ArticleEmerging topics in life sciences2025
Artificial intelligence in precision medicine: transforming disease subtyping, medical imaging, and pharmacogenomics.
Article in Emerging topics in life sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Performance and Fairness of a Peripheral Artery Disease Detection Model Across Patient Phenotypes and Health Systems.medRxiv : the preprint server for health sciences · 2026Article
- Applications of artificial intelligence in drug discovery.Emerging topics in life sciences · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Precision medicine marks a transformative shift towards a patient-centric treatment approach, aiming to match 'the right patients with the right drugs at the right time'. The exponential growth of data from diverse omics modalities, electronic health records, and medical imaging has created unprecedented opportunities for precision medicine. This explosion of data requires advanced processing and analytical tools. At the forefront of this revolution is artificial intelligence (AI), which excels at uncovering hidden patterns within these high-dimensional and complex datasets. AI facilitates the integration and analysis of diverse data types, unlocking unparalleled potential to characterise complex diseases, improve prognosis, and predict treatment response. Despite the enormous potential of AI, challenges related to interpretability, reliability, generalisability, and ethical considerations emerge when translating these tools from research settings into clinical practice.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.