computational tools

Overview

Computational tools in the biomedical field encompass a range of technologies and methodologies that leverage computational power to analyze biological data, enhance diagnostic accuracy, and support therapeutic decision-making. These tools are pivotal in the integration of artificial intelligence (AI) and machine learning (ML) into healthcare, facilitating advancements in personalized medicine, drug discovery, and clinical diagnostics. By processing vast datasets, including multi-omics data, electronic health records, and imaging data, computational tools enable researchers and clinicians to uncover patterns, predict outcomes, and optimize treatment strategies for various diseases, including cancer, cardiovascular diseases, and neurodegenerative disorders.

The biological significance of computational tools lies in their ability to model complex biological systems, simulate drug interactions, and predict patient responses to therapies. For instance, AI-driven models can analyze the tumor microenvironment in cancers such as breast and lung malignancies, aiding in the identification of potential therapeutic targets and improving patient stratification for treatments like immune checkpoint inhibitors. As these tools evolve, they are increasingly integrated into clinical workflows, enhancing the precision and efficiency of healthcare delivery.

Focus of Latest Publications

Recent studies have highlighted the transformative impact of computational tools, particularly AI and machine learning, across various domains of medicine. For instance, a randomized controlled trial investigated the integration of AI-assisted case-based learning in reproductive medicine, demonstrating potential improvements in clinical decision-making (PMID: 42200362). This study underscores the role of computational tools in enhancing educational methodologies and clinical reasoning.

In the realm of diagnostics, research has focused on the comparative performance of AI models against human experts in interpreting radiographic images, such as in oral and maxillofacial radiography (PMID: 42093540). These studies reveal that AI can augment diagnostic accuracy, particularly in complex cases like diabetic retinopathy screening, where AI systems showed promising agreement with expert assessments (PMID: 41991402). The application of AI in imaging is further exemplified by its use in echocardiography, where tools like RadAnalyzer facilitate the measurement of cardiac structures, enhancing diagnostic workflows (PMID: 42127004).

Moreover, computational tools are being employed in drug discovery and development, with AI models streamlining the identification of therapeutic targets and optimizing drug design processes (PMID: 41630488). The integration of multi-omics data with AI has been shown to enhance predictive modeling for treatment responses in conditions such as inflammatory bowel disease and cancer (PMID: 41955187). Additionally, AI-driven mobile applications are emerging as valuable resources for patient management, particularly in chronic conditions like diabetes (PMID: 42172283).

The exploration of AI in clinical settings also extends to patient-centered care, where studies have evaluated the effectiveness of AI-driven chatbots in supporting patients with substance use disorders (PMID: 42160748). These findings highlight the potential of computational tools to improve access to care and enhance patient engagement.

Key Publications

• Jun Spatial Visual Proteomics: Insights into Tumor Microenvironment Dynamics. (Genomics, proteomics & bioinformatics, 2026, PMID 42301931): "Computational tools address challenges in cell segmentation, data deconvolution, and multi-omics integration, bridging proteomic, genomic, and transcriptomic datasets."
• Jun Hospitalists Are Already Using AI-Why Implementation Will Determine Its Impact. (Journal of medical Internet research, 2026, PMID 42224270): "The adoption of artificial intelligence (AI) into clinical practice is accelerating, outpacing the development of organizational guidance, training, and governance."
• Jun Adaptive AI for Cardiovascular Event Adjudication: Cardiovascular Event Adjudication Across Different Definitions in the ODYSSEY OUTCOMES and EUCLID Trials. (Circulation, 2026, PMID 41911340): "We developed and validated an artificial intelligence (AI) algorithm (adaptive AI for CEC [ADAPT-CEC]) that adjudicates multiple cardiovascular end points and adapts to new definitions."
• Jun Human versus AI in audiological practice: A comparative evaluation of ChatGPT and physician treatment decisions in idiopathic sudden sensorineural hearing loss. (PloS one, 2026, PMID 42224219): "Artificial Intelligence (AI) is increasingly being applied in the field of audiology, demonstrating potential to support screening, diagnosis, and rehabilitation in auditory and vestibular disorders."
• Jun AI-driven design of antimicrobial peptide for combating resistance and infectious diseases. (Pathology, research and practice, 2026, PMID 41880932): "To overcome these barriers, we employed artificial intelligence (AI) and machine learning (ML) to design a fifteen-amino acid peptide, LCN-15 (RWWRRKKLKAPIWVR), with a molecular weight of 2079 Da."
• Jun Comparative assessment of diagnostic agreement between artificial intelligence and general practitioners in diabetic retinopathy screening using non-mydriatic fundus photography. (Primary care diabetes, 2026, PMID 41991402): "To assess the diagnostic agreement between an artificial intelligence (AI) system and general practitioners (GPs) interpreting fundus photographs for diabetic retinopathy (DR) screening, using the ophthalmologists' assessment as the reference standard."
• Jun Personalized weight loss management through wearable devices and artificial intelligence. (Computers in biology and medicine, 2026, PMID 41996945): "This study explores the application of wearable devices and artificial intelligence (AI) to predict weight loss changes in overweight and obese individuals."
• Jun FDA-authorized oncology artificial intelligence and machine learning devices and their clinical evidence: A cross-sectional analysis. (Journal of cancer policy, 2026, PMID 42025919): "Artificial intelligence (AI) and machine learning (ML) tools are increasingly embedded in cancer care, yet the scope of U.S. Food and Drug Administration (FDA) oncology-specific authorizations and the clinical evidence described in publicly available decision documentation remain unclear."
• Jun Clinical Informatics and Artificial Intelligence: What the Working Emergency Physician Needs to Know. (The Journal of emergency medicine, 2026, PMID 42066526): "While studies from the informatics literature had been gradually introducing more sophisticated machine learning and artificial intelligence (AI) techniques into clinical settings."
• Jun Occupational therapists' perceptions of artificial intelligence: Potential, preparedness, and training needs. (Australian occupational therapy journal, 2026, PMID 42084275): "Artificial intelligence (AI) is transforming health care, yet its impact on occupational therapy remains underexplored."