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Life sciences · Journal article
Frontiers in Artificial Intelligence · September 10, 2026
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This is a software development and feasibility report describing a decoupled prototype for integrating clinical records and mitochondrial imaging data via a locally-stored, LLM-prompted interface. The work demonstrates technical proof-of-concept and usability but does not establish clinical utility, validity of LLM-assisted reasoning, or advantage over existing tools. It lays groundwork for future research but has no direct clinical application or evidence of benefit.
Feasibility study; software prototype development and usability assessment. Ten de-identified patient records with mitochondrial imaging data from human placental tissue obtained via transmission electron microscopy, immunofluorescence, and immunohistochemistry. No inclusion/exclusion criteria, demographic characteristics, or clinical stratification provided.. Intervention: Decoupled software prototype with HTML5/CSS3 client architecture, IndexedDB local storage, and interactive LLM prompt framework structured under Chain-of-Thought paradigm for data integration and interpretation.. n = 10. Not stated..
Platform achieved instantaneous, local read/write storage across independent patient tabs (mitochondria_001 to 010) without external server reliance Prompt framework guided LLMs in translating mitochondrial biomarkers (Mitofusin-2, nitrotyrosine) and clinical data into structured oxidative stress and metabolic risk summaries Authors declare lack of trained computer vision model for automatic segmentation as the main limitation
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This prototype has not been clinically validated and should not inform clinical decision-making. It serves as a technical proof-of-concept for integrating heterogeneous data locally and does not demonstrate superiority to or replacement of existing clinical tools.
A feasibility study of a software prototype with no clinical validation, efficacy testing, or comparison to standard care; demonstrates technical proof-of-concept only.
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This prototype has not been clinically validated and should not inform clinical decision-making. It serves as a technical proof-of-concept for integrating heterogeneous data locally and does not demonstrate superiority to or replacement of existing clinical tools.
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Introduction Obesity is a critical global health challenge due to its close association with cardiovascular and metabolic risk. At the molecular level, the lipotoxic environment alters the structure, dynamics, and connectivity of the mitochondrial network. Despite advances in cell biology and digital health, there is a lack of lightweight computational tools capable of integrating clinical records with molecular and photomicrographic data in a standardized way. This study presents the design, development, and feasibility assessment of a decoupled software prototype and an interactive prompt engineering framework for integrating clinical and histological mitochondrial data in molecular obesity research. Methodology A client-side software architecture was designed using HTML5, CSS3, and the IndexedDB API for the transactional and anonymized persistence of medical records (coded using the ICD-10 classification) and micrographs of human placental tissue (obtained using transmission electron microscopy, immunofluorescence, and immunohistochemistry for the evaluation of mitochondrial fusion molecular markers such as Mitofusin-2, nitrotyrosine). For interpretive processing, a framework of prompts structured under the Chain-of-Thought paradigm was built, designed to interact with Large-Scale Language Models (LLMs). The interface’s usability, local storage performance, and the consistency of AI-assisted reasoning were evaluated. Results The developed platform enabled the seamless and instantaneous management of independent patient tabs (unique storage keys mitochondria_001 to 010), achieving local read/write latency without reliance on external servers or transmission of sensitive personal data. The prompt framework proved effective in guiding LLMs in translating mitochondrial biomarkers and clinical data into structured summaries of oxidative stress status and metabolic risk. The lack of a trained computer vision model for automatic segmentation is declared as the main limitation of the current study. Conclusion The software prototype and prompt interface provide a low-cost, secure (data privacy-oriented), and clinically intuitive solution for translational data management in obesity. This work demonstrates the feasibility of integrating heterogeneous biomedical parameters using conversational AI tools and local storage, laying the groundwork for the future incorporation of image segmentation algorithms.
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