Using generative AI to investigate medical imagery models and datasets

Lang, Oran; Yaya-Stupp, Doron; Traynis, Ilana; Cole-Lewis, Heather; Bennett, Chloe R.; Lyles, Courtney R.; Lau, Charles; Irani, Michal; Semturs, Christopher; Webster, Dale R.; Corrado, Greg S.; Hassidim, Avinatan; Matias, Yossi; Liu, Yun; Hammel, Naama; Babenko, Boris

doi:10.1016/j.ebiom.2024.105075

Cited by 11 publications

(1 citation statement)

References 75 publications

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“…Our findings align with previous studies showing the effectiveness of generative models in medical data augmentation [ 89 , 90 ]. Particularly, the use of ctGANs has shown to be feasible in several applications, such as electroencephalography [ 91 , 92 ].…”

Section: Discussionsupporting

confidence: 92%

Optimizing Rare Disease Gait Classification through Data Balancing and Generative AI: Insights from Hereditary Cerebellar Ataxia

Trabassi,

Castiglia,

Bini

et al. 2024

Sensors

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The interpretability of gait analysis studies in people with rare diseases, such as those with primary hereditary cerebellar ataxia (pwCA), is frequently limited by the small sample sizes and unbalanced datasets. The purpose of this study was to assess the effectiveness of data balancing and generative artificial intelligence (AI) algorithms in generating synthetic data reflecting the actual gait abnormalities of pwCA. Gait data of 30 pwCA (age: 51.6 ± 12.2 years; 13 females, 17 males) and 100 healthy subjects (age: 57.1 ± 10.4; 60 females, 40 males) were collected at the lumbar level with an inertial measurement unit. Subsampling, oversampling, synthetic minority oversampling, generative adversarial networks, and conditional tabular generative adversarial networks (ctGAN) were applied to generate datasets to be input to a random forest classifier. Consistency and explainability metrics were also calculated to assess the coherence of the generated dataset with known gait abnormalities of pwCA. ctGAN significantly improved the classification performance compared with the original dataset and traditional data augmentation methods. ctGAN are effective methods for balancing tabular datasets from populations with rare diseases, owing to their ability to improve diagnostic models with consistent explainability.

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Section: Discussionsupporting

confidence: 92%

Optimizing Rare Disease Gait Classification through Data Balancing and Generative AI: Insights from Hereditary Cerebellar Ataxia

Trabassi,

Castiglia,

Bini

et al. 2024

Sensors

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Filter Bubbles and the Unfeeling: How AI for Social Media Can Foster Extremism and Polarization

Rodilosso

2024

Philos. Technol.

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Social media have undoubtedly changed our ways of living. Their presence concerns an increasing number of users (over 4,74 billion) and pervasively expands in the most diverse areas of human life. Marketing, education, news, data, and sociality are just a few of the many areas in which social media play now a central role. Recently, some attention toward the link between social media and political participation has emerged. Works in the field of artificial intelligence have already pointed out that there is a close link between the use of machine learning algorithms in social media and possible epistemic isolation, which could lead to political radicalization. The idea supporting this paper is that artificial intelligence for social media can actively put users’ deliberative capacity at risk and foster political extremism. To prove these claims, I proceed along two lines of inquiry. First, I focus on filter bubbles, namely the result of selections made by algorithms that recommend contents that meet users’ expectations and opinions. To analyze this phenomenon, I refer to the Deweyan model of experience. Second, I connect the filter bubbles problem to the Deweyan idea of deliberative and participatory democracy and Nussbaum’s concept of political compassion. The purpose of this paper is to provide a philosophical foundation that can both (1) effectively serve as a method for analyzing machine learning algorithms and their potential problems in relation to political extremism, and (2) be adopted as a standard to counter the danger of extremism associated with social media experience.

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A Systematic Literature Review of Empirical Research on Applying Generative Artificial Intelligence in Education

Zhang,

Shen

et al. 2024

Front. Digit. Educ.

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Using generative AI to investigate medical imagery models and datasets

Cited by 11 publications

References 75 publications

Optimizing Rare Disease Gait Classification through Data Balancing and Generative AI: Insights from Hereditary Cerebellar Ataxia

Optimizing Rare Disease Gait Classification through Data Balancing and Generative AI: Insights from Hereditary Cerebellar Ataxia

Filter Bubbles and the Unfeeling: How AI for Social Media Can Foster Extremism and Polarization

A Systematic Literature Review of Empirical Research on Applying Generative Artificial Intelligence in Education

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