Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.9
5.0
Journals
AI
Special Issues
Transforming Biomedical Innovation with Artificial Intelligence
share announcement

Transforming Biomedical Innovation with Artificial Intelligence

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

Deadline for manuscript submissions: 15 July 2026 | Viewed by 3670

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

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • 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 (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

23 pages, 2837 KB  
Article
A Real-Time Laryngeal Disease Diagnosis Algorithm on Edge-AI
by Yarong Liu, Dong Leng, Xiaolan Xie and Zhiyu Li
AI 2026, 7(3), 113; https://doi.org/10.3390/ai7030113 - 18 Mar 2026
Viewed by 59
Abstract
Background: Laryngeal lesions represent a significant clinical challenge due to the complexity of the laryngeal structure, making manual diagnosis time-consuming and prone to subjective errors. Therefore, developing an accurate and lightweight automatic detection method is essential for improving the efficiency of laryngeal disease [...] Read more.
Background: Laryngeal lesions represent a significant clinical challenge due to the complexity of the laryngeal structure, making manual diagnosis time-consuming and prone to subjective errors. Therefore, developing an accurate and lightweight automatic detection method is essential for improving the efficiency of laryngeal disease screening and diagnosis. Methods: This study proposes MSBA-YOLO, a lightweight laryngeal disease detection algorithm based on an improved YOLOv5s architecture. The method integrates FasterNet as the backbone network to reduce computational redundancy through partial convolutions and incorporates a Single-Head Self-Attention mechanism to capture long-range dependencies in complex lesion features. In addition, an MSBA-FIoU loss function is introduced to enhance the localization accuracy of multi-scale targets. Results: Experimental results show that MSBA-YOLO achieves a mean Average Precision (mAP) of 96.1% with a model size of only 6.4 MB, representing a 54.6% reduction in parameters compared with the baseline model. When deployed on the Jetson Orin Nano edge platform, the proposed method achieves real-time inference with a speed exceeding 50 FPS while maintaining low power consumption of 5.82 W. Conclusions: The results demonstrate that MSBA-YOLO effectively balances detection accuracy and computational efficiency, providing a robust and practical solution for portable and real-time clinical screening of laryngeal diseases on edge devices. Full article
(This article belongs to the Special Issue Transforming Biomedical Innovation with Artificial Intelligence)
►▼ Show Figures

Figure 1

19 pages, 1923 KB  
Article
A Novel Recurrent Neural Network Framework for Prediction and Treatment of Oncogenic Mutation Progression
by Rishab Parthasarathy and Achintya K. Bhowmik
AI 2026, 7(2), 54; https://doi.org/10.3390/ai7020054 - 2 Feb 2026
Viewed by 604
Abstract
Despite significant medical advancements, cancer remains the second leading cause of death in the US, causing over 600,000 deaths per year. One emerging field, pathway analysis, is promising but still relies on manually derived wet lab data, which is time-consuming to acquire. This [...] Read more.
Despite significant medical advancements, cancer remains the second leading cause of death in the US, causing over 600,000 deaths per year. One emerging field, pathway analysis, is promising but still relies on manually derived wet lab data, which is time-consuming to acquire. This work proposes an efficient, effective, end-to-end framework for Artificial Intelligence (AI)-based pathway analysis that predicts both cancer severity and mutation progression in order to recommend possible treatments. The proposed technique involves a novel combination of time-series machine learning models and pathway analysis. First, mutation sequences were isolated from The Cancer Genome Atlas (TCGA) Database. Then, a novel preprocessing algorithm was used to filter key mutations by mutation frequency. This data was fed into a Recurrent Neural Network (RNN) that predicted cancer severity. The model probabilistically used the RNN predictions, information from the preprocessing algorithm, and multiple drug-target databases to predict future mutations and recommend possible treatments. This framework achieved robust results and Receiver Operating Characteristic (ROC) curves (a key statistical metric) with accuracies greater than 60%, similar to existing cancer diagnostics. In addition, preprocessing played a key role in isolating a few hundred key driver mutations per cancer stage, consistent with current research. Heatmaps based on predicted gene frequency were also generated, highlighting key mutations in each cancer. Overall, this work is the first to propose an efficient, cost-effective end-to-end framework for projecting cancer prognosis and providing possible treatments without relying on expensive, time-consuming wet lab work. Full article
(This article belongs to the Special Issue Transforming Biomedical Innovation with Artificial Intelligence)
►▼ Show Figures

Figure 1

24 pages, 6118 KB  
Article
Effective Approach for Classifying EMG Signals Through Reconstruction Using Autoencoders
by Natalia Rendón Caballero, Michelle Rojo González, Marcos Aviles, José Manuel Alvarez Alvarado, José Billerman Robles-Ocampo, Perla Yazmin Sevilla-Camacho and Juvenal Rodríguez-Reséndiz
AI 2026, 7(1), 36; https://doi.org/10.3390/ai7010036 - 22 Jan 2026
Viewed by 467
Abstract
The study of muscle signal classification has been widely explored for the control of myoelectric prostheses. Traditional approaches rely on manually designed features extracted from time- or frequency-domain representations, which may limit the generalization and adaptability of EMG-based systems. In this work, an [...] Read more.
The study of muscle signal classification has been widely explored for the control of myoelectric prostheses. Traditional approaches rely on manually designed features extracted from time- or frequency-domain representations, which may limit the generalization and adaptability of EMG-based systems. In this work, an autoencoder-based framework is proposed for automatic feature extraction, enabling the learning of compact latent representations directly from raw EMG signals and reducing dependence on handcrafted features. A custom instrumentation system with three surface EMG sensors was developed and placed on selected forearm muscles to acquire signals associated with five hand movements from 20 healthy participants aged 18 to 40 years. The signals were segmented into 200 ms windows with 75% overlap. The proposed method employs a recurrent autoencoder with a symmetric encoder–decoder architecture, trained independently for each sensor to achieve accurate signal reconstruction, with a minimum reconstruction loss of 3.3×10−4V2. The encoder’s latent representations were then used to train a dense neural network for gesture classification. An overall efficiency of 93.84% was achieved, demonstrating that the proposed reconstruction-based approach provides high classification performance and represents a promising solution for future EMG-based assistive and control applications. Full article
(This article belongs to the Special Issue Transforming Biomedical Innovation with Artificial Intelligence)
►▼ Show Figures

Figure 1

33 pages, 582 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 - 14 Jan 2026
Viewed by 515
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)
►▼ Show Figures

Figure 1

Review

Jump to: Research

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
Viewed by 1788
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)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop