In this week’s round of insights, we discuss three of the latest findings in the application of artificial intelligence (AI) in diagnosing and monitoring disease.
Ultrasound AI models can improve prognosis in lymphoma
New research from the Annals of Hematology has found that ultrasound-based AI models may offer an effective, non-invasive approach to risk stratification in diffuse large B-cell lymphoma (DLBCL).
These models combine imaging with clinical parameters and can predict patient outcomes with high accuracy. The findings indicated that integrating imaging-derived biomarkers into predictive models could support early identification of high-risk patients and enable tailored treatment strategies.
The authors stressed, however, that further validation is necessary before clinical integration.
AI predicts cognitive status in multiple sclerosis
A recent study in the Journal of Neurology has demonstrated how AI-driven models can accurately predict cognitive status in patients with multiple sclerosis (MS).
Cognitive impairment is common in MS but often underdiagnosed because of variability in symptoms. Fuchs et al. used machine learning (ML) techniques to analyse clinical and demographic data to classify cognitive function with predictive performance.
These tools may help clinicians identify patients at risk early, thus enabling timely intervention and personalised care.
Machine learning may predict MS progression
Another study from the Journal of Neurology has stated that machine learning can improve the ability to predict disease progression in multiple sclerosis (MS).
According to the authors, these tools can accurately forecast disability progression. Accurate prediction of progression remains highly necessary for MS, as its course can vary considerably between patients.
Machine learning models offer the potential to move toward proactive disease management. However, challenges remain around data standardisation, validation and integration into the clinic.
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