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Verlingue L, Boyer C, Olgiati L, Brutti Mairesse C, Morel D, Blay JY. Artificial intelligence in oncology: ensuring safe and effective integration of language models in clinical practice. THE LANCET REGIONAL HEALTH. EUROPE 2024; 46:101064. [PMID: 39290808 PMCID: PMC11406067 DOI: 10.1016/j.lanepe.2024.101064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Revised: 08/07/2024] [Accepted: 08/26/2024] [Indexed: 09/19/2024]
Abstract
In this Personal View, we address the latest advancements in automatic text analysis with artificial intelligence (AI) in medicine, with a focus on its implications in aiding treatment decisions in medical oncology. Acknowledging that a majority of hospital medical content is embedded in narrative format, natural language processing has become one of the most dynamic research fields for developing clinical decision support tools. In addition, large language models have recently reached unprecedented performance, notably when answering medical questions. Emerging applications include prognosis estimation, treatment recommendations, multidisciplinary tumor board recommendations and matching patients to recruiting clinical trials. Altogether, we advocate for a forward-looking approach in which the community efficiently initiates global prospective clinical evaluations of promising AI-based decision support systems. Such assessments will be essential to validate and evaluate potential biases, ensuring these innovations can be effectively and safely translated into practical tools for oncological practice. We are at a pivotal moment, where continued advancements in patient care must be pursued with scientific rigor.
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Affiliation(s)
- Loïc Verlingue
- Centre Léon Bérard, Centre de Recherche en Cancérologie de Lyon, France
- INSERM U1030, Molecular Radiotherapy, Villejuif, France
| | - Clara Boyer
- Centre Léon Bérard, Centre de Recherche en Cancérologie de Lyon, France
| | - Louise Olgiati
- Centre Léon Bérard, Centre de Recherche en Cancérologie de Lyon, France
| | | | - Daphné Morel
- INSERM U1030, Molecular Radiotherapy, Villejuif, France
- Department of Radiation Oncology, Gustave Roussy, Villejuif, France
| | - Jean-Yves Blay
- Centre Léon Bérard, Centre de Recherche en Cancérologie de Lyon, France
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2
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Kolla L, Parikh RB. Uses and limitations of artificial intelligence for oncology. Cancer 2024; 130:2101-2107. [PMID: 38554271 PMCID: PMC11170282 DOI: 10.1002/cncr.35307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 02/19/2024] [Accepted: 03/15/2024] [Indexed: 04/01/2024]
Abstract
Modern artificial intelligence (AI) tools built on high-dimensional patient data are reshaping oncology care, helping to improve goal-concordant care, decrease cancer mortality rates, and increase workflow efficiency and scope of care. However, data-related concerns and human biases that seep into algorithms during development and post-deployment phases affect performance in real-world settings, limiting the utility and safety of AI technology in oncology clinics. To this end, the authors review the current potential and limitations of predictive AI for cancer diagnosis and prognostication as well as of generative AI, specifically modern chatbots, which interfaces with patients and clinicians. They conclude the review with a discussion on ongoing challenges and regulatory opportunities in the field.
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Affiliation(s)
- Likhitha Kolla
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ravi B. Parikh
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
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3
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Bryant AK, Zamora‐Resendiz R, Dai X, Morrow D, Lin Y, Jungles KM, Rae JM, Tate A, Pearson AN, Jiang R, Fritsche L, Lawrence TS, Zou W, Schipper M, Ramnath N, Yoo S, Crivelli S, Green MD. Artificial intelligence to unlock real-world evidence in clinical oncology: A primer on recent advances. Cancer Med 2024; 13:e7253. [PMID: 38899720 PMCID: PMC11187737 DOI: 10.1002/cam4.7253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 02/05/2024] [Accepted: 04/28/2024] [Indexed: 06/21/2024] Open
Abstract
PURPOSE Real world evidence is crucial to understanding the diffusion of new oncologic therapies, monitoring cancer outcomes, and detecting unexpected toxicities. In practice, real world evidence is challenging to collect rapidly and comprehensively, often requiring expensive and time-consuming manual case-finding and annotation of clinical text. In this Review, we summarise recent developments in the use of artificial intelligence to collect and analyze real world evidence in oncology. METHODS We performed a narrative review of the major current trends and recent literature in artificial intelligence applications in oncology. RESULTS Artificial intelligence (AI) approaches are increasingly used to efficiently phenotype patients and tumors at large scale. These tools also may provide novel biological insights and improve risk prediction through multimodal integration of radiographic, pathological, and genomic datasets. Custom language processing pipelines and large language models hold great promise for clinical prediction and phenotyping. CONCLUSIONS Despite rapid advances, continued progress in computation, generalizability, interpretability, and reliability as well as prospective validation are needed to integrate AI approaches into routine clinical care and real-time monitoring of novel therapies.
