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BioMedInformatics
Special Issues
Integrating Health Informatics and Artificial Intelligence for Advanced Medicine
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Integrating Health Informatics and Artificial Intelligence for Advanced Medicine

A special issue of BioMedInformatics (ISSN 2673-7426).

Deadline for manuscript submissions: 31 August 2025 | Viewed by 4017

Special Issue Editor

Dr. Joaquim Carreras

E-Mail Website
Guest Editor
Department of Pathology, Faculty of Medicine, Tokai University School of Medicine, 143 Shimokasuya, Isehara 259-1193, Japan
Interests: artificial intelligence; molecular histopathology; pathology; neoplasia; inflammatory diseases; biomarkers; immune checkpoint; immuno-oncology; health care informatics; diagnosis and treatment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) is transforming medical practice through innovative applications that enhance the quality, efficiency, and personalization of healthcare. This Special Issue explores AI’s role across diverse areas, advancing our understanding of how AI-driven methods can support healthcare professionals and improve patient outcomes. Topics include, but are not limited to, the following:

  • Disease Detection and Diagnosis: leveraging AI for the early and precise identification of diseases, aiding clinicians in diagnosing complex conditions and improving clinical decision making.
  • Personalized Treatment: utilizing AI to customize treatment plans that align with individual patient profiles, leading to more effective, targeted therapies.
  • Medical Imaging: enhancing diagnostic imaging interpretation through machine learning and deep learning algorithms, allowing for quicker, more accurate analyses of medical images.
  • Clinical Trial Efficiency: streamlining clinical trial processes by applying AI to participant selection, data management, and real-time monitoring, thereby accelerating drug development and improving trial outcomes.
  • Accelerated Drug Development: incorporating AI to identify drug candidates, predict outcomes, and optimize the discovery process, shortening the time to bring new treatments to patients.

We welcome original research, reviews, and case studies that demonstrate AI’s capabilities and the latest advancements in these areas.

You may choose our Joint Special Issue in Healthcare, Joint Special Issue in Diagnostics, or Joint Special Issue in Life.

Dr. Joaquim Carreras
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. BioMedInformatics is an international peer-reviewed open access quarterly 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 1000 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
  • artificial neural networks
  • prognosis
  • treatment
  • medicine
  • health care
  • pathology

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  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

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14 pages, 917 KiB  
Article
Deep Learning Treatment Recommendations for Patients Diagnosed with Non-Metastatic Castration-Resistant Prostate Cancer Receiving Androgen Deprivation Treatment
by Chunyang Li, Julia Bohman, Vikas Patil, Richard Mcshinsky, Christina Yong, Zach Burningham, Matthew Samore and Ahmad S. Halwani
BioMedInformatics 2025, 5(3), 42; https://doi.org/10.3390/biomedinformatics5030042 - 4 Aug 2025
Viewed by 397
Abstract
Background: Prostate cancer (PC) is the second leading cause of cancer-related death in men in the United States. A subset of patients develops non-metastatic, castration-resistant PC (nmCRPC), for which management requires a personalized consideration for appropriate treatment. However, there is no consensus regarding [...] Read more.
Background: Prostate cancer (PC) is the second leading cause of cancer-related death in men in the United States. A subset of patients develops non-metastatic, castration-resistant PC (nmCRPC), for which management requires a personalized consideration for appropriate treatment. However, there is no consensus regarding when to switch from androgen deprivation therapy (ADT) to more aggressive treatments like abiraterone or enzalutamide. Methods: We analyzed 5037 nmCRPC patients and employed a Weibull Time to Event Recurrent Neural Network to identify patients who would benefit from switching from ADT to abiraterone/enzalutamide. We evaluated this model using differential treatment benefits measured by the Kaplan–Meier estimation and milestone probabilities. Results: The model achieved an area under the curve of 0.738 (standard deviation (SD): 0.057) for patients treated with abiraterone/enzalutamide and 0.693 (SD: 0.02) for patients exclusively treated with ADT at the 2-year milestone. The model recommended 14% of ADT patients switch to abiraterone/enzalutamide. Analysis showed a statistically significant absolute improvement using model-recommended treatments in progression-free survival (PFS) of 0.24 (95% confidence interval (CI): 0.23–0.24) at the 2-year milestone (PFS rate increasing from 0.50 to 0.74) with a hazard ratio of 0.44 (95% CI: 0.39–0.50). Conclusions: Our model successfully identified nmCRPC patients who would benefit from switching to abiraterone/enzalutamide, demonstrating potential outcome improvements. Full article
(This article belongs to the Special Issue Integrating Health Informatics and Artificial Intelligence for Advanced Medicine)
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Review

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40 pages, 2828 KiB  
Review
Generative Artificial Intelligence in Healthcare: Applications, Implementation Challenges, and Future Directions
by Syed Arman Rabbani, Mohamed El-Tanani, Shrestha Sharma, Syed Salman Rabbani, Yahia El-Tanani, Rakesh Kumar and Manita Saini
BioMedInformatics 2025, 5(3), 37; https://doi.org/10.3390/biomedinformatics5030037 - 7 Jul 2025
Viewed by 3117
Abstract
Generative artificial intelligence (AI) is rapidly transforming healthcare systems since the advent of OpenAI in 2022. It encompasses a class of machine learning techniques designed to create new content and is classified into large language models (LLMs) for text generation and image-generating models [...] Read more.
Generative artificial intelligence (AI) is rapidly transforming healthcare systems since the advent of OpenAI in 2022. It encompasses a class of machine learning techniques designed to create new content and is classified into large language models (LLMs) for text generation and image-generating models for creating or enhancing visual data. These generative AI models have shown widespread applications in clinical practice and research. Such applications range from medical documentation and diagnostics to patient communication and drug discovery. These models are capable of generating text messages, answering clinical questions, interpreting CT scan and MRI images, assisting in rare diagnoses, discovering new molecules, and providing medical education and training. Early studies have indicated that generative AI models can improve efficiency, reduce administrative burdens, and enhance patient engagement, although most findings are preliminary and require rigorous validation. However, the technology also raises serious concerns around accuracy, bias, privacy, ethical use, and clinical safety. Regulatory bodies, including the FDA and EMA, are beginning to define governance frameworks, while academic institutions and healthcare organizations emphasize the need for transparency, supervision, and evidence-based implementation. Generative AI is not a replacement for medical professionals but a potential partner—augmenting decision-making, streamlining communication, and supporting personalized care. Its responsible integration into healthcare could mark a paradigm shift toward more proactive, precise, and patient-centered systems. Full article
(This article belongs to the Special Issue Integrating Health Informatics and Artificial Intelligence for Advanced Medicine)
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