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Podemos realmente confiar em modelos generativos em saúde? Revisão sistemática da quantificação da incerteza na IA generativa para imagens médicas

Medicina e Tecnologia
Imagem de arquivo, ilustrativa — não é do estudo. Pastilha de silício de um processador AMD Ryzen ao microscópio. Fritzchens Fritz · CC0 · Wikimedia Commons

Revisão sistemáticaNível 1 · Evidência consolidadaComputer methods and programs in biomedicine21 jul 20261 citações

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Resumo original em inglês

O que aconteceu

Generative AI is transforming medical imaging research through synthesis, enhancement, and reconstruction of clinical images. While these advances show promise in addressing data scarcity and supporting diagnostics, clinical adoption remains limited due to challenges in assessing the trustworthiness of generated content. This study aims to systematically evaluate the integration of uncertainty quantification (UQ) methods within generative models for medical imaging to enhance result reliability.

A systematic review following PRISMA guidelines was conducted, analyzing studies from January 2018 to December 2025 that combined generative models with UQ techniques in medical imaging. The search strategy covered major medical and computer science databases, with studies evaluated against predefined inclusion criteria focusing on implementation methodology and performance metrics.

O que encontraram

From the analysis of 41 eligible studies, 29 focused on radiology, 8 on microscopy, and 4 on optical coherence tomography. Across this heterogeneous body of evidence, integrating UQ was frequently associated with improved performance or with more informative reliability assessment, including reported gains in reconstruction quality, segmentation accuracy, and anomaly detection. Notably, 56% of studies (n=23) were published in 2025, indicating rapid field growth.

Por que isso importa

UQ integration represents a crucial advancement toward trustworthy generative AI systems in medical imaging. Key priorities identified include standardizing uncertainty metrics, developing computationally efficient frameworks, and embedding uncertainty awareness within generation processes. These findings suggest that UQ methods can enhance the clinical reliability of generative AI applications in medical imaging.

Resumo publicado sob licença CC BY. A tradução para o português ainda não chegou a este estudo.

O estudo original

Can we really trust generative models in healthcare? A systematic review of uncertainty quantification in generative AI for medical imaging

Alen Shahini, Silvia Seoni, Alessandra Manzin, Martina Oria, Anjan Gudigar, U Raghavendra, Abdulkadir Sengur, Rajendra Acharya, Massimo Salvi

Descritores MeSH

  • Delivery of Health Care
  • Diagnostic Imaging
  • Artificial Intelligence
  • Reproducibility of Results
  • Generative Artificial Intelligence
  • Uncertainty
  • Humans
  • Image Processing, Computer-Assisted

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