Evidence map›Paper›PMID 39678882›Full record

ArticleJournal of thoracic disease2024

Insights into artificial intelligence and our intelligence-on the frontier of lung cancer screening.

Philippa Jane Temple Bowers, Frazer Michael Kirk

Abstract read
In one paragraph

Article in Journal of thoracic disease, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Philippa Jane Temple BowersCardiothoracic Surgery Department, The Prince Charles Hospital, Chermside, Australia.ORCID https://orcid.org/0000-0002-6561-6872
Frazer Michael KirkCardiothoracic Surgery Department, The Prince Charles Hospital, Chermside, Australia.ORCID https://orcid.org/0000-0001-6655-1778

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper explores the potential of artificial intelligence (AI) in lung cancer screening programs, particularly in the interpretation of computed tomography (CT) scans. The authors acknowledge the benefits of AI, including faster and potentially more accurate analysis of scans, but also raise concerns about clinician trust, transparency, and the deskilling of radiologists due to decreased scan exposure. The rise of AI in medicine and the introduction of national lung cancer screening programs are both increasing contemporarily and naturally the overlap and interplay between the two in the future is ensured. The paper highlights the importance of human-AI collaboration, emphasizing the need for interpretable models and ongoing validation through clinical trials. The promising results and problems uncovered the current pilot studies is explored. Building trust with patients and clinicians is also crucial, considering factors like disease risk perception and the human element of patient interaction. The authors conclude that while AI offers significant promise, widespread adoption hinges on addressing ethical considerations and ensuring a balanced, synergistic relationship between AI and medical professionals. This report aims to provide a talking point to inspire conversations around, and prepare clinicians for the rapidly approaching frontier that is AI in healthcare.

Indexed as

artificial intelligence (AI)diagnostic screening programsLung cancer

Identifiers

PMID39678882
PMCPMC11635207

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.