Industry Insights AI: 31/10/25
This week’s insights post explores some of the latest developments and discussion points regarding the application of artificial intelligence (AI) in healthcare.
AI can help address clinician burnout
Burnout is a common problem in healthcare that affects almost half of healthcare workers. Recent findings suggest that AI tools can help address this problem.
A randomised study of nearly 300 Swedish healthcare workers found that combining ‘demands’ and ‘control’ modules within an AI digital stress management programme effectively reduced burnout, anxiety and emotional exhaustion amongst healthcare workers. The authors emphasised how AI-enabled health apps can go beyond mindfulness prompts and offer adaptive, evidence-based micro-interventions. These tools may help prevent burnout while supporting mental wellness in high-pressure environments.
CDC link – Burnout; Link to study
Machine learning aids in early detection of MASLD in diabetes
A recent study published in Diabetes, Obesity and Metabolism presented a machine learning model capable of successfully identifying metabolic dysfunction–associated steatotic liver disease (MASLD) in patients with type 2 diabetes. MASLD is a chronic liver condition caused by high levels of fat in the liver. This model, known as XGBoost, used data from 3,836 adults specifying 13 routine metrics, such as BMI, triglyceride fat levels and HbA1c. According to the authors, their tool is able to generate risk profiles in real time through a web-based calculator.
AI may considerably improve alopecia assessment scores
A case report published in JAAD Case Reports showcased an AI tool that is able to calculate the area of hair loss (AI-SALT score) with higher accuracy than traditional manual scoring. Alopecia areata, or spot baldness, is a condition associated with hair loss from some or all areas of the body. In a patient treated for alopecia areata, the AI system detected subtle regrowth that manual assessment missed, thereby motivating patient adherence to therapy. That said, the researchers acknowledged the study’s limitations and stressed that further validation is necessary to increase the tool’s applicability.
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