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Related Experiment Video

Updated: Jul 4, 2026

Apparatus for Harvesting Tissue Microcolumns
06:06

Apparatus for Harvesting Tissue Microcolumns

Published on: October 25, 2018

Longitudinal Treatment-Aware Multimodal AI for Dermatology: A Scoping Review.

Ahmed Sheikh1, Mowafa Househ1

  • 1College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.

Studies in Health Technology and Informatics
|July 3, 2026
PubMed
Summary

Current AI in dermatology analyzes single images, lacking patient history. Future AI needs longitudinal data and treatment awareness for chronic skin conditions like eczema and psoriasis.

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Area of Science:

  • Dermatology
  • Artificial Intelligence
  • Medical Informatics

Background:

  • Dermatological AI has evolved from basic image classifiers to complex multimodal foundation models.
  • Existing AI systems achieve high diagnostic accuracy but lack longitudinal patient data and treatment history integration.
  • Chronic inflammatory skin diseases (e.g., eczema, acne, psoriasis, rosacea) require continuous monitoring and personalized treatment strategies.

Purpose of the Study:

  • To review the evidence for longitudinal, treatment-aware, and multimodal large language model (LLM) approaches in dermatology.
  • To identify current trends and research gaps in AI for chronic inflammatory skin conditions.
  • To explore the potential of advanced AI in providing comprehensive dermatological care.

Main Methods:

Keywords:
DermatologyLongitudinal AIMultimodal Large Language ModelsTemporal ReasoningTreatment-Aware AI

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Last Updated: Jul 4, 2026

Apparatus for Harvesting Tissue Microcolumns
06:06

Apparatus for Harvesting Tissue Microcolumns

Published on: October 25, 2018

  • Systematic literature review across five major scientific databases (PubMed, ACM, IEEE Xplore, Web of Science, Scopus).
  • Search conducted for studies published between 2020 and 2026.
  • Selection of 30 studies from 1,644 citations adhering to PRISMA-ScR guidelines.

    • Main Results:

    • Identified five key themes: shift to longitudinal AI, multimodal vision-language integration, treatment-aware decision support, temporal reasoning, and evaluation methods.
    • No current models integrate longitudinal imaging, medication logs, and LLM reasoning for chronic inflammatory skin diseases.
    • Significant research gaps exist in developing comprehensive AI solutions for dynamic dermatological conditions.

    Conclusions:

    • The field is moving towards longitudinal and multimodal AI, but integration with treatment data and LLM reasoning is nascent.
    • There is a critical need for longitudinal, treatment-annotated benchmarks to advance AI in chronic inflammatory dermatology.
    • Future research should focus on developing AI systems that incorporate temporal patient data and treatment responses for improved dermatological care.