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Bioengineering
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Advances in Intelligent Health Management and Rehabilitation Technology: Integrating Large...
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Advances in Intelligent Health Management and Rehabilitation Technology: Integrating Large Language Models and AI Solutions

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: 31 May 2026 | Viewed by 4212

Special Issue Editor

Dr. Ning Deng

E-Mail Website
Guest Editor
College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, China
Interests: chronic disease management; artificial intelligent in medicine
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The emergence of intelligent health management and rehabilitation technologies represents a dynamic integration of artificial intelligence (AI), machine learning (ML), and contemporary healthcare practices. This interdisciplinary field aims to enhance traditional rehabilitation, enable precise health monitoring, and promote proactive management of chronic diseases. With the evolution of large language models (LLMs), such as GPT and similar foundational models, the potential for intelligent, personalized, and explainable health solutions is significantly expanded.

Building upon the success of our previous Special Issue, "Intelligent Health Management, Nursing and Rehabilitation Technology", which concluded on 30 April 2025, and garnered significant attention with over 10,700 views globally, we are pleased to announce the launch of Volume II. The initial Special Issue attracted high-quality submissions from researchers worldwide, reflecting the growing interest and advancements in the integration of AI and ML in healthcare and rehabilitation.

In continuation, we aim to delve deeper into the transformative potential of medical LLMs in intelligent health management and rehabilitation. The fusion of LLMs with existing AI and ML technologies promises to revolutionize patient care by enabling real-time, personal decision-making.

The scope of this Special Issue includes, but is not limited to, the following themes:

  • Proactive health systems: designing and optimizing systems for real-time health monitoring and proactive management at individual and population levels.
  • Predictive models: developing predictive models to identify early health risks, inform treatment pathways, and improve chronic disease management.
  • Medical artificial intelligence: leveraging AI and ML for clinical decision support, diagnostic assistance, and optimization of treatment protocols.
  • Smart healthcare: implementing smart systems that integrate AI and data analytics to enhance rehabilitation outcomes and improve quality of life.
  • Personalized rehabilitation strategies: utilizing machine learning and data-driven approaches to develop individualized rehabilitation plans responsive to patient progress.
  • Remote monitoring and intervention: exploring technologies that support remote patient engagement, real-time health monitoring, and timely interventions.
  • Clinical knowledge representation and decision support: employing LLMs for summarizing clinical information, supporting multidisciplinary decision-making, and generating adaptive care plans.
  • Intelligent agents in patient interaction: utilizing LLMs to develop intelligent agents that provide empathetic, interactive support to patients throughout rehabilitation and chronic disease management.
  • Intelligent documentation and workflow automation: applying LLMs to automate medical documentation, improve care coordination, and reduce provider workload.

By integrating LLMs with existing intelligent health systems, this Special Issue aims to uncover the potential of explainable, adaptive, and interactive healthcare solutions. These technologies can empower patients and clinicians alike with real-time insights, efficient workflows, and personalized care.

We invite original research articles, review papers, and visionary perspectives that contribute to the advancement of intelligent rehabilitation and health management, with a special focus on the transformative role of large language models.

Dr. Ning Deng
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Bioengineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • intelligent rehabilitation technology
  • health monitoring systems
  • machine learning in healthcare
  • intelligent nursing technology
  • proactive health management
  • large language models in healthcare
  • AI-assisted clinical decision-making
  • personalized rehabilitation strategies

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Related Special Issue

Published Papers (3 papers)

