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Transforming Biomedical Innovation with Artificial Intelligence
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Transforming Biomedical Innovation with Artificial Intelligence

A special issue of AI (ISSN 2673-2688).

Deadline for manuscript submissions: 15 July 2026 | Viewed by 311

Special Issue Editor

Dr. Achintya K. Bhowmik

E-Mail Website
Guest Editor
Stanford University School of Medicine, Stanford University, Stanford, CA 94305, USA
Interests: sensory augmentation; computational perception; cognitive neuroscience; intelligent systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) is rapidly reshaping the landscape of biomedical sciences by enabling the development of intelligent systems that can perceive, analyze, predict, and assist in complex biological and clinical tasks. From precision diagnostics and personalized therapeutics to digital pathology and wearable health monitoring, AI is catalyzing a profound transformation across the biomedical spectrum.

Recent advances in machine learning, deep neural networks, natural language processing, and multimodal data integration have demonstrated remarkable success in analyzing diverse and large-scale biomedical datasets, including medical imaging, genomics, electronic health records, sensor signals, and speech. These developments are opening new avenues for disease prediction, early diagnosis, treatment planning, drug discovery, and patient monitoring, with implications for clinical decision support and population health.

This Special Issue invites original research papers, comprehensive reviews, and visionary perspectives that explore how AI is transforming biomedical sciences in theory and practice. We are particularly interested in contributions that combine methodological innovation with real-world applicability, especially those that address challenges in interpretability, generalizability, fairness, and integration into clinical workflows.

By bringing together interdisciplinary efforts from AI researchers, biomedical scientists, healthcare professionals, and technologists, this Special Issue aims to illuminate the evolving synergy between artificial intelligence and biomedical discovery toward a future of intelligent, personalized, and equitable healthcare.

Dr. Achintya K. Bhowmik
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. AI 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 1800 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 in biomedicine
  • machine learning for health and disease
  • deep learning for biomedical innovation
  • medical imaging and computational diagnostics
  • digital health technologies and wearable AI devices
  • bioinformatics and computational biology
  • clinical decision support and predictive analytics
  • explainable, robust, and ethical AI in healthcare
  • multimodal biomedical data fusion
  • AI for drug discovery and personalized therapeutics

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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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36 pages, 575 KB  
Article
In Silico Proof of Concept: Conditional Deep Learning-Based Prediction of Short Mitochondrial DNA Fragments in Archosaurs
by Dimitris Angelakis, Dionisis Cavouras, Dimitris Th. Glotsos, Spiros A. Kostopoulos, Emmanouil I. Athanasiadis, Ioannis K. Kalatzis and Pantelis A. Asvestas
AI 2026, 7(1), 27; https://doi.org/10.3390/ai7010027 (registering DOI) - 14 Jan 2026
Abstract
This study presents an in silico proof of concept exploring whether deep learning models can perform conditional mitochondrial DNA (mtDNA) sequence prediction across species boundaries. A CNN–BiLSTM model was trained under a leave-one-species-out (LOSO) scheme on complete mitochondrial genomes from 21 vertebrate species, [...] Read more.
This study presents an in silico proof of concept exploring whether deep learning models can perform conditional mitochondrial DNA (mtDNA) sequence prediction across species boundaries. A CNN–BiLSTM model was trained under a leave-one-species-out (LOSO) scheme on complete mitochondrial genomes from 21 vertebrate species, primarily archosaurs. Model behavior was evaluated through multiple complementary tests. Under context-conditioned settings, the model performed next-nucleotide prediction using overlapping 200 bp windows to assemble contiguous 2000 bp fragments for held-out species; the resulting high token-level accuracy (>99%) under teacher forcing is reported as a diagnostic of conditional modeling capacity. To assess leakage-free performance, a two-flank masked-span imputation task was conducted as the primary evaluation, requiring free-running reconstruction of 500 bp interior spans using only distal flanking context; in this setting, the model consistently outperformed nearest-neighbor and demonstrated competitive performance relative to flank-copy baselines. Additional robustness analyses examined sensitivity to window placement, genomic region (coding versus D-loop), and random initialization. Biological plausibility was further assessed by comparing predicted fragments to reconstructed ancestral sequences and against composition-matched null models, where observed identities significantly exceeded null expectations. Using the National Center for Biotechnology Information (NCBI) BLAST web interface, BLASTn species identification was performed solely as a biological plausibility check, recovering the correct species as the top hit in all cases. Although limited by dataset size and the absence of ancient DNA damage modeling, these results demonstrate the feasibility of conditional mtDNA sequence prediction as an initial step toward more advanced generative and evolutionary modeling frameworks. Full article
(This article belongs to the Special Issue Transforming Biomedical Innovation with Artificial Intelligence)

Review

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28 pages, 2594 KB  
Review
From Algorithm to Medicine: AI in the Discovery and Development of New Drugs
by Ana Beatriz Lopes, Célia Fortuna Rodrigues and Francisco A. M. Silva
AI 2026, 7(1), 26; https://doi.org/10.3390/ai7010026 - 14 Jan 2026
Abstract
The discovery and development of new drugs is a lengthy, complex, and costly process, often requiring 10–20 years to progress from initial concept to market approval, with clinical trials representing the most resource-intensive stage. In recent years, Artificial Intelligence (AI) has emerged as [...] Read more.
The discovery and development of new drugs is a lengthy, complex, and costly process, often requiring 10–20 years to progress from initial concept to market approval, with clinical trials representing the most resource-intensive stage. In recent years, Artificial Intelligence (AI) has emerged as a transformative technology capable of reshaping the entire pharmaceutical research and development (R&D) pipeline. The purpose of this narrative review is to examine the role of AI in drug discovery and development, highlighting its contributions, challenges, and future implications for pharmaceutical sciences and global public health. A comprehensive review of the scientific literature was conducted, focusing on published studies, reviews, and reports addressing the application of AI across the stages of drug discovery, preclinical development, clinical trials, and post-marketing surveillance. Key themes were identified, including AI-driven target identification, molecular screening, de novo drug design, predictive toxicity modelling, and clinical monitoring. The reviewed evidence indicates that AI has significantly accelerated drug discovery and development by reducing timeframes, costs, and failure rates. AI-based approaches have enhanced the efficiency of target identification, optimized lead compound selection, improved safety predictions, and supported adaptive clinical trial designs. Collectively, these advances position AI as a catalyst for innovation, particularly in promoting accessible, efficient, and sustainable healthcare solutions. However, substantial challenges remain, including reliance on high-quality and representative biomedical data, limited algorithmic transparency, high implementation costs, regulatory uncertainty, and ethical and legal concerns related to data privacy, bias, and equitable access. In conclusion, AI represents a paradigm shift in pharmaceutical research and drug development, offering unprecedented opportunities to improve efficiency and innovation. Addressing its technical, ethical, and regulatory limitations will be essential to fully realize its potential as a sustainable and globally impactful tool for therapeutic innovation. Full article
(This article belongs to the Special Issue Transforming Biomedical Innovation with Artificial Intelligence)
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