2025

• (18 April 2025) Published in American Journal of Hypertension – A Transformer-Based Framework for Counterfactual Estimation of Antihypertensive Treatment Effect on COVID-19 Infection Risk - A Proof-of-Concept Study – at https://doi.org/10.1093/ajh/hpaf055
  
  A new study in the American Journal of Hypertension investigates the relationship between antihypertensive medications and COVID-19 infection risk. The research employed a transformer-based framework to analyze real-world data from over 300,000 patients.
  
  Key findings indicate that while ACE inhibitors showed a negligible effect on COVID-19 risk, beta-blockers and calcium channel blockers were associated with a protective effect. Statins and thiazides showed a slight increase in risk.
  
  This study demonstrates the potential of advanced causal inference models in evaluating treatment outcomes in complex healthcare scenarios and offers important insights for clinical consideration.
  
  Read the full paper at https://doi.org/10.1093/ajh/hpaf055.

• (8 April 2025) Paper published in Journal of Imaging Informatics in Medicine – How Do Radiologists Currently Monitor AI in Radiology and What Challenges Do They Face? – at DOI:10.1007/s10278-025-01493-8
  
  As AI tools become more common in radiology, monitoring their performance is increasingly important—but still underdeveloped. A recent qualitative study interviewed 16 radiologists across Europe and the U.S., revealing that many AI systems are still in early validation phases. Current monitoring typically involves manual, retrospective comparisons to radiology reports—effective, but labor-intensive.
  
  Key barriers include a lack of standardized monitoring guidelines, limited technological tools, and constrained resources. The study recommends mixed-method monitoring, dedicated governance teams, and long-term resource planning.
  
  This research highlights the need for clearer frameworks and investment to ensure AI improves clinical workflows.
  
  Read it at DOI:10.1007/s10278-025-01493-8.

• (4 April 2025) Preprint published on arXiv – Towards Deployment-Centric Multimodal AI Beyond Vision and Language – at arXiv:2504.03603v1
  
  This work introduces a deployment-centric workflow for multimodal AI, emphasizing real-world applicability beyond just vision and language models. While multimodal systems hold immense potential in areas like healthcare and engineering, deployment challenges are often an afterthought. This paper pushes for a proactive approach—integrating data readiness, model robustness, and system integration into early development.
  
  By shifting focus from just performance benchmarks to deployment feasibility, this research bridges the gap between prototypes and practical implementation.
  
  It’s a compelling case for aligning AI innovation with real-world impact.
  
  Read it at arXiv:2504.03603v1.

• (1 April 2025) Preprint published on arXiv – IHC-LLMiner- Automated extraction of tumour immunohistochemical profiles from PubMed abstracts using large language models – at arXiv:2504.00748v1
  
  This study introduces IHC-LLMiner, an automated pipeline that extracts immunohistochemical (IHC) tumour profiles from PubMed abstracts using large language models (LLMs). The pipeline performs two tasks - classifying abstracts for relevance and extracting IHC-tumour profiles from relevant abstracts.
  
  The fine-tuned Gemma-2 model achieved 91.5% accuracy in classification and 63.3% correctness in profile extraction, outperforming GPT4-O in both accuracy and speed. Extracted profiles were normalized to UMLS concepts, facilitating consistent analysis.
  
  IHC-LLMiner demonstrates potential for large-scale IHC data mining, enhancing accessibility and utility for research and clinical applications.
  
  Read it at arXiv:2504.00748v1.

• (3 February 2025) Paper published on Lancet Digital Health - Prevalence and demographics of 331 rare diseases and associated COVID-19-related mortality among 58 million individuals- a nationwide retrospective observational study - at DOI:10.1016/S2589-7500(24)00253-X
  
  This study of over 58 million people has identified eight rare diseases that carry significantly increased risks for COVID-19-related mortality in fully vaccinated individuals. This important research calls for better inclusion of rare diseases in public health strategies, including future pandemic planning, vaccination policies, and NHS service provision. We uncovered eight rare diseases linked with an increased risk of dying from COVID-19 and found that people with rare diseases were nearly five times more like to die from COVID-19 than the general population. The conditions carrying the highest risk were infertility disorders and kidney diseases.
  
  The rarity of some diseases can contribute to a lack of comprehensive data due to lengthy waits for diagnosis. And as the study period spans two ‘waves’ of the pandemic, it’s difficult to untangle the effects of different COVID-19 variants, as well as overlapping symptoms in multiple illnesses.
  
  However, this study takes advantage of one of our health system’s unique strengths, having population-wide granular data coverage in EHRs and diversity in representation, highlighting signfificant health inequalities faced by specific ethnic groups in which some rare diseases were disproportionately common. It’s also the first time an analysis has been performed on so many rare diseases, and the impact of COVID-19 on the people with such conditions, a landmark moment for rare disease research.
  
  Living with a rare disease can be extremely challenging, from a difficult diagnosis to relentless appointments to manage health and wellbeing. But these findings fill a critical knowledge gap, emphasising the power of electronic health record data to bring to light the prevalence and outcomes of rare diseases.
  
  Read it at DOI:10.1016/S2589-7500(24)00253-X.