Eduardo Farina

NEURORRADIOLOGISTA

Médico pela EMESCAM. Radiologista e Neurorradiologista pela UNIFESP. Neurorradiologista e Consultor de Inteligência Artificial no Hospital Israelita Albert Einstein. Membro do Radiology AI Data Standards Committee do RSNA e do Machine Learning Education Subcommittee da SIIM. Associate Editor da Radiology: Artificial Intelligence.

Publicações

Seleção de artigos e capítulos. Lista completa e citações no Google Acadêmico.

  • An overview of artificial intelligence in oncology

    E Farina, JJ Nabhen, MI Dacoregio, F Batalini, FY Moraes

    Future Science OA 8 (4), FSO787 · 2022

  • Performance of ChatGPT on the Brazilian radiology and diagnostic imaging and mammography board examinations

    LC Almeida, EMJM Farina, PEA Kuriki, N Abdala, FC Kitamura

    Radiology: Artificial Intelligence 6 (1), e230103 · 2023

  • The RSNA cervical spine fracture CT dataset

    HM Lin, E Colak, T Richards, FC Kitamura, LM Prevedello, J Talbott, et al.

    Radiology: Artificial Intelligence 5 (5), e230034 · 2023

  • The RSNA Abdominal Traumatic Injury CT (RATIC) Dataset

    JD Rudie, HM Lin, RL Ball, S Jalal, LM Prevedello, S Nicolaou, et al.

    Radiology: Artificial Intelligence 6 (6), e240101 · 2024

  • Social Media Platforms for Radiologists: Perks and Perils

    EM Júdice de Mattos Farina, N Abdala, FC Kitamura

    Radiology 307 (4), e220974 · 2023

  • Teaching AI for Radiology Applications: a Multisociety‑Recommended Syllabus from the AAPM, ACR, RSNA, and SIIM

    F Kitamura, T Kline, D Warren, L Moy, R Daneshjou, F Maleki, I Santos, et al.

    Medical Physics 52 (10), e17779 · 2025

  • Texts Are More than Notes, They Are Data: A Glimpse into How Machines Understand Text

    FC Kitamura, EMJM Farina, JP Mazuco Rodriguez, L Moy, LM Prevedello

    Radiology 316 (2), e243217 · 2025

  • The RSNA Lumbar Degenerative Imaging Spine Classification (LumbarDISC) Dataset

    TJ Richards, AE Flanders, E Colak, LM Prevedello, RL Ball, F Kitamura, et al.

    Radiology: Artificial Intelligence, e250480 · 2026

  • Smartphone Imaging and AI: A Commentary on Cardiac Device Classification

    EM Júdice de Mattos Farina, LA Celi

    Radiology: Artificial Intelligence 6 (5), e240418 · 2024

  • The Global Reading Room: Responding to a Social Media Post

    SL Ayesa, EMJ de Mattos Farina, RL Seidel, N Sharma

    American Journal of Roentgenology 222 (2), e2329846 · 2024

  • Pixel Tampering: Does Face Redaction Harm Medical AI Performance?

    EMJM Farina, FA Matsuoka, G Corradi, Y Yamagishi, M Abe, M Pfeiffer, et al.

    Journal of Imaging Informatics in Medicine · 2025

  • Evaluating the Clinical Impact of Generative Inpainting on Bone Age Estimation

    FA Matsuoka, EMJM Farina, AS Serpa, S Monteiro, R Ragazzini, et al.

    arXiv preprint arXiv:2511.23066 · 2025

  • A bibliometric network analysis of coronavirus during the first eight months of COVID-19 in 2020

    LB Furstenau, B Rabaioli, MK Sott, D Cossul, MS Bender, EMJDM Farina, et al.

    International Journal of Environmental Research and Public Health 18 (3), 952 · 2021

  • An interpretable machine learning model for COVID-19 screening

    GC Pinasco, EMJM Farina, FN Barcellos Filho, WF Fiorotti, MCM Ferreira, et al.

    Journal of Human Growth and Development 32 (2), 268-274 · 2022

Apresentações
IA na Neurorradiologia — Ameaça, Ferramenta ou Diferencial?
Como as diferentes camadas de inteligência artificial estão alterando a prática neurorradiológica — da visão computacional aos sistemas agênticos — e o que isso implica para o especialista.
15 slides05/03/2026Remoto
Textos São Mais Que Notas, São Dados — Como Máquinas Entendem Texto
Visão geral dos mecanismos internos dos modelos transformer e LLMs, com base no artigo de Kitamura et al. (Radiology 2025), para que radiologistas compreendam e confiem em sistemas de IA.
13 slides06/03/2026Remoto