Evidence map›Paper›PMID 41346538›Full record

ArticleInternational journal of trichology

Comment on Artificial Intelligence in Trichology.

Ralph Michel Trüeb

Abstract readComment
In one paragraph

Article in International journal of trichology. 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

5 · Who and what money

Authors and funding

1 author.

Ralph Michel TrüebCenter for Dermatology and Hair Diseases Professor Trüeb, University of Zurich, Zurich, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) is intelligence exhibited by computation, as opposed to natural human intelligence. Founded as an academic discipline in 1956, AI went through cycles of optimism, followed by periods of disappointment. The growing use of AI in our century is influencing a shift toward increased automation, data-driven decision-making, and the integration of AI systems into various areas of life, including health care. On the occasion of the European Academy of Dermatology and Venereology Spring Symposium 2024, reference was made to AI in dermatology, specifically trichology. Particularly in the domain of trichoscopy, AI's contribution to automated image analysis stands out. Due to the fairly standardized imaging and a limited amount of diagnoses, trichoscopic images have become a center of interest for automated medical image analysis and its commercial utilization. Yet, costly, multi-parameter computer-assisted trichoscopic image analysis is the least effective in medical practice. Using dermoscopy, signature patterns are seen in a range of scalp and hair conditions that have provided the foundation for diagnostic algorithms. Algorithms discourage physicians from thinking independently and with creativity. Doctors are trained to romance with technology. However, healing is replaced with treating, caring is supplanted by managing, and the art of listening is taken over by technology. The power of technology, particularly computer-based, may shake the confidence of a specialist in his initial diagnosis. Nevertheless, machines cannot replace the doctor's mind, and his thinking about what he sees and what he does not see. Ultimately, common AI mistakes are often not too different from mistakes born out of natural human error. Taking all of this into account, it does not seem wise to precociously hail an evolving technology in public without the respective experimentation and experience to back up its factual utility in our practice.

Indexed as

Algorithmsartificial intelligenceautomated image analysistrichoscopy

Identifiers

PMID41346538
PMCPMC12674493

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-SA
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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.