Why educating for clinical machine learning still requires attention to history: a rejoinder to Gauld <i>et al</i>

Starke, Georg; Clercq, Eva De; Borgwardt, Stefan; Elger, Bernice Simone

doi:10.1017/s0033291720004766

Cited by 1 publication

(1 citation statement)

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“…ML automatically improves and optimizes computer algorithms and programs by analyzing the data rather than relying on explicit program instructions (Nichols et al, 2019;Cho et al, 2021). Among the various ML models that have emerged, neural networks simulate the synaptic structure of human neurons and improve the computational ability of ML by adjusting the parameters of network models (Starke et al, 2021). Convolutional neural networks (CNNs), which have an encoding structure similar to that of visual cortical neurons (Hou et al, 2019), have become one of the most popular neural network models (Mintz and Brodie, 2019).…”

Section: What Is Artificial Intelligence?mentioning

confidence: 99%

Orbital and eyelid diseases: The next breakthrough in artificial intelligence?

Bao

Sun

Zhan

et al. 2022

Front. Cell Dev. Biol.

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Orbital and eyelid disorders affect normal visual functions and facial appearance, and precise oculoplastic and reconstructive surgeries are crucial. Artificial intelligence (AI) network models exhibit a remarkable ability to analyze large sets of medical images to locate lesions. Currently, AI-based technology can automatically diagnose and grade orbital and eyelid diseases, such as thyroid-associated ophthalmopathy (TAO), as well as measure eyelid morphological parameters based on external ocular photographs to assist surgical strategies. The various types of imaging data for orbital and eyelid diseases provide a large amount of training data for network models, which might be the next breakthrough in AI-related research. This paper retrospectively summarizes different imaging data aspects addressed in AI-related research on orbital and eyelid diseases, and discusses the advantages and limitations of this research field.

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