ReviewJMA journal2026
Ethical Aspects of Artificial Intelligence in Precision Medicine for Obesity Management.
Review in JMA journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) has established itself as a key tool in the development of precision medicine, offering opportunities to improve risk prediction, early diagnosis, and personalized treatments for obesity. However, its implementation poses ethical challenges that cannot be ignored. The use of sensitive data such as genetic, metabolic, and behavioral information raises questions about privacy, security, and informed consent. Furthermore, the existence of algorithmic biases can reproduce inequities and limit the validity of models in diverse populations, particularly in low-resource settings. Another critical aspect is the lack of transparency in many AI systems, whose operation as "black boxes" makes it difficult to interpret their recommendations and weakens trust between professionals and patients. Legal accountability for errors in clinical decision-making, limited accessibility of high-cost technologies, and the imperative to respect patient autonomy and cultural context highlight the urgency of establishing a clear and enforceable ethical framework. This narrative review analyzes the main ethical dilemmas associated with the use of AI in precision medicine for obesity, highlighting challenges related to equity, transparency, social justice, and clinical accountability. The need for responsible technological development is raised, ensuring respect for patients' rights and promoting equitable use of these innovative tools in addressing one of the most complex and prevalent diseases worldwide.
Indexed as
Identifiers
What Socratic 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 Socratic graph.