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      New regulatory thinking is needed for AI-based personalised drug and cell therapies in precision oncology

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          Abstract

          Until recently the application of artificial intelligence (AI) in precision oncology was confined to activities in drug development and had limited impact on the personalisation of therapy. Now, a number of approaches have been proposed for the personalisation of drug and cell therapies with AI applied to therapy design, planning and delivery at the patient’s bedside. Some drug and cell-based therapies are already tuneable to the individual to optimise efficacy, to reduce toxicity, to adapt the dosing regime, to design combination therapy approaches and, preclinically, even to personalise the receptor design of cell therapies. Developments in AI-based healthcare are accelerating through the adoption of foundation models, and generalist medical AI models have been proposed. The application of these approaches in therapy design is already being explored and realistic short-term advances include the application to the personalised design and delivery of drugs and cell therapies. With this pace of development, the limiting step to adoption will likely be the capacity and appropriateness of regulatory frameworks. This article explores emerging concepts and new ideas for the regulation of AI-enabled personalised cancer therapies in the context of existing and in development governance frameworks.

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          Most cited references94

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          Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models

          We evaluated the performance of a large language model called ChatGPT on the United States Medical Licensing Exam (USMLE), which consists of three exams: Step 1, Step 2CK, and Step 3. ChatGPT performed at or near the passing threshold for all three exams without any specialized training or reinforcement. Additionally, ChatGPT demonstrated a high level of concordance and insight in its explanations. These results suggest that large language models may have the potential to assist with medical education, and potentially, clinical decision-making.
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            CAR-T cell therapy: current limitations and potential strategies

            Chimeric antigen receptor (CAR)-T cell therapy is a revolutionary new pillar in cancer treatment. Although treatment with CAR-T cells has produced remarkable clinical responses with certain subsets of B cell leukemia or lymphoma, many challenges limit the therapeutic efficacy of CAR-T cells in solid tumors and hematological malignancies. Barriers to effective CAR-T cell therapy include severe life-threatening toxicities, modest anti-tumor activity, antigen escape, restricted trafficking, and limited tumor infiltration. In addition, the host and tumor microenvironment interactions with CAR-T cells critically alter CAR-T cell function. Furthermore, a complex workforce is required to develop and implement these treatments. In order to overcome these significant challenges, innovative strategies and approaches to engineer more powerful CAR-T cells with improved anti-tumor activity and decreased toxicity are necessary. In this review, we discuss recent innovations in CAR-T cell engineering to improve clinical efficacy in both hematological malignancy and solid tumors and strategies to overcome limitations of CAR-T cell therapy in both hematological malignancy and solid tumors.
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              Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

              In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in the field of ML, thus achieving outstanding results on several complex cognitive tasks, matching or even beating those provided by human performance. One of the benefits of DL is the ability to learn massive amounts of data. The DL field has grown fast in the last few years and it has been extensively used to successfully address a wide range of traditional applications. More importantly, DL has outperformed well-known ML techniques in many domains, e.g., cybersecurity, natural language processing, bioinformatics, robotics and control, and medical information processing, among many others. Despite it has been contributed several works reviewing the State-of-the-Art on DL, all of them only tackled one aspect of the DL, which leads to an overall lack of knowledge about it. Therefore, in this contribution, we propose using a more holistic approach in order to provide a more suitable starting point from which to develop a full understanding of DL. Specifically, this review attempts to provide a more comprehensive survey of the most important aspects of DL and including those enhancements recently added to the field. In particular, this paper outlines the importance of DL, presents the types of DL techniques and networks. It then presents convolutional neural networks (CNNs) which the most utilized DL network type and describes the development of CNNs architectures together with their main features, e.g., starting with the AlexNet network and closing with the High-Resolution network (HR.Net). Finally, we further present the challenges and suggested solutions to help researchers understand the existing research gaps. It is followed by a list of the major DL applications. Computational tools including FPGA, GPU, and CPU are summarized along with a description of their influence on DL. The paper ends with the evolution matrix, benchmark datasets, and summary and conclusion.
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                Author and article information

                Contributors
                stephen.gilbert@tu-dresden.de
                Journal
                NPJ Precis Oncol
                NPJ Precis Oncol
                NPJ Precision Oncology
                Nature Publishing Group UK (London )
                2397-768X
                30 January 2024
                30 January 2024
                2024
                : 8
                : 23
                Affiliations
                [1 ]ProductLife Group, Paris, France
                [2 ]Groupe de recherche et d’accueil en droit et économie de la santé (GRADES), Faculty of Pharmacy, University Paris-Saclay, ( https://ror.org/03xjwb503) Paris, France
                [3 ]Fraunhofer Institute for Cell Therapy and Immunology, ( https://ror.org/04x45f476) Leipzig, Germany
                [4 ]GRID grid.4488.0, ISNI 0000 0001 2111 7257, Carl Gustav Carus University Hospital Dresden, Dresden University of Technology, ; Dresden, Germany
                [5 ]Department of Emergency Medicine, University Clinic Marburg, Philipps-University, ( https://ror.org/01rdrb571) Marburg, Germany
                [6 ]Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), Dresden/Leipzig, Germany
                [7 ]Institute of Clinical Immunology, University Leipzig, ( https://ror.org/03s7gtk40) Leipzig, Germany
                [8 ]Else Kröner Fresenius Center for Digital Health, TUD Dresden University of Technology, ( https://ror.org/042aqky30) Dresden, Germany
                Author information
                http://orcid.org/0000-0002-3470-6779
                http://orcid.org/0000-0001-9389-4688
                http://orcid.org/0000-0002-4452-4872
                http://orcid.org/0000-0002-8159-9160
                http://orcid.org/0000-0002-1997-1689
                Article
                517
                10.1038/s41698-024-00517-w
                10828509
                38291217
                2057d7ad-733e-4f82-84e3-ebfc6f517ec1
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 18 August 2023
                : 6 January 2024
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100002347, Bundesministerium für Bildung und Forschung (Federal Ministry of Education and Research);
                Award ID: 16KISA100K
                Award Recipient :
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                © Springer Nature Limited 2024

                health policy,drug regulation,stem-cell therapies
                health policy, drug regulation, stem-cell therapies

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