ReviewNPJ digital medicine2024
Ethical data acquisition for LLMs and AI algorithms in healthcare.
Review in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- Building health-literate artificial intelligence.Nature human behaviour · 2026Review
- Reclaiming informed consent to train mental health AI with patient data.NPJ digital medicine · 2026Article
- Ethical and Legal Implications of Implementing AI in Gastrointestinal Endoscopy.Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society · 2026Review
- Artificial intelligence for precision oncology from phenotyping and drug discovery to clinical translation.Discover oncology · 2026Review
- Opportunities and challenges of artificial intelligence-assisted endoscopy and high-quality data for esophageal squamous cell carcinoma.World journal of gastrointestinal oncology · 2026Review
- Large language models in hepatology: A systematic review.Hepatology forum · 2026Review
- Wearable Electrochemical Biosensors for Monitoring and Management of Chronic Wounds.Biosensors · 2025Review
- Death risk prediction model for patients with non-traumatic intracerebral hemorrhage.BMC medical informatics and decision making · 2025Article
- AI-driven transformation of precision medicine: a comprehensive narrative review of key application areas, emerging paradigms, and future directions.Frontiers in public health · 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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) algorithms will become increasingly integrated into our healthcare systems in the coming decades. These algorithms require large volumes of data for development and fine-tuning. Patient data is typically acquired for AI algorithms through an opt-out system in the United States, while others support an opt-in model. We argue that ethical principles around autonomy, patient ownership of data, and privacy should be prioritized in the data acquisition paradigm.
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.