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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM50727

Artificial Intelligence in Cosmetic- Dermatology

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM50727

Volume:

9 |

6 |

Jun 2025

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/ijsrem50727

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

MMP

Mrs. M. Parimala

BK

B. Keerthana

S

S.Harika

C

Ch.Akhila

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Abstract

This paper presents an AI-driven prototype for personalized skincare and haircare product recommendation. The system integrates a rule-based chatbot interface with a structured product knowledge base in JSON format. Users interact via natural language, describing dermatological or hair-related concerns (e.g., oily skin, acne, dandruff), and receive relevant product suggestions based on

KEYWORDS

matching and routine detection. The chatbot is built using Python and deployed through a Gradio web interface allowing both text and image input. Although image analysis is currently implemented as a placeholder the framework is designed for future integration of deep learning models (e.g. CNNs) to classify skin and scalp conditions. This work lays the foundation for an accessible and extensible AI assistant in the domain of cosmetic dermatology. Future iterations will incorporate PyTorch-based convolutional neural networks for condition classification and real-time severity scoring. The prototype demonstrates the feasibility of combining structured knowledge and conversational AI for enhancing user self-care decisions in dermatology and trichology

Cite This Article

Mrs. M. Parimala, B. Keerthana, S.Harika, Ch.Akhila, "Artificial Intelligence in Cosmetic- Dermatology," International Journal of Scientific Research in Engineering and Management, vol. 9, no. 6, Jun 2025. https://doi.org/10.55041/ijsrem50727

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Publication Date

6/19/2025

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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5

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The authors declare no conflict of interest related to this research work.

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