Evidence map›Paper›PMID 41098154›Full record

ReviewAvicenna journal of medicine2025

Ethical Challenges and Current Opportunities of Artificial Intelligence in Cardiology.

Mohammed A R Chamsi-Pasha, Hassan Chamsi-Pasha

Abstract readReview
In one paragraph

Review in Avicenna journal of medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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.

Mohammed A R Chamsi-PashaDepartment of Cardiovascular Medicine, Houston Methodist Hospital, Houston, Texas, United States.
Hassan Chamsi-PashaDepartment of Cardiology, European Medical Center, Jeddah, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) has great potential in diagnosing, managing, and predicting cardiovascular diseases through imaging, clinical decision support, remote monitoring, and optimizing treatment strategies. AI in cardiology brings unique ethical issues that need careful examination and resolution. There are several ethical concerns, including privacy, bias, trust, accountability, and responsibility. AI systems handle large quantities of data, which can present privacy and security risks if hacked or exploited illegally. AI models may exhibit biases due to limited or nonrepresentative training data sets, impacting their reliability. ChatGPT shows potential in cardiology for patient education, clinician support, and research facilitation. However, its use in direct patient care is limited due to concerns regarding accuracy, ethical issues, and the necessity for human oversight. AI's responsible development and application in cardiology hinges on thorough evaluation, regulatory compliance, and ethical oversight to ensure safety and effectiveness. The collaboration of health care professionals, data scientists, ethicists, researchers, and policymakers is essential for the advancement of AI in cardiology and the resolution of its associated challenges. Collaboration is mandatory to ensure AI tools improve patient care while upholding the highest medical standards. The incorporation of AI into cardiology offers significant potential for the coming years. With its extensive data sets and strong evidence-based guidelines, cardiology is ideally suited to using this technology.

Indexed as

artificial intelligencecardiovascular diseaseChatGPTethics

Identifiers

PMID41098154
PMCPMC12520755

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.