JACC. Cardiovascular imaging · 2024
Bon-Kwon Koo、Seokhun Yang、Jae Wook Jung 等
BACKGROUND: A lesion-level risk prediction for acute coronary syndrome (ACS) needs better characterization. OBJECTIVES: This study sought to investigate the additive value of artificial intelligence-enabled quantitative coronary plaque and hemodynamic analysis (AI-QCPHA). METHODS: Among ACS patients who underwent coronary computed tomography angiography (CTA) from 1 month to 3 years before the ACS event, culprit and nonculprit lesions on coronary CTA were adjudicated based on invasive coronary angiography. The primary endpoint was the predictability of the risk models for ACS culprit lesions. The reference model included the Coronary Artery Disease Reporting and Data System, a standardized classification for stenosis severity, and high-risk plaque, defined as lesions with ≥2 adverse plaque characteristics. The new prediction model was the reference model plus AI-QCPHA features, selected by hierarchical clustering and information gain in the derivation cohort. The model performance was assessed in the validation cohort. RESULTS: Among 351 patients (age: 65.9 ± 11.7 years) with 2,088 nonculprit and 363 culprit lesions, the median interval from coronary CTA to ACS event was 375 days (Q1-Q3: 95-645 days), and 223 patients (63.5%) presented with myocardial infarction. In the derivation cohort (n = 243), the best AI-QCPHA features were fractional flow reserve across the lesion, plaque burden, total plaque volume, low-attenuation plaque volume, and averaged percent total myocardial blood flow. The addition of AI-QCPHA features showed higher predictability than the reference model in the validation cohort (n = 108) (AUC: 0.84 vs 0.78; P < 0.001). The additive value of AI-QCPHA features was consistent across different timepoints from coronary CTA. CONCLUSIONS: AI-enabled plaque and hemodynamic quantification enhanced the predictability for ACS culprit lesions over the conventional coronary CTA analysis. (Exploring the Mechanism of Plaque Rupture in Acute Coronary Syndrome Using Coronary Computed Tomography Angiography and Computational Fluid Dynamics II [EMERALD-II]; NCT03591328).
United European gastroenterology journal · 2024
Marietta Iacucci、Yasuharu Maeda、Subrata Ghosh
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Cancer Treatment Reviews · 2026
W. Haque、Kareena Sai-Palm、I. Rana 等
Artificial intelligence (AI) is increasingly integrated into pharmaceutical discovery, yet the extent to which AI-enabled assets have translated into clinical-stage programs remains incompletely characterized. We performed a cross-sectional descriptive analysis of therapeutic assets discovered or developed using AI approaches that had entered at least one interventional human clinical trial through March 1, 2026. We identified 129 AI-enabled therapeutic assets across 69 companies. Oncology was the dominant therapeutic area (74 assets, 57.3%), and small molecules accounted for the majority of the pipeline (103 assets, 79.8%). Among these assets, 72 (55.8%) had completed Phase 1 and 24 (18.9%) had completed Phase 2; only five had entered or completed Phase 3. The observed probability of success was 77.8% for Phase 1 and 19.5% for Phase 2. Forty-eight assets (37.2%) were classified as targeting a novel biological target. Among companies with available data, the median time from founding to Phase 1 entry was 6.5 years (IQR 4.0-9.2), median total funding raised was $186.7 million (IQR $48.1-$595.0 million), and the median employee count at clinical entry was 73 (IQR 32-129), with a median operational intensity of $2.72 million per employee. AI-enabled therapeutics are entering clinical testing at increasing scale, but most programs remain early in development, with few having advanced beyond Phase 2. This dataset provides a baseline for future evaluation of whether AI-enabled discovery ultimately improves regulatory approval rates and clinical outcomes.
Annual review of medicine · 2022
Pratik Sinha、Nuala J Meyer、Carolyn S Calfee
Heterogeneity in sepsis and acute respiratory distress syndrome (ARDS) is increasingly being recognized as one of the principal barriers to finding efficacious targeted therapies. The advent of multiple high-throughput biological data ("omics"), coupled with the widespread access to increased computational power, has led to the emergence of phenotyping in critical care. Phenotyping aims to use a multitude of data to identify homogenous subgroups within an otherwise heterogenous population. Increasingly, phenotyping schemas are being applied to sepsis and ARDS to increase understanding of these clinical conditions and identify potential therapies. Here we present a selective review of the biological phenotyping schemas applied to sepsis and ARDS. Further, we outline some of the challenges involved in translating these conceptual findings to bedside clinical decision-making tools.
