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20 pages, 6802 KB

Open AccessArticle

Deep Learning for Predicting Late-Onset Breast Cancer Metastasis: The Single-Hyperparameter Grid Search (SHGS) Strategy for Meta-Tuning a Deep Feed-Forward Neural Network

by Yijun Zhou, Om Arora-Jain and Xia Jiang

Bioengineering 2025, 12(11), 1214; https://doi.org/10.3390/bioengineering12111214 - 7 Nov 2025

Cited by 1 | Viewed by 1005

Abstract

Background: While machine learning has advanced in medicine, its widespread use in clinical applications, especially in predicting breast cancer metastasis, is still limited. We have been dedicated to constructing a deep feed-forward neural network (DFNN) model to predict breast cancer metastasis n [...] Read more.

Background: While machine learning has advanced in medicine, its widespread use in clinical applications, especially in predicting breast cancer metastasis, is still limited. We have been dedicated to constructing a deep feed-forward neural network (DFNN) model to predict breast cancer metastasis n years in advance. However, the challenge lies in efficiently identifying optimal hyperparameter values through grid search, given the constraints of time and resources. Issues such as the infinite possibilities for continuous hyperparameters like L1 and L2, as well as the time-consuming and costly process, further complicate the task. Methods: To address these challenges, we developed the Single-Hyperparameter Grid Search (SHGS) strategy, serving as a preselection method before grid search. Our experiments with SHGS applied to DFNN models for breast cancer metastasis prediction focused on analyzing eight target hyperparameters (epochs, batch size, dropout, L1, L2, learning rate, decay, and momentum). Results: We created three figures, each depicting the experimental results obtained from three LSM-I-10+-year datasets. These figures illustrate the relationship between model performance and the target hyperparameter values. Our experiments achieved maximum test AUC scores of 0.770, 0.762, and 0.886 for the 10-year, 12-year, and 15-year datasets, respectively. For each hyperparameter, we analyzed whether changes in this hyperparameter would affect model performance, examined whether there were specific patterns, and explored how to choose values for the hyperparameter. Conclusions: Our experimental findings reveal that the optimal value of a hyperparameter is not only dependent on the dataset but is also significantly influenced by the settings of other hyperparameters. Additionally, our experiments suggest a reduced range of values for a target hyperparameter, which may be helpful for “low-budget” grid search. This approach serves as a foundation for the subsequent use of grid search to enhance model performance. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Complex Diseases)

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14 pages, 2192 KB

Open AccessCommunication

PARKA AI: A Sensor-Integrated Mobile Application for Parkinson’s Disease Monitoring and Self-Management

by Krisha Sanjay Bhalala and Hamid Mansoor

Bioengineering 2025, 12(10), 1059; https://doi.org/10.3390/bioengineering12101059 - 30 Sep 2025

Cited by 3 | Viewed by 2413

Abstract

Parkinson’s disease (PD), a progressive neurodegenerative disorder affecting over 10 million people worldwide, necessitates continuous symptom monitoring to optimize treatment and enhance quality of life. Effective communication between patients and healthcare providers (HCPs) is vital but often hindered by fragmented data and cognitive [...] Read more.

Parkinson’s disease (PD), a progressive neurodegenerative disorder affecting over 10 million people worldwide, necessitates continuous symptom monitoring to optimize treatment and enhance quality of life. Effective communication between patients and healthcare providers (HCPs) is vital but often hindered by fragmented data and cognitive impairments. PARKA AI, a novel iOS application, leverages Apple Watch HealthKit data (e.g., tremor detection, mobility metrics, heart rate, and sleep patterns) and integrates it with self-reported logs (e.g., mood, medication adherence) to empower PD self-management and improve patient–HCP interactions. Employing a human-centered design approach, we developed a high-fidelity prototype using a large language model (LLM)— Google Gemini 1.5 Flash—to process and analyze self-reports and objective sensor-derived data from Apple Healthkit to generate patient-friendly summaries and concise HCP reports. PARKA AI provides accessible data visualizations, personalized self-management tools, and streamlined HCP reports to foster engagement and communication. This paper outlines the derived design requirements, prototype features, and illustrative use cases to show how LLMs can be used in digital health tools. Future work will focus on real-world usability testing to validate the application’s efficacy and accessibility. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Complex Diseases)

