Evidence map›Paper›PMID 40501296›Full record

ReviewJournal of cosmetic dermatology2025

Artificial Intelligence in Aesthetic Medicine: Applications, Challenges, and Future Directions.

Mohammed Saleh Al-Dhubaibi, Ghada Farouk Mohammed, Lina Mohammed Atef, Saleh Salem Bahaj, Ahmed Mohammed Al-Dhubaibi, Ahmad Mohammed Bukhari

Abstract readReview
In one paragraph

Review in Journal of cosmetic dermatology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 1 pooled it
–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

13 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

6 authors.

Mohammed Saleh Al-DhubaibiDepartment of Dermatology, College of Medicine, Shaqra University, Dawadmi, Saudi Arabia.ORCID https://orcid.org/0000-0002-6418-6647
Ghada Farouk MohammedDepartment of Dermatology, Venereology, and Sexology, Faculty of Medicine, Suez Canal University, Ismailia, Egypt.ORCID https://orcid.org/0000-0003-3074-1347
Lina Mohammed AtefDepartment of Dermatology, Venereology, and Sexology, Faculty of Medicine, Suez Canal University, Ismailia, Egypt.ORCID https://orcid.org/0000-0003-2455-8669
Saleh Salem BahajDepartment of Microbiology and Immunology, Faculty of Medicine and Health Sciences, Sana'a University, Yemen.ORCID https://orcid.org/0000-0001-6582-907X
Ahmed Mohammed Al-DhubaibiMedical Student, College of Medicine, Marmara University, Istanbul, Turkey.ORCID https://orcid.org/0009-0000-8928-2443
Ahmad Mohammed BukhariMedical Student, College of Medicine, Umm Al-Qua University, Makkah, Saudi Arabia.ORCID https://orcid.org/0009-0004-3235-2809

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial Intelligence (AI) is transforming healthcare by enhancing diagnostics, treatment personalization, and operational efficiency. In aesthetic medicine-a field blending medical expertise with artistic judgment-AI is increasingly being used to improve precision, optimize treatment outcomes, and personalize patient care. However, its integration presents both opportunities and ethical challenges, necessitating a critical evaluation of its role in this evolving field.

objectiveThis study examines AI's applications in aesthetic medicine, focusing on its role in facial analysis, robotic-assisted procedures, predictive patient outcome modeling, and personalized treatment planning. Additionally, it explores ethical concerns, algorithmic biases, data privacy issues, and regulatory challenges affecting AI adoption in aesthetic practices.

methodsA comprehensive review of AI-driven technologies in aesthetic medicine was conducted, analyzing literature on machine learning (ML), deep learning, and computer vision applications. Case studies on AI-assisted facial symmetry analysis, robotic hair transplantation, and predictive analytics in patient care were examined to evaluate AI's effectiveness and limitations.

resultsAI enhances aesthetic procedures by improving diagnostic accuracy, offering virtual simulations of treatment outcomes, and enabling hyper-personalized treatment plans based on patient data. AI-driven chatbots and virtual assistants streamline patient interactions, while robotic systems assist in precision-based tasks such as laser treatments and hair restoration. However, challenges such as biased training data, lack of transparency in AI decision-making, and inconsistencies in regulatory approvals hinder widespread adoption. The integration of AI in aesthetic medicine presents a paradigm shift from traditional approaches to data-driven, personalized interventions. However, ethical concerns such as data privacy, informed consent, and algorithmic fairness must be addressed. Overreliance on AI may diminish the human-centric approach essential in aesthetic procedures, where patient expectations and subjective perceptions of beauty play a crucial role. Collaboration between technologists, clinicians, and policymakers is necessary to develop standardized AI guidelines that ensure fairness, safety, and efficacy.

conclusionAI has the potential to revolutionize aesthetic medicine by improving precision, efficiency, and patient satisfaction. However, its successful implementation requires balancing technological advancements with ethical considerations and regulatory frameworks. Future research should focus on integrating AI with emerging technologies such as augmented reality (AR) and genomic-based personalization to enhance aesthetic outcomes while maintaining transparency and patient trust.

Indexed as

Artificial IntelligenceCosmetic TechniquesEstheticsHumansPrecision Medicineaesthetic medicineartificial intelligencemachine learning

Identifiers

PMID40501296
PMCPMC12159716

What OpenQuestion holds

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LicenceCC BY
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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.