American Society of Clinical Oncology educational book. American Society of Clinical Oncology. Annual Meeting · 2025
A. Loaiza-Bonilla、N. Thaker、Caroline Chung 等
Artificial intelligence (AI) is transforming multidisciplinary oncology at an unprecedented pace, redefining how clinicians detect, classify, and treat cancer. From earlier and more accurate diagnoses to personalized treatment planning, AI's impact is evident across radiology, pathology, radiation oncology, and medical oncology. By leveraging vast and diverse data-including imaging, genomic, clinical, and real-world evidence-AI algorithms can uncover complex patterns, accelerate drug discovery, and help identify optimal treatment regimens for each patient. However, realizing the full potential of AI also necessitates addressing concerns regarding data quality, algorithmic bias, explainability, privacy, and regulatory oversight-especially in low- and middle-income countries (LMICs), where disparities in cancer care are particularly pronounced. This study provides a comprehensive overview of how AI is reshaping cancer care, reviews its benefits and challenges, and outlines ethical and policy implications in line with ASCO's 2025 theme, Driving Knowledge to Action. We offer concrete calls to action for clinicians, researchers, industry stakeholders, and policymakers to ensure that AI-driven, patient-centric oncology is accessible, equitable, and sustainable worldwide.
Journal of Clinical Oncology · 2026
Enrique I. Velazquez Villarreal、F. Carranza、B. Waldrup 等
192
Background:
Early-onset colorectal cancer (EOCRC; <50 years) is increasing at an alarming rate and disproportionately affects Hispanic/Latino (H/L) and other underserved populations. The biological drivers of EOCRC and the influence of clinical exposures and social determinants of health (SDoH) remain poorly defined. Spatial proteomic and transcriptomic technologies enable high-resolution analysis of tumor, immune, and stromal compartments, while artificial intelligence (AI) offers opportunities for data integration. We hypothesized that EOCRC in H/L patients harbors ancestry-associated alterations and spatial expression patterns that influence treatment response and outcomes.
Methods:
We conducted a retrospective, observational multi-cohort study of 4,874 CRC patients (early- and late-onset, multi-ethnic) using institutional and public datasets. Data included spatial proteomics (Phenocycler 2.0), spatial transcriptomics (10x Genomics Visium HD), RNA sequencing, whole-exome sequencing (WES), and microbiome (16S/shotgun) profiling. Clinical records and SDoH variables were integrated to evaluate ancestry-specific associations with different therapies (FOLFOX, FOLFIRI, anti-EGFR, anti-VEGF, immunotherapy). Standard pipelines were applied, followed by Artificial Intelligence-enabled integration using conversational agents for cross-domain exploration of molecular and clinical features.
Results:
WES revealed ancestry-linked molecular profiles distinguishing EOCRC from late-onset CRC, particularly in H/L patients. Integrated WES/RNAseq analyses confirmed ancestry-specific oncogenic pathway alterations. Spatial proteomics and transcriptomics demonstrated marked compartmental heterogeneity, with EOCRC tumors displaying distinct pathway activation in epithelial, immune, and stromal niches. Multi-omics and microbiome profiling highlighted enrichment of pro-tumorigenic taxa and differential pathway activity in EOCRC. Incorporation of clinical and SDoH variables revealed ancestry-associated differences in treatment response, with preliminary analyses showing distinct overall survival patterns between EOCRC and late-onset CRC under chemotherapy, targeted therapy, and immunotherapy.
Conclusions:
AI-enabled molecular characterization of EOCRC reveals ancestry-associated alterations, spatial heterogeneity, and treatment-linked disparities, particularly in H/L populations. Integrating spatial, multi-omic, microbiome, clinical, and SDoH data establishes a comprehensive framework for precision oncology in EOCRC. These findings emphasize the importance of ancestry and SDoH in biomarker discovery and treatment optimization, advancing precision medicine for young patients.