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18 pages, 1143 KB

Open AccessArticle

Enhancing Clinical Decision Support with Adaptive Iterative Self-Query Retrieval for Retrieval-Augmented Large Language Models

by Srinivasagam Prabha, Cesar A. Gomez-Cabello, Syed Ali Haider, Ariana Genovese, Maissa Trabilsy, Nadia G. Wood, Sanjay Bagaria, Cui Tao and Antonio J. Forte

Bioengineering 2025, 12(8), 895; https://doi.org/10.3390/bioengineering12080895 - 21 Aug 2025

Cited by 8 | Viewed by 3081

Abstract

Retrieval-Augmented Generation (RAG) offers a promising strategy to harness large language models (LLMs) for delivering up-to-date, accurate clinical guidance while reducing physicians’ cognitive burden, yet its effectiveness hinges on query clarity and structure. We propose an adaptive Self-Query Retrieval (SQR) framework that integrates [...] Read more.

Retrieval-Augmented Generation (RAG) offers a promising strategy to harness large language models (LLMs) for delivering up-to-date, accurate clinical guidance while reducing physicians’ cognitive burden, yet its effectiveness hinges on query clarity and structure. We propose an adaptive Self-Query Retrieval (SQR) framework that integrates three refinement modules—PICOT (Population, Intervention, Comparison, Outcome, Time), SPICE (Setting, Population, Intervention, Comparison, Evaluation), and Iterative Query Refinement (IQR)—to automatically restructure and iteratively enhance clinical questions until they meet predefined retrieval-quality thresholds. Implemented on Gemini-1.0 Pro, we benchmarked SQR using thirty postoperative rhinoplasty queries, evaluating responses for accuracy and relevance on a three-point Likert scale and for retrieval quality via precision, recall, and F1 score; statistical significance was assessed by one-way ANOVA with Tukey post-hoc testing. The full SQR pipeline achieved 87% accuracy (Likert 2.4 ± 0.7) and 100% relevance (Likert 3.0 ± 0.0), significantly outperforming a non-refined RAG baseline (50% accuracy, 80% relevance; p < 0.01 and p = 0.03). Precision, recall, and F1 rose from 0.17, 0.39 and 0.24 to 0.53, 1.00, and 0.70, respectively, while PICOT-only and SPICE-only variants yielded intermediate improvements. These findings demonstrate that automated structuring and iterative enhancement of queries via SQR substantially elevate LLM-based clinical decision support, and its model-agnostic architecture enables rapid adaptation across specialties, data sources, and LLM platforms. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Complex Diseases)

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15 pages, 1561 KB

Open AccessArticle

Using Large Language Models to Retrieve Critical Data from Clinical Processes and Business Rules

by Yunguo Yu, Cesar A. Gomez-Cabello, Svetlana Makarova, Yogesh Parte, Sahar Borna, Syed Ali Haider, Ariana Genovese, Srinivasagam Prabha and Antonio J. Forte

Bioengineering 2025, 12(1), 17; https://doi.org/10.3390/bioengineering12010017 - 28 Dec 2024

Cited by 11 | Viewed by 6138

Abstract

Current clinical care relies heavily on complex, rule-based systems for tasks like diagnosis and treatment. However, these systems can be cumbersome and require constant updates. This study explores the potential of the large language model (LLM), LLaMA 2, to address these limitations. We [...] Read more.

Current clinical care relies heavily on complex, rule-based systems for tasks like diagnosis and treatment. However, these systems can be cumbersome and require constant updates. This study explores the potential of the large language model (LLM), LLaMA 2, to address these limitations. We tested LLaMA 2′s performance in interpreting complex clinical process models, such as Mayo Clinic Care Pathway Models (CPMs), and providing accurate clinical recommendations. LLM was trained on encoded pathways versions using DOT language, embedding them with SentenceTransformer, and then presented with hypothetical patient cases. We compared the token-level accuracy between LLM output and the ground truth by measuring both node and edge accuracy. LLaMA 2 accurately retrieved the diagnosis, suggested further evaluation, and delivered appropriate management steps, all based on the pathways. The average node accuracy across the different pathways was 0.91 (SD ± 0.045), while the average edge accuracy was 0.92 (SD ± 0.122). This study highlights the potential of LLMs for healthcare information retrieval, especially when relevant data are provided. Future research should focus on improving these models’ interpretability and their integration into existing clinical workflows. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Complex Diseases)

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Review

Jump to: Research

24 pages, 1049 KB

Open AccessReview

Applications of 3D Printing and Artificial Intelligence in Healthcare Management: A Narrative Review

by Conrado Domínguez Trujillo, Donato Monopoli Forleo, Carmen Delia Dávila Quintana and Juan Mora Delgado

Bioengineering 2026, 13(2), 196; https://doi.org/10.3390/bioengineering13020196 - 9 Feb 2026

Cited by 3 | Viewed by 2378

Abstract

The integration of 3D printing and artificial intelligence is transforming healthcare management by driving innovations in personalized care, supply chain operations, and clinical workflows. This review offers a comprehensive overview and in-depth analysis of recent (2018–2025) applications where AI technologies enhance 3D printing [...] Read more.

