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 1695

Special Issue Editor

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

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Keywords

  • artificial intelligence
  • machine learning
  • artificial neural networks
  • prognosis
  • treatment
  • medicine
  • health care
  • pathology

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Published Papers (1 paper)

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Review

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 1194
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
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