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Luu VP, Fiorini M, Combes S, Quemeneur E, Bonneville M, Bousquet PJ. Challenges of artificial intelligence in precision oncology: public-private partnerships including national health agencies as an asset to make it happen. Ann Oncol 2024; 35:154-158. [PMID: 37769849 DOI: 10.1016/j.annonc.2023.09.3106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 07/13/2023] [Accepted: 09/17/2023] [Indexed: 10/03/2023] Open
Affiliation(s)
- V P Luu
- Epidemiology and innovation Unit, Artificial Intelligence and Cancers Association, Paris, France.
| | - M Fiorini
- Artificial Intelligence and Cancers Association, Paris, France
| | | | - E Quemeneur
- France Biotech, Paris, France; Transgene S.A., Illkirch-Graffenstaden, France
| | - M Bonneville
- Alliance pour la Recherche et l'Innovation des Industries de Santé, Paris, France; Institut Mérieux, Lyon, France
| | - P J Bousquet
- Health Survey, Data-Science, Assessment Division, Institut National du Cancer, Boulogne Billancourt, France; Aix Marseille University, INSERM, IRD, Economics and Social Sciences Applied to Health & Analysis of Medical Information (SESSTIM), Marseille, France
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Brown OR, Hullender DA. Darwinian evolution has become dogma; AI can rescue what is salvageable. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2024; 186:53-56. [PMID: 38145808 DOI: 10.1016/j.pbiomolbio.2023.12.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 09/06/2023] [Accepted: 12/22/2023] [Indexed: 12/27/2023]
Abstract
Artificial Intelligence (AI), as an academic discipline, is traceable to the mid-1950s but it is currently exploding in applications with successes and concerns. AI can be defined as intelligence demonstrated by computers, with intelligence difficult to define but it must include concepts of ability to learn, reason, and generalize from a vast amount of information and, we propose, to infer meaning. The type of AI known as general AI, has strong, but unrealized potential both for assessing and also for solving major problems with the scientific theory of Darwinian evolution, including its modern variants and for origin of life studies. Specifically, AI should be applied first to evaluate the strengths and weaknesses of the assumptions and empirical information underpinning theories of the origin of life and probability of its evolution. AI should then be applied to assess the scientific validity of the theory of how abundant life came to be on earth.
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Affiliation(s)
- Olen R Brown
- Emeritus of Biomedical Sciences, at the University of Missouri, Columbia, MO, USA.
| | - David A Hullender
- Mechanical and Aerospace Engineering at the University of Texas at Arlington, USA
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Gazerani P. Intelligent Digital Twins for Personalized Migraine Care. J Pers Med 2023; 13:1255. [PMID: 37623505 PMCID: PMC10455577 DOI: 10.3390/jpm13081255] [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: 07/21/2023] [Revised: 08/04/2023] [Accepted: 08/12/2023] [Indexed: 08/26/2023] Open
Abstract
Intelligent digital twins closely resemble their real-life counterparts. In health and medical care, they enable the real-time monitoring of patients, whereby large amounts of data can be collected to produce actionable information. These powerful tools are constructed with the aid of artificial intelligence, machine learning, and deep learning; the Internet of Things; and cloud computing to collect a diverse range of digital data (e.g., from digital patient journals, wearable sensors, and digitized monitoring equipment or processes), which can provide information on the health conditions and therapeutic responses of their physical twins. Intelligent digital twins can enable data-driven clinical decision making and advance the realization of personalized care. Migraines are a highly prevalent and complex neurological disorder affecting people of all ages, genders, and geographical locations. It is ranked among the top disabling diseases, with substantial negative personal and societal impacts, but the current treatment strategies are suboptimal. Personalized care for migraines has been suggested to optimize their treatment. The implementation of intelligent digital twins for migraine care can theoretically be beneficial in supporting patient-centric care management. It is also expected that the implementation of intelligent digital twins will reduce costs in the long run and enhance treatment effectiveness. This study briefly reviews the concept of digital twins and the available literature on digital twins for health disorders such as neurological diseases. Based on these, the potential construction and utility of digital twins for migraines will then be presented. The potential and challenges when implementing intelligent digital twins for the future management of migraines are also discussed.
