ArticleJournal of multidisciplinary healthcare2025
Feasibility and Effectiveness of a Low-Code AI Platform for Developing a Neonatal Multimodal Pain Classification Model.
Article in Journal of multidisciplinary healthcare, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Artificial Intelligence (AI) Implementation in Maternal and Child Health: A Scoping Review.Journal of multidisciplinary healthcare · 2026Review
- Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Artificial intelligence (AI) has advanced neonatal pain recognition, yet a significant gap persists in translating complex algorithms into practical clinical applications. Low-code AI development platforms, which simplify and automate model creation, offer a potential solution to bridge this gap between research and bedside practice. Objective: This study aimed to explore the feasibility of constructing and validating a neonatal multimodal pain classification model using a commercial low-code AI development platform (EasyDL). The objective was to develop an accessible, cost-effective, and efficient method that empowers clinical professionals to create their own AI tools without extensive programming expertise. Methods: We uploaded 426 neonatal acute pain multimodal data segments to the EasyDL platform and trained a video classification model using its AutoML capabilities. The model underwent internal testing on a held-out dataset portion, followed by external validation on an independent prospective cohort. For external validation, we compared model performance against the N-PASS (Neonatal Pain, Agitation, and Sedation Scale) scores assessed by a senior nurse as the clinical gold standard. Results: The neonatal multimodal pain classification model developed on the platform showed strong performance. Internal validation achieved 89.6% accuracy and an 85.8% F1 score. External validation on unseen data reached 87.7% accuracy, with AUC exceeding 0.95 across all pain categories (no pain, mild pain, severe pain). The streamlined development process enabled seamless API deployment to an Android mobile device for clinical use. Conclusion: Developing a neonatal multimodal pain classification model using a low-code AI platform proves both feasible and effective. The model demonstrates robust performance and strong clinical integration potential. This approach offers a practical pathway to democratize AI development, enabling healthcare professionals to create digital solutions for neonatal pain management.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.