Nature · 2025
Guotian Li、Linna An、Wanneng Yang 等
Crops provide food, clothing and other important products for the global population. To meet the demands of a growing population, substantial improvements are required in crop yield, quality and production sustainability. However, these goals are constrained by various environmental factors and limited genetic resources. Overcoming these limitations requires a paradigm shift in crop improvement by fully leveraging natural genetic diversity alongside biotechnological approaches such as genome editing and the heterologous expression of designed proteins, coupled with multimodal data integration. In this Review, we provide an in-depth analysis of integrated uses of omics technologies, genome editing, protein design and high-throughput phenotyping, in crop improvement, supported by artificial intelligence-enabled tools. We discuss the emerging applications and current challenges of these technologies in crop improvement. Finally, we present a perspective on how elite alleles generated through these technologies can be incorporated into the genomes of existing and de novo domesticated crops, aided by a proposed artificial intelligence model. We suggest that integrating these technologies with agricultural practices will lead to a new revolution in crop improvement, contributing to global food security in a sustainable manner.
JMIR Human Factors · 2021
Michael Knop、S. Weber、Marius Mueller 等
Background The digitization and automation of diagnostics and treatments promise to alter the quality of health care and improve patient outcomes, whereas the undersupply of medical personnel, high workload on medical professionals, and medical case complexity increase. Clinical decision support systems (CDSSs) have been proven to help medical professionals in their everyday work through their ability to process vast amounts of patient information. However, comprehensive adoption is partially disrupted by specific technological and personal characteristics. With the rise of artificial intelligence (AI), CDSSs have become an adaptive technology with human-like capabilities and are able to learn and change their characteristics over time. However, research has not reflected on the characteristics and factors essential for effective collaboration between human actors and AI-enabled CDSSs. Objective Our study aims to summarize the factors influencing effective collaboration between medical professionals and AI-enabled CDSSs. These factors are essential for medical professionals, management, and technology designers to reflect on the adoption, implementation, and development of an AI-enabled CDSS. Methods We conducted a literature review including 3 different meta-databases, screening over 1000 articles and including 101 articles for full-text assessment. Of the 101 articles, 7 (6.9%) met our inclusion criteria and were analyzed for our synthesis. Results We identified the technological characteristics and human factors that appear to have an essential effect on the collaboration of medical professionals and AI-enabled CDSSs in accordance with our research objective, namely, training data quality, performance, explainability, adaptability, medical expertise, technological expertise, personality, cognitive biases, and trust. Comparing our results with those from research on non-AI CDSSs, some characteristics and factors retain their importance, whereas others gain or lose relevance owing to the uniqueness of human-AI interactions. However, only a few (1/7, 14%) studies have mentioned the theoretical foundations and patient outcomes related to AI-enabled CDSSs. Conclusions Our study provides a comprehensive overview of the relevant characteristics and factors that influence the interaction and collaboration between medical professionals and AI-enabled CDSSs. Rather limited theoretical foundations currently hinder the possibility of creating adequate concepts and models to explain and predict the interrelations between these characteristics and factors. For an appropriate evaluation of the human-AI collaboration, patient outcomes and the role of patients in the decision-making process should be considered.
The journal of allergy and clinical immunology. In practice · 2024
Arnaud Bourdin、Guy Brusselle、Simon Couillard 等
Severe asthma is associated with significant morbidity and mortality despite the maximal use of inhaled corticosteroids and additional controller medications, and has a high economic burden. Biologic therapies are recommended for the management of severe, uncontrolled asthma to help to prevent exacerbations and to improve symptoms and health-related quality of life. The effective management of severe asthma requires consideration of clinical heterogeneity that is driven by varying clinical and inflammatory phenotypes, which are reflective of distinct underlying disease mechanisms. Phenotyping patients using a combination of clinical characteristics such as the age of onset or comorbidities and biomarker profiles, including blood eosinophil counts and levels of fractional exhaled nitric oxide and serum total immunoglobulin E, is important for the differential diagnosis of asthma. In addition, phenotyping is beneficial for risk assessment, selection of treatment, and monitoring of the treatment response in patients with asthma. This review describes the clinical and inflammatory phenotypes of asthma, provides an overview of biomarkers routinely used in clinical practice and those that have recently been explored for phenotyping, and aims to assess the value of phenotyping in severe asthma management in the current era of biologics.
Journal of the American College of Cardiology · 2024
Márton Tokodi、Attila Kovács
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