Evidence map›Paper›PMID 40565456›Full record

ArticleHealthcare (Basel, Switzerland)2025

Exploring Opportunities and Challenges of AI in Primary Healthcare: A Qualitative Study with Family Doctors in Lithuania.

Kotryna Ratkevičiūtė, Vygintas Aliukonis

Abstract read
In one paragraph

Article in Healthcare (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Atencion primaria · 2026
    Pooled it
  2. Pooled it
  3. Review
  4. Review
  5. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Kotryna RatkevičiūtėCentre for Health Ethics, Law and History, Institute of Health Sciences, Faculty of Medicine, Vilnius University, 10257 Vilnius, Lithuania.
Vygintas AliukonisCentre for Health Ethics, Law and History, Institute of Health Sciences, Faculty of Medicine, Vilnius University, 10257 Vilnius, Lithuania.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

objectivesAI is transforming healthcare, with family doctors at the forefront. As primary care providers, they play a key role in integrating AI into patient care. Despite AI's potential, concerns about trust, data privacy, and physician autonomy persist. Little research exists on family doctors' perspectives. This study investigates the views of Lithuanian family physicians on AI's ethical challenges and benefits, aiming to support responsible implementation. MATERIALS AND

methodsA review of the literature was conducted (2015-2025) using Google Scholar, PubMed, and Scopus. This qualitative study explored family physicians' perceptions of AI in Lithuania, focusing on ethics, AI's role, experience, training, and concerns about replacement. Informed consent and ethical guidelines were followed.

resultsAI has strong potential in family medicine, automating administrative tasks, improving diagnostic accuracy, and supporting patient autonomy. AI tools, like clinical documentation systems and smart devices save time, allowing physicians to focus on patient care. They also improve diagnostic precision, enabling earlier detection of conditions such as cancer and coronary artery disease. Physicians express concerns about AI's reliability, biases, and data privacy. While AI boosts efficiency, many emphasize the importance of human oversight in decision-making, especially in complex cases. Privacy concerns around health data and the need for stricter regulations are crucial. Lithuanian family physicians generally accept AI as a helpful tool for routine tasks but remain cautious regarding its trustworthiness. Job displacement concerns were not prevalent, with AI seen as a tool to augment rather than replace their role. Successful AI integration requires training, transparency, and ethical guidelines to build trust and ensure patient safety.

conclusionsAI enhances efficiency in family medicine but requires structured training and ethical safeguards to address concerns about data privacy, accountability, and bias. AI is viewed as supportive, not as a replacement.

Indexed as

artificial intelligenceblack boxethicsfamily medicineprimary care

Identifiers

PMID40565456
PMCPMC12193594

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.