Product Images Kiehls Gently Effective Acne Treating Cleansing
View Photos of Packaging, Labels & Appearance
Product Label Images
The following 3 images provide visual information about the product associated with Kiehls Gently Effective Acne Treating Cleansing NDC 49967-464 by L'oreal Usa Products Inc, such as packaging, labeling, and the appearance of the drug itself. This resource could be helpful for medical professionals, pharmacists, and patients seeking to verify medication information and ensure they have the correct product.
image of a label - 125 mL primary label
This text is a list of details about KIEHL’S dermatologist solution for acne. It contains information about the active ingredient (salicylic acid 2%), uses (treatment of acne blemishes), warnings, directions for use, and a list of specific ingredients in the product. Additionally, there is a contact number for questions or comments related to the product.*
image of a label - 15 mL primary
Description: This is a product label for Kiehl's Dermatologist Solutions Acne Blemish Control. It is a treatment for acne blemishes that helps skin heal and prevent new acne blemishes. The product contains 2% salicylic acid. It advises users to be cautious of skin irritation and dryness, especially if using other topical acne medications simultaneously. The product should be kept out of reach of children and if irritation occurs, users should seek medical help. The directions recommend massaging the product onto the skin and rinsing well. The product is made in the USA and the company is based in New York.*
image of a label - 15 ml secondary
This text provides instructions and drug facts for a topical acne medication. It includes information on how to use the product, inactive ingredients, and directions for application. The text also contains a warning to keep the medication out of reach of children and what to do in case of ingestion. Contact information for assistance and the manufacturer's details are included as well.*
* The product label images have been analyzed using a combination of traditional computing and machine learning techniques. It should be noted that the descriptions provided may not be entirely accurate as they are experimental in nature. Use the information in this page at your own discretion and risk.