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Affiliation(s)
- Alex K. Bryant
- Department of Radiation OncologyUniversity of Michigan School of MedicineAnn ArborMichiganUSA
- Department of Radiation Oncology, Veterans Affairs Ann Arbor Healthcare SystemAnn ArborMichiganUSA
| | - Rafael Zamora‐Resendiz
- Applied Mathematics and Computational Research Division, Lawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA
| | - Xin Dai
- Computational Science Initiative, Brookhaven National LaboratoryUptonNew YorkUSA
| | - Destinee Morrow
- Applied Mathematics and Computational Research Division, Lawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA
| | - Yuewei Lin
- Computational Science Initiative, Brookhaven National LaboratoryUptonNew YorkUSA
| | - Kassidy M. Jungles
- Department of PharmacologyUniversity of Michigan School of MedicineAnn ArborMichiganUSA
| | - James M. Rae
- Department of PharmacologyUniversity of Michigan School of MedicineAnn ArborMichiganUSA
- Department of Internal MedicineUniversity of Michigan School of MedicineAnn ArborMichiganUSA
| | - Akshay Tate
- Department of Radiation OncologyUniversity of Michigan School of MedicineAnn ArborMichiganUSA
| | - Ashley N. Pearson
- Department of Radiation OncologyUniversity of Michigan School of MedicineAnn ArborMichiganUSA
| | - Ralph Jiang
- Department of Radiation OncologyUniversity of Michigan School of MedicineAnn ArborMichiganUSA
- Department of StatisticsUniversity of MichiganAnn ArborMichiganUSA
| | - Lars Fritsche
- Department of StatisticsUniversity of MichiganAnn ArborMichiganUSA
| | - Theodore S. Lawrence
- Department of Radiation OncologyUniversity of Michigan School of MedicineAnn ArborMichiganUSA
| | - Weiping Zou
- Department of StatisticsUniversity of MichiganAnn ArborMichiganUSA
- Center of Excellence for Cancer Immunology and ImmunotherapyUniversity of Michigan Rogel Cancer CenterAnn ArborMichiganUSA
- Department of PathologyUniversity of MichiganAnn ArborMichiganUSA
- Graduate Program in ImmunologyUniversity of MichiganAnn ArborMichiganUSA
| | - Matthew Schipper
- Department of Radiation OncologyUniversity of Michigan School of MedicineAnn ArborMichiganUSA
- Department of PharmacologyUniversity of Michigan School of MedicineAnn ArborMichiganUSA
| | - Nithya Ramnath
- Division of Hematology Oncology, Department of MedicineUniversity of Michigan School of MedicineAnn ArborMichiganUSA
- Division of Hematology Oncology, Department of MedicineVeterans Affairs Ann Arbor Healthcare SystemAnn ArborMichiganUSA
| | - Shinjae Yoo
- Computational Science Initiative, Brookhaven National LaboratoryUptonNew YorkUSA
| | - Silvia Crivelli
- Applied Mathematics and Computational Research Division, Lawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA
| | - Michael D. Green
- Department of Radiation OncologyUniversity of Michigan School of MedicineAnn ArborMichiganUSA
- Department of Radiation Oncology, Veterans Affairs Ann Arbor Healthcare SystemAnn ArborMichiganUSA
- Graduate Program in ImmunologyUniversity of MichiganAnn ArborMichiganUSA
- Graduate Program in Cancer BiologyUniversity of MichiganAnn ArborMichiganUSA
- Department of Microbiology and ImmunologyUniversity of Michigan School of MedicineAnn ArborMichiganUSA
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Lotter W, Hassett MJ, Schultz N, Kehl KL, Van Allen EM, Cerami E. Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions. Cancer Discov 2024; 14:711-726. [PMID: 38597966 PMCID: PMC11131133 DOI: 10.1158/2159-8290.cd-23-1199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 01/29/2024] [Accepted: 02/28/2024] [Indexed: 04/11/2024]
Abstract
Artificial intelligence (AI) in oncology is advancing beyond algorithm development to integration into clinical practice. This review describes the current state of the field, with a specific focus on clinical integration. AI applications are structured according to cancer type and clinical domain, focusing on the four most common cancers and tasks of detection, diagnosis, and treatment. These applications encompass various data modalities, including imaging, genomics, and medical records. We conclude with a summary of existing challenges, evolving solutions, and potential future directions for the field. SIGNIFICANCE AI is increasingly being applied to all aspects of oncology, where several applications are maturing beyond research and development to direct clinical integration. This review summarizes the current state of the field through the lens of clinical translation along the clinical care continuum. Emerging areas are also highlighted, along with common challenges, evolving solutions, and potential future directions for the field.
