Integrating AI into Cancer Immunotherapy—A Narrative Review of Current Applications and Future Directions
Background: Cancer remains a leading cause of morbidity and mortality worldwide. Traditional treatments like chemotherapy and radiation often result in significant side effects and varied patient outcomes. Immunotherapy has emerged as a promising alternative, harnessing the immune system to target c...
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| Format: | Article |
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MDPI AG
2025-01-01
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| Series: | Diseases |
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| Online Access: | https://www.mdpi.com/2079-9721/13/1/24 |
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| author | David B. Olawade Aanuoluwapo Clement David-Olawade Temitope Adereni Eghosasere Egbon Jennifer Teke Stergios Boussios |
| author_facet | David B. Olawade Aanuoluwapo Clement David-Olawade Temitope Adereni Eghosasere Egbon Jennifer Teke Stergios Boussios |
| author_sort | David B. Olawade |
| collection | DOAJ |
| description | Background: Cancer remains a leading cause of morbidity and mortality worldwide. Traditional treatments like chemotherapy and radiation often result in significant side effects and varied patient outcomes. Immunotherapy has emerged as a promising alternative, harnessing the immune system to target cancer cells. However, the complexity of immune responses and tumor heterogeneity challenges its effectiveness. Objective: This mini-narrative review explores the role of artificial intelligence [AI] in enhancing the efficacy of cancer immunotherapy, predicting patient responses, and discovering novel therapeutic targets. Methods: A comprehensive review of the literature was conducted, focusing on studies published between 2010 and 2024 that examined the application of AI in cancer immunotherapy. Databases such as PubMed, Google Scholar, and Web of Science were utilized, and articles were selected based on relevance to the topic. Results: AI has significantly contributed to identifying biomarkers that predict immunotherapy efficacy by analyzing genomic, transcriptomic, and proteomic data. It also optimizes combination therapies by predicting the most effective treatment protocols. AI-driven predictive models help assess patient response to immunotherapy, guiding clinical decision-making and minimizing side effects. Additionally, AI facilitates the discovery of novel therapeutic targets, such as neoantigens, enabling the development of personalized immunotherapies. Conclusions: AI holds immense potential in transforming cancer immunotherapy. However, challenges related to data privacy, algorithm transparency, and clinical integration must be addressed. Overcoming these hurdles will likely make AI a central component of future cancer immunotherapy, offering more personalized and effective treatments. |
| format | Article |
| id | doaj-art-1e1e3ecb90de4bc881b899658a12591e |
| institution | Kabale University |
| issn | 2079-9721 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diseases |
| spelling | doaj-art-1e1e3ecb90de4bc881b899658a12591e2025-01-24T13:29:17ZengMDPI AGDiseases2079-97212025-01-011312410.3390/diseases13010024Integrating AI into Cancer Immunotherapy—A Narrative Review of Current Applications and Future DirectionsDavid B. Olawade0Aanuoluwapo Clement David-Olawade1Temitope Adereni2Eghosasere Egbon3Jennifer Teke4Stergios Boussios5Department of Allied and Public Health, School of Health, Sport and Bioscience, University of East London, London E16 2RD, UKEndoscopy Unit, Glenfield Hospital, University Hospitals of Leicester NHS Trust, Leicester LE3 9QP, UKDepartment of Public Health, University of Dundee, Dundee DD1 4HN, UKDepartment of Tissue Engineering and Regenerative Medicine, Faculty of Life Science Engineering, FH Technikum, 1200 Vienna, AustriaDepartment of Research and Innovation, Medway NHS Foundation Trust, Gillingham, Kent ME7 5NY, UKFaculty of Medicine, Health and Social Care, Canterbury Christ Church University, Canterbury, Kent CT1 1QU, UKBackground: Cancer remains a leading cause of morbidity and mortality worldwide. Traditional treatments like chemotherapy and radiation often result in significant side effects and varied patient outcomes. Immunotherapy has emerged as a promising alternative, harnessing the immune system to target cancer cells. However, the complexity of immune responses and tumor heterogeneity challenges its effectiveness. Objective: This mini-narrative review explores the role of artificial intelligence [AI] in enhancing the efficacy of cancer immunotherapy, predicting patient responses, and discovering novel therapeutic targets. Methods: A comprehensive review of the literature was conducted, focusing on studies published between 2010 and 2024 that examined the application of AI in cancer immunotherapy. Databases such as PubMed, Google Scholar, and Web of Science were utilized, and articles were selected based on relevance to the topic. Results: AI has significantly contributed to identifying biomarkers that predict immunotherapy efficacy by analyzing genomic, transcriptomic, and proteomic data. It also optimizes combination therapies by predicting the most effective treatment protocols. AI-driven predictive models help assess patient response to immunotherapy, guiding clinical decision-making and minimizing side effects. Additionally, AI facilitates the discovery of novel therapeutic targets, such as neoantigens, enabling the development of personalized immunotherapies. Conclusions: AI holds immense potential in transforming cancer immunotherapy. However, challenges related to data privacy, algorithm transparency, and clinical integration must be addressed. Overcoming these hurdles will likely make AI a central component of future cancer immunotherapy, offering more personalized and effective treatments.https://www.mdpi.com/2079-9721/13/1/24artificial intelligencecancer immunotherapybiomarkerspredictive modelspersonalized medicine |
| spellingShingle | David B. Olawade Aanuoluwapo Clement David-Olawade Temitope Adereni Eghosasere Egbon Jennifer Teke Stergios Boussios Integrating AI into Cancer Immunotherapy—A Narrative Review of Current Applications and Future Directions Diseases artificial intelligence cancer immunotherapy biomarkers predictive models personalized medicine |
| title | Integrating AI into Cancer Immunotherapy—A Narrative Review of Current Applications and Future Directions |
| title_full | Integrating AI into Cancer Immunotherapy—A Narrative Review of Current Applications and Future Directions |
| title_fullStr | Integrating AI into Cancer Immunotherapy—A Narrative Review of Current Applications and Future Directions |
| title_full_unstemmed | Integrating AI into Cancer Immunotherapy—A Narrative Review of Current Applications and Future Directions |
| title_short | Integrating AI into Cancer Immunotherapy—A Narrative Review of Current Applications and Future Directions |
| title_sort | integrating ai into cancer immunotherapy a narrative review of current applications and future directions |
| topic | artificial intelligence cancer immunotherapy biomarkers predictive models personalized medicine |
| url | https://www.mdpi.com/2079-9721/13/1/24 |
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