ReviewNPJ digital medicine2025
Unlocking the potential: multimodal AI in biotechnology and digital medicine-economic impact and ethical challenges.
Review in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
5 citing papers in PubMed.
- Endothelial Continuum and Capillary Specialization in Pulmonary Vascular Development.Arteriosclerosis, thrombosis, and vascular biology · 2026Review
- Artificial Intelligence in Lung Cancer: A Narrative Review of Recent Advances in Diagnosis, Biomarker Discovery, and Drug Development.Pharmaceutics · 2026Review
- Checkpoint inhibition and beyond: Precision immune engineering for the immune-privileged landscape of ocular malignancies.BioImpacts : BI · 2026Review
- Editorial: Smart forecasting: deep learning and explainable AI for real-world time series prediction.Frontiers in big data · 2026Article
- Glioinflammation: disease-associated microglia and astrocytes in psychiatric disorders, neurodegeneration, and senescence.Frontiers in cellular neuroscience · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial Intelligence (AI) is revolutionizing biotechnology by accelerating advancements in drug discovery, genomics, medical imaging, and personalized medicine, thereby enhancing efficiency and reducing healthcare costs. This review emphasizes the transformative potential of multimodal AI-systems that integrate diverse data types such as genomic, clinical, and imaging data-to deliver more accurate and holistic biomedical insights. We explore AI's economic impact, role in driving innovation, and implications for both researchers and policymakers. Additionally, the review addresses key challenges, including data quality, algorithmic transparency, and ethical concerns, highlighting the urgent need for explainable AI models, robust regulatory frameworks, and equitable implementation to ensure responsible and impactful adoption across global healthcare systems.
Identifiers
What OpenQuestion holds
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.