Evidence map›Paper›PMID 40995283›Full record

ArticleCureus2025

Evaluating AI-Generated Responses for Patient Counseling and Treatment Decision-Making in Melasma: A Cross-Sectional Study.

Shreya Deoghare, Rashmi Sarkar, Seemal R Desai

Abstract read
In one paragraph

Article in Cureus, 2025. 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

3 authors.

Shreya DeoghareDermatology, Alexis Multi-Speciality Hospital, Nagpur, IND.
Rashmi SarkarDermatology, Lady Hardinge Medical College, New Delhi, IND.
Seemal R DesaiDermatology, The University of Texas Southwestern Medical Center, Dallas, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background Melasma poses challenges in treatment decisions and patient counseling due to its chronic nature and variable response to therapies. Evaluating AI-generated responses from ChatGPT (OpenAI, Inc., San Francisco, CA, USA) and ChatSonic (Writesonic, Inc., San Francisco, CA, USA) could provide insights into their potential role in supporting dermatologists with patient counseling and treatment decision-making for melasma. Aim This study aims to compare responses generated by ChatGPT and ChatSonic for patient counseling and treatment decisions in melasma, as evaluated by senior dermatologists for satisfaction, quality, and reliability. Methodology This cross-sectional observational study, conducted virtually in January 2023, involved asking five questions each about patient counseling and treatment decision-making in melasma to ChatGPT and ChatSonic. Responses were compiled in a Microsoft Excel assessment sheet (Microsoft Corporation, Redmond, WA, USA) and presented in a blinded format to two senior dermatologists (Evaluators A and B) for evaluation of quality (Global Quality Score), reliability (modified DISCERN scale), and satisfaction (Likert scale). Results For patient counseling in melasma, ChatGPT responses were rated higher in satisfaction by Evaluator A, with no significant differences in quality or reliability between the two AI chatbots. For treatment decisions, Evaluator B significantly favored ChatGPT over ChatSonic in both satisfaction and quality. Conclusions AI chatbots such as ChatGPT and ChatSonic can effectively support dermatologists in patient counseling and treatment decision-making for melasma, with ChatGPT demonstrating superior satisfaction and quality.

Indexed as

artificial intelligencecounsellingdecision-makingmelasmapatient satisfaction

Identifiers

PMID40995283
PMCPMC12456358

What OpenQuestion holds

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Registered trials

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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.