.27)). The size of associations between multimorbidity and all outcomes were larger in younger patients - for example, the adjusted odds ratio of 30 day mortality was 3.03 (95% CI 2.68 to 3.42) in people younger than 65years versus 1.61 (95% CI 1.53 to 1.71) in those 65years or older. Almost one in ten patients presenting to emergency department had multimorbidity using Elixhauser index conditions. Multimorbidity was strongly associated with adverse outcomes and these associations were stronger in younger people. The increasing prevalence of multimorbidity in the population is likely to exacerbate strain on emergency departments unless practice and policy evolve to meet the growing demand.
  
  Read it at DOI:10.1136/bmjmed-2023-000731 .