The integration of 3D printing and artificial intelligence is transforming healthcare management by driving innovations in personalized care, supply chain operations, and clinical workflows. This review offers a comprehensive overview and in-depth analysis of recent (2018–2025) applications where AI technologies enhance 3D printing within healthcare. We explore how AI-powered design and optimization facilitate the creation of patient-specific medical devices, implants, and even bioprinted tissues, while intelligent process control increases both quality and efficiency. Additionally, we examine regulatory and ethical considerations, including the evolution of frameworks for AI-enabled devices, as well as challenges in data governance, validation, and equitable access. The review takes a global perspective, presenting real-world case studies that showcase both successful implementations and ongoing challenges. We also discuss various perspectives and controversies, such as the balance between innovation and safety in autonomous AI design, and highlight areas where further research is needed. In contrast to previous narrative reviews that focus solely on clinical applications or technical aspects, this review uniquely evaluates the combined impact of AI and 3D printing on healthcare management—including cost-effectiveness, governance, decision-making processes, and point-of-care manufacturing. This work is particularly valuable for hospital administrators, clinical operations leaders, health policymakers, and biomedical innovation teams seeking to understand the broader implications of AI-enhanced 3D printing in healthcare management. Nevertheless, despite promising advancements, the field is constrained by heterogeneous evidence, a lack of standardized evaluation metrics, and insufficient long-term outcome data, which together limit the ability to fully assess the sustained impact of AI-integrated 3D printing in healthcare environments. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Complex Diseases)

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27 pages, 1633 KB

Open AccessReview

Transformer Models, Graph Networks, and Generative AI in Gut Microbiome Research: A Narrative Review

by Yan Zhu, Yiteng Tang, Xin Qi and Xiong Zhu

Bioengineering 2026, 13(2), 144; https://doi.org/10.3390/bioengineering13020144 - 27 Jan 2026

Cited by 7 | Viewed by 3440

Abstract

Background: The rapid advancement in artificial intelligence (AI) has fundamentally reshaped gut microbiome research by enabling high-resolution analysis of complex, high-dimensional microbial communities and their functional interactions with the human host. Objective: This narrative review aims to synthesize recent methodological advances in AI-driven [...] Read more.

Background: The rapid advancement in artificial intelligence (AI) has fundamentally reshaped gut microbiome research by enabling high-resolution analysis of complex, high-dimensional microbial communities and their functional interactions with the human host. Objective: This narrative review aims to synthesize recent methodological advances in AI-driven gut microbiome research and to evaluate their translational relevance for therapeutic optimization, personalized nutrition, and precision medicine. Methods: A narrative literature review was conducted using PubMed, Google Scholar, Web of Science, and IEEE Xplore, focusing on peer-reviewed studies published between approximately 2015 and early 2025. Representative articles were selected based on relevance to AI methodologies applied to gut microbiome analysis, including machine learning, deep learning, transformer-based models, graph neural networks, generative AI, and multi-omics integration frameworks. Additional seminal studies were identified through manual screening of reference lists. Results: The reviewed literature demonstrates that AI enables robust identification of diagnostic microbial signatures, prediction of individual responses to microbiome-targeted therapies, and design of personalized nutritional and pharmacological interventions using in silico simulations and digital twin models. AI-driven multi-omics integration—encompassing metagenomics, metatranscriptomics, metabolomics, proteomics, and clinical data—has improved functional interpretation of host–microbiome interactions and enhanced predictive performance across diverse disease contexts. For example, AI-guided personalized nutrition models have achieved AUC exceeding 0.8 for predicting postprandial glycemic responses, while community-scale metabolic modeling frameworks have accurately forecast individualized short-chain fatty acid production. Conclusions: Despite substantial progress, key challenges remain, including data heterogeneity, limited model interpretability, population bias, and barriers to clinical deployment. Future research should prioritize standardized data pipelines, explainable and privacy-preserving AI frameworks, and broader population representation. Collectively, these advances position AI as a cornerstone technology for translating gut microbiome data into actionable insights for diagnostics, therapeutics, and precision nutrition. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Complex Diseases)

