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Artificial intelligence (AI) and machine learning (ML) are revolutionizing cancer immunotherapy by addressing the complex interplay between cancer and the immune system. This chapter explores how AI technologies enhance immunotherapy development across multiple domains: antibody design, response prediction, biomarker identification, and T-cell target discovery. In therapeutic antibody design, AI improves efficiency through predictive modeling of antibody-antigen interactions, structure prediction tools, generative models that create novel antibody sequences, and developability optimization. Clinical applications include AI-powered systems that predict immunotherapy responses using multi-omics data integration, helping distinguish pseudoprogression from true disease progression. Beyond conventional biomarkers like programmed cell death protein 1, AI enables identification of additional markers including tumor mutational burden, microsatellite instability, immune cell infiltration patterns, and novel genomic alterations. Multi-omics approaches leverage AI to synthesize diverse data types, uncovering complex biomarker signatures that more accurately predict treatment outcomes. For T-cell target identification, next-generation immunoediting platforms like Gritstone's EDGE™ system exemplify AI-powered approaches that precisely identify neoantigens by integrating sequencing technologies with sophisticated prediction algorithms (Table 2.1). These platforms support both personalized and shared antigen approaches to immunotherapy, potentially enhanced through integration with innate immune pathways. Despite remarkable progress, challenges persist in addressing tumor heterogeneity, immune evasion mechanisms, and technical limitations in prediction algorithms. The continued refinement of AI approaches, expansion to diverse cancer types, and integration with complementary therapeutic modalities represent promising future directions. Overall, AI and ML are poised to transform cancer immunotherapy by enabling more precise, effective, and personalized treatment approaches that harness the immune system's power against cancer.
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http://dx.doi.org/10.1007/978-3-031-97242-3_2 | DOI Listing |
JMIR Hum Factors
September 2025
Seidenberg School of Computer Science and Information Systems, Pace University, New York City, NY, United States.
Background: As information and communication technologies and artificial intelligence (AI) become deeply integrated into daily life, the focus on users' digital well-being has grown across academic and industrial fields. However, fragmented perspectives and approaches to digital well-being in AI-powered systems hinder a holistic understanding, leaving researchers and practitioners struggling to design truly human-centered AI systems.
Objective: This paper aims to address the fragmentation by synthesizing diverse perspectives and approaches to digital well-being through a systematic literature review.
J Med Microbiol
September 2025
Alberta Precision Laboratories Public Health Lab, Edmonton, Alberta, Canada.
For thousands of years, parasitic infections have represented a constant challenge to human health. Despite constant progress in science and medicine, the challenge has remained mostly unchanged over the years, partly due to the vast complexity of the host-parasite-environment relationships. Over the last century, our approaches to these challenges have evolved through considerable advances in science and technology, offering new and better solutions.
View Article and Find Full Text PDFInt J Surg
September 2025
Department of Oral and Maxillofacial Surgery, The Affiliated Tai'an City Central Hospital of Qingdao University, Taian, China.
J Robot Surg
September 2025
Department of CSE, United Institute of Technology, Coimbatore, India.
Diabetologia
September 2025
Department of Diabetology and Internal Medicine, Medical University of Warsaw, Warsaw, Poland.
This review article, developed by the EASD Global Council, addresses the growing global challenges in diabetes research and care, highlighting the rising prevalence of diabetes, the increasing complexity of its management and the need for a coordinated international response. With regard to research, disparities in funding and infrastructure between high-income countries and low- and middle-income countries (LMICs) are discussed. The under-representation of LMIC populations in clinical trials, challenges in conducting large-scale research projects, and the ethical and legal complexities of artificial intelligence integration are also considered as specific issues.
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