ReviewESMO real world data and digital oncology2024
Human-centered AI as a framework guiding the development of image-based diagnostic tools in oncology: a systematic review.
Review in ESMO real world data and digital oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Artificial intelligence diagnostic tools (AIDTs) in oncology show high image classification accuracy but limited clinical adoption. Their adoption could be enhanced by (i) using user feedback during the software design, (ii) demonstrating that AIDTs improve the user's decisions, and (iii) providing explanations of AI decisions tailored to the user, three aspects central to human-centered AI (HCAI). This review assesses these three aspects in AIDTs for oncology in general, exemplifying its concepts in the established field of skin cancer diagnostics as a specific use case. Materials and methods: We carried out three Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) searches using PubMed and ScienceDirect, limiting the results to articles published from 2019 to 2024. The first search focused on articles that used user feedback to develop AIDTs. The second search addressed whether AIDT improves dermatologists' decisions. The third search targeted explainable AI in skin cancer. Results: Five studies incorporated user feedback in AIDT design for cancer. Zooming in on AIDT for skin cancer, nine studies (3/37 in 2019, 3/93 in 2023) indicated that AIDTs improve dermatologists' decisions in experimental ( Conclusions: User feedback has been used to develop AIDTs tailored to clinicians' needs. Evidence shows that AIDTs can improve clinicians' decisions. This, combined with XAI, increases clinicians' trust in AIDTs, potentially favoring their widespread usage.
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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.