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Comparative Evaluation of Artificial Intelligence-Generated Physician-Level Educational Content in Diabetic Foot Ulcers: Quality, Guideline Concordance, and Clinical Reliability

Emrah Işıktekin, Erhan Hocaoğlu • Uludağ Üniversitesi Tıp Fakültesi Dergisi (2026)

The study aimed to evaluate the quality, clinical reliability, and guideline concordance of physician-level educational responses generated by artificial intelligence (AI) in diabetic foot ulcer management. A blinded, comparative analysis w...

DOAJ Open Access (DOAJ) DOI: 10.32708/uutfd.1949030

Artificial Intelligence and Economic Growth

Philippe Aghion, Benjamin Jones, Charles I. Jones • National Bureau of Economic Research (2017)

This paper examines the potential impact of artificial intelligence (A.I.) on economic growth.We model A.I. as the latest form of automation, a broader process dating back more than 200 years.Electricity, internal combustion engines, and se...

OPENALEX Open Access DOI: https://doi.org/10.3386/w23928

Cost-effective, open-source, automated apparatus for testing transitive inference in mice

Silvia Margarian, Yihan Chen, Jumana Waheed, Parham Rezaeimanesh et al. • Scientific Reports (2026)

Abstract Transitive inference is a form of reasoning that relies on prior knowledge and benefits survival in diverse species. While initially designed for humans, the transitive inference task has been adapted for mice, allowing for integra...

DOAJ Open Access (DOAJ) DOI: 10.1038/s41598-026-43430-x

Is cardiovascular risk profiling from UK Biobank retinal images using explicit deep learning estimates of traditional risk factors equivalent to actual risk measurements? A prospective cohort study design

Kohji Nishida, Ryo Kawasaki, Yiming Qian, Liangzhi Li et al. • BMJ Open (2024)

Objective Despite extensive exploration of potential biomarkers of cardiovascular diseases (CVDs) derived from retinal images, it remains unclear how retinal images contribute to CVD risk profiling and how the results can inform lifestyle m...

DOAJ Open Access (DOAJ) DOI: 10.1136/bmjopen-2023-078609

Artificial intelligence in retina

Ursula Margarethe Schmidt-Erfurth, Amir Sadeghipour, Bianca S. Gerendas, Sebastian M. Waldstein et al. • Progress in Retinal and Eye Research (2018)

Major advances in diagnostic technologies are offering unprecedented insight into the condition of the retina and beyond ocular disease. Digital images providing millions of morphological datasets can fast and non-invasively be analyzed in ...

OPENALEX cc-by-nc-nd DOI: https://doi.org/10.1016/j.preteyeres.2018.07.004