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Affiliation(s)
- William Lotter
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Pathology, Brigham and Women’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Michael J. Hassett
- Harvard Medical School, Boston, MA, USA
- Division of Population Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Nikolaus Schultz
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center; New York, NY, USA
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Kenneth L. Kehl
- Harvard Medical School, Boston, MA, USA
- Division of Population Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Eliezer M. Van Allen
- Harvard Medical School, Boston, MA, USA
- Division of Population Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Cancer Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ethan Cerami
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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Corbaux P, Bayle A, Besle S, Vinceneux A, Vanacker H, Ouali K, Hanvic B, Baldini C, Cassier PA, Terret C, Verlingue L. Patients' selection and trial matching in early-phase oncology clinical trials. Crit Rev Oncol Hematol 2024; 196:104307. [PMID: 38401694 DOI: 10.1016/j.critrevonc.2024.104307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 02/19/2024] [Accepted: 02/20/2024] [Indexed: 02/26/2024] Open
Abstract
BACKGROUND Early-phase clinical trials (EPCT) represent an important part of innovations in medical oncology and a valuable therapeutic option for patients with metastatic cancers, particularly in the era of precision medicine. Nevertheless, adult patients' participation in oncology clinical trials is low, ranging from 2% to 8% worldwide, with unequal access, and up to 40% risk of early discontinuation in EPCT, mostly due to cancer-related complications. DESIGN We review the tools and initiatives to increase patients' orientation and access to early phase cancer clinical trials, and to limit early discontinuation. RESULTS New approaches to optimize the early-phase clinical trial referring process in oncology include automatic trial matching, tools to facilitate the estimation of patients' prognostic and/or to better predict patients' eligibility to clinical trials. Classical and innovative approaches should be associated to double patient recruitment, improve clinical trial enrollment experience and reduce early discontinuation rates. CONCLUSIONS Whereas EPCT are essential for patients to access the latest medical innovations in oncology, offering the appropriate trial when it is relevant for patients should increase by organizational and technological innovations. The oncologic community will need to closely monitor their performance, portability and simplicity for implementation in daily clinical practice.
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Affiliation(s)
- P Corbaux
- Medical Oncology Department, Centre Léon Bérard, Lyon, France; Medical Oncology, Institut de Cancérologie et d'Hématologie Universitaire de Saint-Étienne (ICHUSE), Centre Hospitalier Universitaire de Saint-Etienne, France
| | - A Bayle
- Drug Development Department (DITEP), Gustave Roussy, Université Paris-Saclay, Villejuif F-94805, France
| | - S Besle
- Centre de Recherche en Cancérologie de Lyon (CRCL), France
| | - A Vinceneux
- Medical Oncology Department, Centre Léon Bérard, Lyon, France
| | - H Vanacker
- Medical Oncology Department, Centre Léon Bérard, Lyon, France; Centre de Recherche en Cancérologie de Lyon (CRCL), France
| | - K Ouali
- Drug Development Department (DITEP), Gustave Roussy, Université Paris-Saclay, Villejuif F-94805, France
| | - B Hanvic
- Medical Oncology Department, Centre Léon Bérard, Lyon, France
| | - C Baldini
- Drug Development Department (DITEP), Gustave Roussy, Université Paris-Saclay, Villejuif F-94805, France
| | - P A Cassier
- Medical Oncology Department, Centre Léon Bérard, Lyon, France; Centre de Recherche en Cancérologie de Lyon (CRCL), France
| | - C Terret
- Medical Oncology Department, Centre Léon Bérard, Lyon, France
| | - L Verlingue
- Medical Oncology Department, Centre Léon Bérard, Lyon, France; Centre de Recherche en Cancérologie de Lyon (CRCL), France.
