ReviewFrontiers in artificial intelligence2024
Ethics and responsible AI deployment.
Review in Frontiers in artificial intelligence, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 107 citations in OpenAlex.
- Ontology-Driven and Human-Centric Digital Twins in Hospitality: A Survey and Research Agenda.Sensors (Basel, Switzerland) · 2026Pooled it
- Artificial intelligence based on forensic medical databases: construction, application, and prospects.Forensic science, medicine, and pathology · 2026Review
- Examining developer perspectives on medical AI regulatory frameworks.NPJ digital medicine · 2026Article
- A Conversational Platform (Okaya) for Multimodal Digital Biomarkers of Fatigue, Cognition, and Mental Health: Feasibility Observational Study.JMIR formative research · 2026Observational
- Dynamic Personalized Optimization: An AI Functionality Framework for Digital Therapeutics.JMIR medical informatics · 2026Article
- AMULED: Addressing Moral Uncertainty using Large language models for Ethical Decision-making.Frontiers in artificial intelligence · 2026Article
- The context of AI transformation: perceptions of AI ethicality and their impact on trust and use intentions.Frontiers in artificial intelligence · 2026Article
- Artificial Intelligence for the Diagnosis and Management of Patellofemoral Instability: A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2025Review
- Article
- The ethical challenges in the integration of artificial intelligence and large language models in medical education: A scoping review.PloS one · 2025Article
- Patient-Centric In Vitro Fertilization Prognostic Counseling Using Machine Learning for the Pragmatist.Seminars in reproductive medicine · 2024Review
- Review
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 at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As Artificial Intelligence (AI) becomes more prevalent, protecting personal privacy is a critical ethical issue that must be addressed. This article explores the need for ethical AI systems that safeguard individual privacy while complying with ethical standards. By taking a multidisciplinary approach, the research examines innovative algorithmic techniques such as differential privacy, homomorphic encryption, federated learning, international regulatory frameworks, and ethical guidelines. The study concludes that these algorithms effectively enhance privacy protection while balancing the utility of AI with the need to protect personal data. The article emphasises the importance of a comprehensive approach that combines technological innovation with ethical and regulatory strategies to harness the power of AI in a way that respects and protects individual privacy.
Indexed as
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.