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February 14, 2026Scientific ReportsOpen Access

Quantum-enhanced multimodal prognostic transformer for skin disease progression prediction and visualization

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Authors

CAC. V AravindaJRJoseph Emerson RajaSASultan Alasmari

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Overview

This proof-of-concept study demonstrates a novel AI system predicting skin disease progression using combined imaging and metadata, indicating its potential for improved clinical intervention.

Key Points

  • The aim is to develop an AI system that predicts skin disease progression based on integrated imaging and patient data.
  • Integrated dermoscopic images with patient metadata for analysis.
  • Utilized a Vision Transformer with a quantum layer for enhanced feature representation.
  • Employed long short-term memory modeling for disease evolution prediction.
  • Used a latent trajectory predictor and a quantum-inspired generative module for simulation.
  • Implemented attention rollouts and Integrated Gradients for explainability.
  • Achieved 89.4% accuracy in disease classification.
  • Reached 87.3% accuracy for disease stage prediction.
  • Outperformed conventional CNNs and Vision Transformer baselines in performance.
  • Demonstrated the potential of combining quantum computation with multimodal AI for dermatology.

Cite This Study

Aravinda et al. (2026) studied this question.

synapsesocial.com/papers/699010942ccff479cfe56e2ehttps://doi.org/10.1038/s41598-026-35951-2
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