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Bioengineering
Special Issues
Application of Artificial Intelligence in Complex Diseases
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Application of Artificial Intelligence in Complex Diseases

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

Deadline for manuscript submissions: 31 August 2025 | Viewed by 4475

Special Issue Editor

Dr. Yan Zhu

E-Mail Website
Guest Editor
Department of Ophthalmology, University of Pennsylvania, Philadelphia, PA 19104, USA
Interests: artificial intelligence; machine learning; healthcare; glaucoma; cancer; bioinformatics

Special Issue Information

Dear Colleagues,

The application of Artificial Intelligence (AI) in the medical field is poised to revolutionize healthcare, particularly in managing complex diseases such as cancers and glaucoma. Integrating AI technologies promises to enhance diagnostic accuracy, personalize treatment plans, and improve patient outcomes. This Special Issue of Bioengineering on "Application of Artificial Intelligence in Complex Diseases" aims to bring together contributions from experts worldwide to discuss advancements and innovations in this domain. Topics of interest include AI-driven diagnostic tools, predictive modeling, AI in medical imaging, and the ethical implications of AI in healthcare.

This issue will provide a platform for researchers and clinicians to share their latest findings, methodologies, and insights, fostering a collaborative environment to address the challenges and opportunities presented by AI in medicine.

Dr. Yan Zhu
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 100 words) can be sent to the Editorial Office for announcement on this website.

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

  • artificial intelligence
  • machine learning
  • complex diseases
  • healthcare
  • diagnostics
  • predictive modeling
  • medical imaging
  • ethics in AI

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Published Papers (2 papers)

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Research

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15 pages, 1561 KiB  
Article
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 2 | Viewed by 1943
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

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26 pages, 1904 KiB  
Review
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
Viewed by 2046
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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