Application of Artificial Intelligence in Biomedicines

A special issue of Biomedicines (ISSN 2227-9059). This special issue belongs to the section "Molecular and Translational Medicine".

Deadline for manuscript submissions: 28 February 2026 | Viewed by 1319

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NeMO Lab Research Center, ASST Grande Ospedale Metropolitano Niguarda, 20162 Milan, Italy
Interests: supramolecular hydrogels; nanostructured scaffolds; self-assembling peptides; polymers; nanofibers; nano-nutraceuticals; bioactive materials; regenerative medicine; tissue engineering; neural stem cell
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Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) plays an increasingly transformative role in biomedicine, enabling breakthroughs across a wide range of domains, ranging from accelerating drug discovery and enhancing diagnostic accuracy to enabling precision medicine and augmenting surgical procedures. This Special Issue will showcase the latest advances, innovative methodologies, and real-world applications of AI in biomedical research.

AI and Generative AI have demonstrated an exceptional capacity to analyze complex, high-dimensional datasets; uncover hidden patterns; and generate predictive models that support data-driven decision-making in clinical and research environments. With the advent of deep learning, reinforcement learning, and other advanced techniques, AI is rapidly redefining the frontiers of biomedical science.

Hence, in this Special Issue, we welcome original research articles and reviews that explore—but are not limited to—the following topics:

Drug Discovery and Development

  • AI-driven identification and optimization of drug candidates;
  • Predictive models for drug efficacy, toxicity, and pharmacokinetics;
  • Computational design of bioactive molecules and peptides. 

Disease Diagnosis and Prognosis

  • AI-based medical image analysis (e.g., MRI, CT, histopathology);
  • Predictive analytics for early disease detection and progression modeling. 

Personalized and Precision Medicine

  • AI algorithms for individualized therapy recommendations;
  • Monitoring and adaptive treatment strategies using AI. 

Biomedical Research and Discovery

  • Generative AI for hypothesis generation and experimental design;
  • AI in clinical decision support;
  • Knowledge discovery from scientific literature using natural language processing (NLP) and Large Langue Models (LLMs);
  • Ensuring fairness, transparency, and accountability in AI models;
  • Ethical principles for trustworthy AI in personalized medicine. 

This Special Issue will bring together contributions from academia and industry to provide a comprehensive overview of how AI is shaping the future of biomedicine. By highlighting both opportunities and challenges, including those related to ethics and trust, we hope to foster interdisciplinary dialogue and drive the responsible integration of AI into biomedical science.

Dr. Raffaele Pugliese
Guest Editor

Manuscript Submission Information

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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. Biomedicines 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 2600 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
  • generative artificial intelligence
  • precision medicine
  • clinical decision support
  • drug discovery
  • personalized therapy
  • trustworthy AI

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Published Papers (1 paper)

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Review

33 pages, 955 KB  
Review
Artificial Intelligence-Driven Neuromodulation in Neurodegenerative Disease: Precision in Chaos, Learning in Loss
by Andrea Calderone, Desirèe Latella, Elvira La Fauci, Roberta Puleo, Arturo Sergi, Mariachiara De Francesco, Maria Mauro, Angela Foti, Leda Salemi and Rocco Salvatore Calabrò
Biomedicines 2025, 13(9), 2118; https://doi.org/10.3390/biomedicines13092118 - 30 Aug 2025
Viewed by 1208
Abstract
Neurodegenerative disorders such as Alzheimer’s disease (AD), Parkinson’s disease (PD), and multiple sclerosis (MS) are marked by progressive network dysfunction that challenges conventional, protocol-based neurorehabilitation. In parallel, neuromodulation, encompassing deep brain stimulation (DBS), transcranial magnetic stimulation (TMS), transcranial direct current stimulation (tDCS), vagus [...] Read more.
Neurodegenerative disorders such as Alzheimer’s disease (AD), Parkinson’s disease (PD), and multiple sclerosis (MS) are marked by progressive network dysfunction that challenges conventional, protocol-based neurorehabilitation. In parallel, neuromodulation, encompassing deep brain stimulation (DBS), transcranial magnetic stimulation (TMS), transcranial direct current stimulation (tDCS), vagus nerve stimulation (VNS), and artificial intelligence (AI), has matured rapidly, offering complementary levers to tailor therapy in real time. This narrative review synthesizes current evidence at the intersection of AI and neuromodulation in neurorehabilitation, focusing on how data-driven models can personalize stimulation and improve functional outcomes. We conducted a targeted literature synthesis of peer-reviewed studies identified via PubMed, Embase, Scopus, and reference chaining, prioritizing recent clinical and translational reports on adaptive/closed-loop systems, predictive modeling, and biomarker-guided protocols. Across indications, convergent findings show that AI can optimize device programming, enable state-dependent stimulation, and support clinician decision-making through multimodal biomarkers derived from neural, kinematic, and behavioral signals. Key barriers include data quality and interoperability, model interpretability and safety, and ethical and regulatory oversight. Here we argue that AI-enhanced neuromodulation reframes neurorehabilitation from static dosing to adaptive, patient-specific care. Advancing this paradigm will require rigorous external validation, standardized reporting of control policies and artifacts, clinician-in-the-loop governance, and privacy-preserving analytics. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Biomedicines)
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