Evidence map›Paper›PMID 39803113›Full record

ReviewCureus2024

Implementing Artificial Intelligence in Family Medicine: Challenges and Limitations.

Paraskevi F Katsakiori, George C Kagadis, Francesk Mulita, Markos Marangos

Abstract readReview
In one paragraph

Review in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–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

3 citing papers in PubMed.

  1. Review
  2. AI-powered tools in family medicine: Bridging technology and practice.Journal of family medicine and primary care · 2026
    Article
  3. Artificial intelligence in preventive care in primary health care settings: a scoping review.Archives of medical sciences. Atherosclerotic diseases · 2026
    Article
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

4 authors.

Paraskevi F KatsakioriDepartment of Family Medicine, Health Center of Akrata, Akrata, GRC.
George C KagadisFaculty of Medicine, University of Patras, Rion, GRC.
Francesk MulitaDepartment of Surgery, General University Hospital of Patras, Patras, GRC.
Markos MarangosFaculty of Medicine, University of Patras, Rion, GRC.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

General practice/family medicine is a primary healthcare discipline that focuses on providing comprehensive, patient-centered care to individuals and families across their lifespan. This study aimed to assess the implementation of artificial intelligence (AI) in general practice/family medicine regarding the 12 characteristics of its discipline as described in the recently revised European Definition of General Practice/Family Medicine. AI has the potential to revolutionize family medicine practice by improving the accuracy, efficiency, and effectiveness of various clinical tasks. From diagnostic decision-making to chronic condition management and clinical workflow optimization, AI-powered tools offer a range of benefits for patients and clinicians. However, appropriate education and training of the primary healthcare providers are deemed necessary to maximize benefits and minimize potential pitfalls. To ensure efficient and safe integration of AI in medical practice, important implications regarding privacy, malpractice, bias, autonomy, and overtreatment should also be addressed. AI application should be treated as a practical, assistive tool for the clinician, that can hardly take over decision-making and cannot replace the doctor-patient relationship.

Indexed as

artificial intelligencefamily medicinegeneral practicemedical educationprimary health care

Identifiers

PMID39803113
PMCPMC11719217

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.