SynthesisBMC medical informatics and decision making2026
Ethical challenges to patient autonomy in the era of artificial intelligence: a systematic review.
Synthesis in BMC medical informatics and decision making, 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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence (AI) has rapidly transformed clinical practice by improving diagnostic accuracy and enhancing decision-making processes. However, its growing role in patient care raises profound ethical concerns regarding patient autonomy. This systematic review synthesizes current evidence on how AI may support, challenge, or reshape patient autonomy in healthcare.
methodsThis review was conducted in accordance with PRISMA guidelines. Searches were conducted in PubMed, Scopus, Web of Science, Cochrane Library, and Science Direct for studies published between January 2010 and June 2025. The initial search was performed in September 2024 and updated to June 2025 during the peer review process. Eligible studies included empirical, conceptual, or mixed-methods research that addressed AI applications in healthcare and their implications for patient autonomy. Quality assessment was performed using the STROBE, CASP, and MMAT checklists. A narrative synthesis and thematic analysis were conducted.
resultsOf 472 records screened from the initial search, 11 studies met the eligibility criteria. The updated search (October 2024 - June 2025) identified 3 additional eligible studies, bringing the final total to 14 studies. Seven overarching themes emerged: (1) philosophical and existential dimensions of autonomy, (2) human-centered and autonomy-supportive design, (3) cognitive threats and diminished agency, (4) moral risks and social inequality, (5) covert control and preference manipulation, (6) balancing human and AI authority in clinical decisions, and (7) the need for transparency, dialogue, and adaptive autonomy. Results show that AI can enhance autonomy through shared decision-making and improved information availability, but significant risks exist, including algorithmic opacity, nudging, clinician de-skilling, and increased inequality. The three 2025 studies independently confirmed all seven themes, with Alnawafleh et al. finding that patient autonomy is the predominant ethical concern (67%) in AI-integrated nursing practice.
conclusionAI introduces both opportunities and threats to patient autonomy. Healthcare systems must ensure transparent, human-centered, and ethically governed AI deployment. Autonomy should function as a guiding principle in AI design, training, and regulation. Strong policy frameworks, validated measurement tools, implementation science approaches, and clinical education are essential to ensure that AI serves as a tool for empowerment rather than a source of subtle control. CLINICAL TRIAL NUMBER: Not applicable.
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.