M.S. Applied Data Science - Capstone Chronicles 2025

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Both studies point to shortcomings in current recall classification systems, with Y. Zhou focusing on external influences and Dubin et al. (2021) emphasizing the inherent risks tied to different approval pathways. However, neither study offers a data-driven, objective method for recall classification, leaving a gap in addressing these issues in a more systematic and unbiased way. 3.2 Characteristics and Trends in Medical Device Recalls Mooghali et al. (2023) provided a detailed analysis of Class I medical device recalls from 2018 to 2022, revealing that such recalls are frequent and affect millions of devices annually. Their findings highlight inefficiencies in the recall process, noting that Class I recalls lasted a median of 24 months from initiation to termination. The research identifies trends in recall frequency, the types of devices most affected, and the length of time recalls take to resolve. However, it does not address the potential for predictive models that could identify high-risk recalls before they occur, indicating a lack of proactive tools for managing recall risks in advance. 3.3 Impact of Recalls on Firm Innovation The study by Ni et al. (2023) emphasized the complex interplay between product failures and organizational learning. It identifies an inverted U-shaped relationship, where moderate levels of recalls may encourage innovation, but excessive recalls can have a negative effect on innovation and firm performance. The research highlights the complex interactions between product failures and organizational learning, suggesting that recalls can both drive

and hinder innovation depending on their frequency. However, the study is limited to the automotive sector and does not explore whether these findings can be generalized to other industries, leaving a gap in understanding the broader implications of recalls on innovation. 3.4 Recall Prediction J. An (2024) applies structural topic modeling to analyze FDA recalls in the plant-based food industry, revealing two dominant themes: market actors’ opportunism and food culture practices. This approach demonstrates the utility of advanced text analysis techniques for uncovering recall-related trends. However, the study’s focus on a single industry limits the generalizability of its findings across broader product categories. 3.5 Regulatory Approaches and Public Health Impact The study by Barbosa-Slivinskis et al. (2024) developed a machine learning algorithm to predict FDA medical device recalls, achieving high sensitivity and specificity with lead times of up to 12 months. This research highlights the potential of machine learning for proactive recall management, enabling earlier detection of risks and better resource allocation. However, the study focuses exclusively on medical devices and does not explore the broader application of these techniques across different industries. Several gaps are identified, including the need for a comprehensive, multi-industry approach to recall prediction, the use of machine learning and NLP techniques across product categories, and the development of standardized methods for assessing recall severity. Additionally, there is limited exploration of supply chain data integration and the long-term impacts of recalls on public health and industry innovation.

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