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Research

12 pages, 1051 KB  
Article
Assessing the Efficacy of Ortho GPT: A Comparative Study with Medical Students and General LLMs on Orthopedic Examination Questions
by Philippe Fabian Pohlmann, Maximilian Glienke, Richard Sandkamp, Christian Gratzke, Hagen Schmal, Dominik Stephan Schoeb and Andreas Fuchs
Bioengineering 2025, 12(12), 1290; https://doi.org/10.3390/bioengineering12121290 - 24 Nov 2025
Cited by 1 | Viewed by 666
Abstract
Background: Domain-specific large language models (LLMs) like Ortho GPT have potential advantages over general-purpose models in medical education, offering improved factual accuracy and contextual relevance. This study evaluates the performance of Ortho GPT against general LLMs and senior medical students on validated orthopedic [...] Read more.
Background: Domain-specific large language models (LLMs) like Ortho GPT have potential advantages over general-purpose models in medical education, offering improved factual accuracy and contextual relevance. This study evaluates the performance of Ortho GPT against general LLMs and senior medical students on validated orthopedic examination questions. Methods: Six LLMs (Ortho GPT 4o, ChatGPT 4o, ChatGPT 3.5, Perplexity AI, DeepSeek-R1, and Llama 3.3-70B) were tested using multiple-choice items from final-year medical student orthopedic exams in German language. Each model answered identical questions under standardized zero-shot conditions; accuracy rates and item-level results were compared using McNemar’s test, Jaccard similarity, and point-biserial correlation with student difficulty ratings. Results: Ortho GPT achieved the highest accuracy across models. McNemar’s tests revealed the significant superiority of Ortho GPT over DeepSeek (p = 2.33 × 10−35), Llama 3.3-70B (p = 1.11 × 10−32), and Perplexity (p = 4.01 × 10−5). Differences between Ortho GPT and ChatGPT 4o were non-significant (p = 0.065), suggesting near-equivalent performance to the strongest general model. No LLM showed correlation with student item difficulty (|r| < 0.07, p > 0.05), indicating that models solved items independently of human-perceived difficulty. Jaccard indices suggested moderate overlap between Ortho GPT and ChatGPT 4o, but distinct response profiles compared with general LLMs. Conclusions: These findings illustrate the superiority of Ortho GPT in orthopedic exam accuracy and context relevance, attributed to its specialized training data. The domain-specific approach enables performance matching or exceeding top general LLMs in orthopedics, emphasizing the importance of domain specialization for reliable, curriculum-aligned support in medical education. Full article
(This article belongs to the Special Issue Advances in Intelligent Health Management and Rehabilitation Technology: Integrating Large Language Models and AI Solutions)
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21 pages, 2617 KB  
Article
A Study on Evaluating Cardiovascular Diseases Using PPG Signals
by Lei Wang, Meng-Yu Hsiao, Zi-Jun Chen, Ruo-Jhen Wu and Meng-Ting Wu
Bioengineering 2025, 12(12), 1283; https://doi.org/10.3390/bioengineering12121283 - 21 Nov 2025
Viewed by 1145
Abstract
The widely used oximeter design was adopted and improved as the configuration mainframe in this study to acquire PPG signals. When users wear a finger probe and input their height, the device acquires PPG signals through the probe circuit, then filters and amplifies [...] Read more.
The widely used oximeter design was adopted and improved as the configuration mainframe in this study to acquire PPG signals. When users wear a finger probe and input their height, the device acquires PPG signals through the probe circuit, then filters and amplifies the signals to remove unnecessary noise, and uses an ARM-M4 to analyze the main peak, dicrotic wave, and wave valley of the PPG waveform to calculate related indexes for the final assessment. After 100 s, the HRV, sine wave ratio, and SI results are estimated, and a cardiovascular disease risk assessment is presented using a risk level from 0 to 5. This study uses the stiffness index (SI), sine wave ratio (SIN), and heart rate variability (HRV) to assess cardiovascular status. The SI is derived from PPG signal characteristics and reflects vascular stiffness based on blood flow rebound time. However, some PPG signals lack a dicrotic wave (sine waves), which is often caused by severe arterial stiffness. These waveforms lead to errors in SI calculation due to misidentification of the dicrotic wave. The appearance of a sine wave indicates that blood pulsation is abnormal; however, it will make the SI calculation algorithm produce a seemingly normal health performance. To address this, the