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Cil T, Boileau JF, Chia S, DeCoteau MJ, Jerzak KJ, Koch A, Nixon N, Quan ML, Roberts A, Brezden-Masley C. The Canadian Breast Cancer Symposium 2023 Meeting Report. Curr Oncol 2024; 31:1774-1802. [PMID: 38668038 PMCID: PMC11049169 DOI: 10.3390/curroncol31040135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 03/01/2024] [Accepted: 03/08/2024] [Indexed: 04/28/2024] Open
Abstract
On 15-16 June 2023, healthcare professionals and breast cancer patients and advocates from across Canada met in Toronto, Ontario, for the 2023 Canadian Breast Cancer Symposium (CBSC.). The CBSC. is a national, multidisciplinary event that occurs every 2 years with the goal of developing a personalized approach to the management of breast cancer in Canada. Experts provided state-of-the-art information to help optimally manage breast cancer patients, including etiology, prevention, diagnosis, experimental biology, and therapy of breast cancer and premalignant breast disease. The symposium also had the objectives of increasing communication and collaboration among breast cancer healthcare providers nationwide and providing a comprehensive and real-life review of the many facets of breast cancer. The sessions covered the patient voice, the top breast cancer papers from different disciplines in 2022, artificial intelligence in breast cancer, systemic therapy updates, the management of central nervous system metastases, multidisciplinary management of ductal carcinoma in situ, special populations, optimization-based individual prognostic factors, toxicity management of novel therapeutics, survivorship, and updates in surgical oncology. The key takeaways of these sessions have been summarized in this conference report.
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Affiliation(s)
- Tulin Cil
- Princess Margaret Cancer Centre, Toronto, ON M5G 2M9, Canada; (T.C.); (A.K.)
| | | | - Stephen Chia
- British Columbia Cancer Centre, University of British Columbia, Vancouver, BC V5Z 4E6, Canada;
| | - MJ DeCoteau
- Rethink Breast Cancer, Toronto, ON M4M 3G3, Canada;
| | - Katarzyna J. Jerzak
- Sunnybrook Odette Cancer Centre, University of Toronto, Toronto, ON M4N 3M5, Canada; (K.J.J.); (A.R.)
| | - Anne Koch
- Princess Margaret Cancer Centre, Toronto, ON M5G 2M9, Canada; (T.C.); (A.K.)
| | - Nancy Nixon
- Department of Surgery and Oncology, University of Calgary, Calgary, AB T2N 4Z6, Canada; (N.N.); (M.L.Q.)
| | - May Lynn Quan
- Department of Surgery and Oncology, University of Calgary, Calgary, AB T2N 4Z6, Canada; (N.N.); (M.L.Q.)
| | - Amanda Roberts
- Sunnybrook Odette Cancer Centre, University of Toronto, Toronto, ON M4N 3M5, Canada; (K.J.J.); (A.R.)
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7
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Ryan DK, Maclean RH, Balston A, Scourfield A, Shah AD, Ross J. Artificial intelligence and machine learning for clinical pharmacology. Br J Clin Pharmacol 2024; 90:629-639. [PMID: 37845024 DOI: 10.1111/bcp.15930] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 10/04/2023] [Accepted: 10/06/2023] [Indexed: 10/18/2023] Open
Abstract
Artificial intelligence (AI) will impact many aspects of clinical pharmacology, including drug discovery and development, clinical trials, personalized medicine, pharmacogenomics, pharmacovigilance and clinical toxicology. The rapid progress of AI in healthcare means clinical pharmacologists should have an understanding of AI and its implementation in clinical practice. As with any new therapy or health technology, it is imperative that AI tools are subject to robust and stringent evaluation to ensure that they enhance clinical practice in a safe and equitable manner. This review serves as an introduction to AI for the clinical pharmacologist, highlighting current applications, aspects of model development and issues surrounding evaluation and deployment. The aim of this article is to empower clinical pharmacologists to embrace and lead on the safe and effective use of AI within healthcare.
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Affiliation(s)
- David K Ryan
- Department of Clinical Pharmacology, University College London Hospitals NHS Foundation Trust, London, UK
| | - Rory H Maclean
- Department of Clinical Pharmacology, University College London Hospitals NHS Foundation Trust, London, UK
- Institute of Health Informatics, University College London, London, UK
| | - Alfred Balston
- Department of Clinical Pharmacology, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Andrew Scourfield
- Department of Clinical Pharmacology, University College London Hospitals NHS Foundation Trust, London, UK
| | - Anoop D Shah
- Department of Clinical Pharmacology, University College London Hospitals NHS Foundation Trust, London, UK
- Institute of Health Informatics, University College London, London, UK
- National Institute for Health Research, University College London Hospitals Biomedical Research Centre, London, UK
| | - Jack Ross
- Department of Clinical Pharmacology, University College London Hospitals NHS Foundation Trust, London, UK
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8
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DeClerck YA. Envision the future of precision medicine in pediatric cancer. Cancer Cell 2024; 42:177-179. [PMID: 38350420 DOI: 10.1016/j.ccell.2024.01.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 01/17/2024] [Accepted: 01/17/2024] [Indexed: 02/15/2024]
Abstract
Exploring the diversity within the tumor microenvironment (TME) can offer crucial insights to steer cancer therapy toward precision medicine. In this issue of Cancer Cell, Wienke et al. undertake a comprehensive single-cell analysis of neuroblastoma, unveiling its immune landscape and identifying NECTIN2-TIGIT as a promising target for immunotherapy.