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20 pages, 1515 KB

Open AccessReview

Integration of Artificial Intelligence and Wearable Devices in Pediatric Clinical Care: A Review

by Huili Zheng, Pragya Sharma, Matthew Johnson, Matteo Danieletto, Eugenia Alleva, Alexander W. Charney, Girish N. Nadkarni, Chethan Sarabu, Bjoern M. Eskofier, Yuri Ahuja, Florian Richter, Eyal Klang, Sandeep Gangadharan, Felix Richter, Emma Holmes and Benjamin S. Glicksberg

Bioengineering 2025, 12(12), 1320; https://doi.org/10.3390/bioengineering12121320 - 3 Dec 2025

Cited by 7 | Viewed by 2879

Abstract

Wearable devices are becoming widely applied in healthcare to enable continuous, noninvasive monitoring, but their use in pediatric populations remains relatively underexplored. This review synthesizes 36 clinical studies focused on pediatric hospital and outpatient wearables published between 2014 and 2025. Devices included wrist-worn [...] Read more.

Wearable devices are becoming widely applied in healthcare to enable continuous, noninvasive monitoring, but their use in pediatric populations remains relatively underexplored. This review synthesizes 36 clinical studies focused on pediatric hospital and outpatient wearables published between 2014 and 2025. Devices included wrist-worn trackers, adhesive biosensors, and more, capturing electrocardiography, photoplethysmography, accelerometry, and other signals. Clinical applications spanned a variety of care settings. Artificial intelligence (AI) partially enhanced interpretation for the early detection of conditions such as postoperative complications and sepsis. Despite their promising accuracy, most studies remain small, single-center pilots focused on feasibility and signal validity rather than outcomes such as mortality, readmission, or long-term recovery. Key barriers include pediatric-specific device design, motion-robust signal quality, regulatory clearance, workflow integration, and equitable adoption in low-resource settings. Ethical concerns such as privacy, consent, and incidental findings and regulatory constraints, particularly the lack of pediatric labeling and approval for consumer and AI-driven devices, further limit translation into practice. Future work should prioritize multi-center studies, multimodal analytics, explainable AI, and seamless integration into clinical pathways. With these advances, wearables can move beyond feasibility to become reliable, personalized tools that improve pediatric monitoring and care. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Complex Diseases)

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26 pages, 1904 KB

Open AccessReview

Large Language Models in Genomics—A Perspective on Personalized Medicine

by Shahid Ali, Yazdan Ahmad Qadri, Khurshid Ahmad, Zhizhe Lin, Man-Fai Leung, Sung Won Kim, Athanasios V. Vasilakos and Teng Zhou

Bioengineering 2025, 12(5), 440; https://doi.org/10.3390/bioengineering12050440 - 23 Apr 2025

Cited by 16 | Viewed by 8476

Abstract

Integrating artificial intelligence (AI), particularly large language models (LLMs), into the healthcare industry is revolutionizing the field of medicine. LLMs possess the capability to analyze the scientific literature and genomic data by comprehending and producing human-like text. This enhances the accuracy, precision, and [...] Read more.

Integrating artificial intelligence (AI), particularly large language models (LLMs), into the healthcare industry is revolutionizing the field of medicine. LLMs possess the capability to analyze the scientific literature and genomic data by comprehending and producing human-like text. This enhances the accuracy, precision, and efficiency of extensive genomic analyses through contextualization. LLMs have made significant advancements in their ability to understand complex genetic terminology and accurately predict medical outcomes. These capabilities allow for a more thorough understanding of genetic influences on health issues and the creation of more effective therapies. This review emphasizes LLMs’ significant impact on healthcare, evaluates their triumphs and limitations in genomic data processing, and makes recommendations for addressing these limitations in order to enhance the healthcare system. It explores the latest advancements in LLMs for genomic analysis, focusing on enhancing disease diagnosis and treatment accuracy by taking into account an individual’s genetic composition. It also anticipates a future in which AI-driven genomic analysis is commonplace in clinical practice, suggesting potential research areas. To effectively leverage LLMs’ potential in personalized medicine, it is vital to actively support innovation across multiple sectors, ensuring that AI developments directly contribute to healthcare solutions tailored to individual patients. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Complex Diseases)

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