auxiliary line method was introduced to identify sine waves, and the SIN ratio occurring in contiguous PPG waves was incorporated to calculate their proportion in PPG signals, aiding SI analysis and arterial stiffness evaluation. The total power (TP) value obtained via HRV frequency-domain analysis reflects autonomic nervous activity. As reduced autonomic function may relate to cardiovascular diseases, TP is included as an evaluation indicator. By analyzing PPG signals, calculating SI and SIN, and integrating the HRV indicator, this study evaluates arterial stiffness and cardiovascular health, helping participants understand their physical condition more quickly and conveniently, and potentially preventing cardiovascular diseases at an early stage. Full article
(This article belongs to the Special Issue Advances in Intelligent Health Management and Rehabilitation Technology: Integrating Large Language Models and AI Solutions)
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21 pages, 2761 KB  
Article
The Development and Evaluation of a Retrieval-Augmented Generation Large Language Model Virtual Assistant for Postoperative Instructions
by Syed Ali Haider, Srinivasagam Prabha, Cesar Abraham Gomez Cabello, Ariana Genovese, Bernardo Collaco, Nadia Wood, James London, Sanjay Bagaria, Cui Tao and Antonio Jorge Forte
Bioengineering 2025, 12(11), 1219; https://doi.org/10.3390/bioengineering12111219 - 7 Nov 2025
Cited by 2 | Viewed by 2046
Abstract
Background: During postoperative recovery, patients and their caregivers often lack crucial information, leading to numerous repetitive inquiries that burden healthcare providers. Traditional discharge materials, including paper handouts and patient portals, are often static, overwhelming, or underutilized, leading to patient overwhelm and contributing to [...] Read more.
Background: During postoperative recovery, patients and their caregivers often lack crucial information, leading to numerous repetitive inquiries that burden healthcare providers. Traditional discharge materials, including paper handouts and patient portals, are often static, overwhelming, or underutilized, leading to patient overwhelm and contributing to unnecessary ER visits and overall healthcare overutilization. Conversational chatbots offer a solution, but Natural Language Processing (NLP) systems are often inflexible and limited in understanding, while powerful Large Language Models (LLMs) are prone to generating “hallucinations”. Objective: To combine the deterministic framework of traditional NLP with the probabilistic capabilities of LLMs, we developed the AI Virtual Assistant (AIVA) Platform. This system utilizes a retrieval-augmented generation (RAG) architecture, integrating Gemini 2.0 Flash with a medically verified knowledge base via Google Vertex AI, to safely deliver dynamic, patient-facing postoperative guidance grounded in validated clinical content. Methods: The AIVA Platform was evaluated through 750 simulated patient interactions derived from 250 unique postoperative queries across 20 high-frequency recovery domains. Three blinded physician reviewers assessed formal system performance, evaluating classification metrics (accuracy, precision, recall, F1-score), relevance (SSI Index), completeness, and consistency (5-point Likert scale). Safety guardrails were tested with 120 out-of-scope queries and 30 emergency escalation scenarios. Additionally, groundedness, fluency, and readability were assessed using automated LLM metrics. Results: The system achieved 98.4% classification accuracy (precision 1.0, recall 0.98, F1-score 0.9899). Physician reviews showed high completeness (4.83/5), consistency (4.49/5), and relevance (SSI Index 2.68/3). Safety guardrails successfully identified 100% of out-of-scope and escalation scenarios. Groundedness evaluations demonstrated strong context precision (0.951), recall (0.910), and faithfulness (0.956), with 95.6% verification agreement. While fluency and semantic alignment were high (BERTScore F1 0.9013, ROUGE-1 0.8377), readability was 11th-grade level (Flesch–Kincaid 46.34). Conclusion: The simulated testing demonstrated strong technical accuracy, safety, and clinical relevance in simulated postoperative care. Its architecture effectively balances flexibility and safety, addressing key limitations of standalone NLP and LLMs. While readability remains a challenge, these findings establish a solid foundation, demonstrating readiness for clinical trials and real-world testing within surgical care pathways. Full article
(This article belongs to the Special Issue Advances in Intelligent Health Management and Rehabilitation Technology: Integrating Large Language Models and AI Solutions)
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