ArticleHealth science reports2026
Advancing Tumor Treatment Through Artificial Intelligence and Mathematical Modeling: A Comprehensive Review.
Article in Health science reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and Aims: Solid tumors emerge from uncontrolled cell division and interactions with neighboring cells that influence their growth and resistance to therapy. Anti-cancer drugs are developed to disrupt these processes, but they often damage healthy cells, making treatment difficult to manage safely and effectively. As the global cancer burden continues to rise, there is an urgent need for more precise and personalized approaches to detection and therapy. The study's goal is to analyze recent breakthroughs in artificial intelligence (AI), hybrid frameworks, and mathematical modeling in tumor diagnosis and treatment, examine the challenges of data-driven methodologies, and highlight emerging trends with future recommendations. Method: In recent years, computational methods like AI and mathematical modeling (MM) have emerged as powerful tools to address these challenges. AI techniques, including machine learning and deep learning, are increasingly used to improve early diagnosis, guide surgical planning, and predict treatment outcomes. Meanwhile, mathematical models offer valuable insights into tumor growth patterns and help optimize therapeutic strategies by simulating how tumors respond to different interventions. Results: AI and MM enhance cancer detection, prediction, and treatment through precise and optimized hybrid techniques. However, challenges such as inadequate data, poor interpretability, and generalization persist, necessitating better explainable and robust models for clinical use. Conclusion: This review explores the current applications of AI and MM in the field of neurosurgical oncology, emphasizing both their promising potential and the key limitations. It concludes by discussing future directions aimed at developing safer, more transparent, and individualized cancer care solutions that can ultimately improve patient outcomes.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.