This week’s insights highlight three of the latest findings in the application of artificial intelligence (AI) in the healthcare and pharmaceutical industries.
AI model may improve breast cancer risk prediction
Recent research from JAMA Network Open, published during Women’s Health Month in May, has highlighted the potential of artificial intelligence (AI) and deep learning (DL) to improve breast cancer risk prediction beyond traditional breast density assessment.
Lamb et al. found that AI-based models proved more accurate than radiologist assessments at identifying women at risk of developing breast cancer. The authors suggested that relying solely on breast density may overlook certain risk factors for breast cancer.
According to the study, integrating DL into screening procedures could support breast cancer prevention and early intervention strategies.
Implementing AI to predict diabetes-related amputation risk
Researchers in Singapore have developed an AI-based model to predict the risk of lower-limb amputation in patients with diabetes.
The tool, named Lower Extremity Amputation – Neural Network Model (LEA-Net), analyses clinical and patient data to identify individuals at high risk of diabetes-related amputations. Diabetes-related amputations remain a major global healthcare challenge; they considerably worsen quality of life and increase healthcare costs.
According to a press release, the AI system may help clinicians improve risk stratification and personalise patient management strategies.
How can agentic AI accelerate pharmaceutical market entry
A recent article from pharmaphorum explored how agentic AI could help pharmaceutical companies optimise market entry strategies.
Unlike traditional generative AI systems that primarily respond to prompts, agentic AI can autonomously perform multi-step tasks, coordinate processes, and support decision-making. The article highlighted potential applications in regulatory preparation, market access planning, and stakeholder engagement.
According to the article, agentic AI may help improve speed-to-market by automating repetitive processes. However, the article emphasised that human oversight remains very much necessary.
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