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Affiliation(s)
- Y A DeClerck
- Cancer and Blood Diseases Institute, Department of Pediatrics, Children's Hospital Los Angeles, Los Angeles, CA 90027, USA; University of Southern California, Los Angeles, CA 90027, USA.
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9
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Bailleux C, Gal J, Chamorey E, Mograbi B, Milano G. Artificial Intelligence and Anticancer Drug Development-Keep a Cool Head. Pharmaceutics 2024; 16:211. [PMID: 38399265 PMCID: PMC10893490 DOI: 10.3390/pharmaceutics16020211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Revised: 01/22/2024] [Accepted: 01/26/2024] [Indexed: 02/25/2024] Open
Abstract
Artificial intelligence (AI) is progressively spreading through the world of health, particularly in the field of oncology. AI offers new, exciting perspectives in drug development as toxicity and efficacy can be predicted from computer-designed active molecular structures. AI-based in silico clinical trials are still at their inception in oncology but their wider use is eagerly awaited as they should markedly reduce durations and costs. Health authorities cannot neglect this new paradigm in drug development and should take the requisite measures to include AI as a new pillar in conducting clinical research in oncology.
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Affiliation(s)
- Caroline Bailleux
- Centre Antoine Lacassagne, Oncology Departement Unit, University Côte d’Azur, 06000 Nice, France;
| | - Jocelyn Gal
- Centre Antoine Lacassagne, Epidemiology and Biostatistics Department, University Côte d’Azur, 06000 Nice, France; (J.G.); (E.C.)
| | - Emmanuel Chamorey
- Centre Antoine Lacassagne, Epidemiology and Biostatistics Department, University Côte d’Azur, 06000 Nice, France; (J.G.); (E.C.)
| | | | - Gérard Milano
- Centre Antoine Lacassagne, University Côte d’Azur, 33 Avenue de Valombrose, 06189 Nice, France
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Deng J, Heybati K. RE: Use of artificial intelligence for cancer clinical trial enrollment. J Natl Cancer Inst 2024; 116:170-171. [PMID: 37934140 DOI: 10.1093/jnci/djad228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Accepted: 10/17/2023] [Indexed: 11/08/2023] Open
Affiliation(s)
- Jiawen Deng
- Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Kiyan Heybati
- Mayo Clinic Alix School of Medicine (Jacksonville), Mayo Clinic, Jacksonville, FL, USA
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Zadeh Shirazi A, Tofighi M, Gharavi A, Gomez GA. The Application of Artificial Intelligence to Cancer Research: A Comprehensive Guide. Technol Cancer Res Treat 2024; 23:15330338241250324. [PMID: 38775067 PMCID: PMC11113055 DOI: 10.1177/15330338241250324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Revised: 03/28/2024] [Accepted: 04/08/2024] [Indexed: 05/25/2024] Open
Abstract
Advancements in AI have notably changed cancer research, improving patient care by enhancing detection, survival prediction, and treatment efficacy. This review covers the role of Machine Learning, Soft Computing, and Deep Learning in oncology, explaining key concepts and algorithms (like SVM, Naïve Bayes, and CNN) in a clear, accessible manner. It aims to make AI advancements understandable to a broad audience, focusing on their application in diagnosing, classifying, and predicting various cancer types, thereby underlining AI's potential to better patient outcomes. Moreover, we present a tabular summary of the most significant advances from the literature, offering a time-saving resource for readers to grasp each study's main contributions. The remarkable benefits of AI-powered algorithms in cancer care underscore their potential for advancing cancer research and clinical practice. This review is a valuable resource for researchers and clinicians interested in the transformative implications of AI in cancer care.
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Affiliation(s)
- Amin Zadeh Shirazi
- Centre for Cancer Biology, SA Pathology and the University of South Australia, Adelaide, SA, Australia
| | - Morteza Tofighi
- Department of Electrical Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran
| | - Alireza Gharavi
- Department of Computer Science, Azad University, Mashhad Branch, Mashhad, Iran
| | - Guillermo A. Gomez
- Centre for Cancer Biology, SA Pathology and the University of South Australia, Adelaide, SA, Australia
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