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Affiliation(s)
- Parisa Gazerani
- Department of Life Sciences and Health, Faculty of Health Sciences, Oslo Metropolitan University, 0130 Oslo, Norway;
- Centre for Intelligent Musculoskeletal Health (CIM), Faculty of Health Sciences, Oslo Metropolitan University, 0130 Oslo, Norway
- Department of Health Science and Technology, Faculty of Medicine, Aalborg University, 9260 Gistrup, Denmark
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Chu Y, Li S, Tang J, Wu H. The potential of the Medical Digital Twin in diabetes management: a review. Front Med (Lausanne) 2023; 10:1178912. [PMID: 37547605 PMCID: PMC10397506 DOI: 10.3389/fmed.2023.1178912] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 07/03/2023] [Indexed: 08/08/2023] Open
Abstract
Diabetes is a chronic prevalent disease that must be managed to improve the patient's quality of life. However, the limited healthcare management resources compared to the large diabetes mellitus (DM) population are an obstacle that needs modern information technology to improve. Digital twin (DT) is a relatively new approach that has emerged as a viable tool in several sectors of healthcare, and there have been some publications on DT in disease management. The systematic summary of the use of DTs and its potential applications in DM is less reported. In this review, we summarized the key techniques of DTs, proposed the potentials of DTs in DM management from different aspects, and discussed the concerns of this novel technique in DM management.
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Sun T, Wang J, Suo M, Liu X, Huang H, Zhang J, Zhang W, Li Z. The Digital Twin: A Potential Solution for the Personalized Diagnosis and Treatment of Musculoskeletal System Diseases. Bioengineering (Basel) 2023; 10:627. [PMID: 37370558 DOI: 10.3390/bioengineering10060627] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2023] [Revised: 05/12/2023] [Accepted: 05/20/2023] [Indexed: 06/29/2023] Open
Abstract
Due to the high prevalence and rates of disability associated with musculoskeletal system diseases, more thorough research into diagnosis, pathogenesis, and treatments is required. One of the key contributors to the emergence of diseases of the musculoskeletal system is thought to be changes in the biomechanics of the human musculoskeletal system. However, there are some defects concerning personal analysis or dynamic responses in current biomechanical research methodologies. Digital twin (DT) was initially an engineering concept that reflected the mirror image of a physical entity. With the application of medical image analysis and artificial intelligence (AI), it entered our lives and showed its potential to be further applied in the medical field. Consequently, we believe that DT can take a step towards personalized healthcare by guiding the design of industrial personalized healthcare systems. In this perspective article, we discuss the limitations of traditional biomechanical methods and the initial exploration of DT in musculoskeletal system diseases. We provide a new opinion that DT could be an effective solution for musculoskeletal system diseases in the future, which will help us analyze the real-time biomechanical properties of the musculoskeletal system and achieve personalized medicine.
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Affiliation(s)
- Tianze Sun
- Department of Orthopedics, First Affiliated Hospital of Dalian Medical University, Dalian 116600, China
- Key Laboratory of Molecular Mechanism for Repair and Remodeling of Orthopedic Diseases, Dalian 116000, China
| | - Jinzuo Wang
- Department of Orthopedics, First Affiliated Hospital of Dalian Medical University, Dalian 116600, China
- Key Laboratory of Molecular Mechanism for Repair and Remodeling of Orthopedic Diseases, Dalian 116000, China
| | - Moran Suo
- Department of Orthopedics, First Affiliated Hospital of Dalian Medical University, Dalian 116600, China
- Key Laboratory of Molecular Mechanism for Repair and Remodeling of Orthopedic Diseases, Dalian 116000, China
| | - Xin Liu
- Department of Orthopedics, First Affiliated Hospital of Dalian Medical University, Dalian 116600, China
- Key Laboratory of Molecular Mechanism for Repair and Remodeling of Orthopedic Diseases, Dalian 116000, China
| | - Huagui Huang
- Department of Orthopedics, First Affiliated Hospital of Dalian Medical University, Dalian 116600, China
- Key Laboratory of Molecular Mechanism for Repair and Remodeling of Orthopedic Diseases, Dalian 116000, China
| | - Jing Zhang
- Department of Orthopedics, First Affiliated Hospital of Dalian Medical University, Dalian 116600, China
- Key Laboratory of Molecular Mechanism for Repair and Remodeling of Orthopedic Diseases, Dalian 116000, China
| | - Wentao Zhang
- Department of Orthopedics, First Affiliated Hospital of Dalian Medical University, Dalian 116600, China
- Key Laboratory of Molecular Mechanism for Repair and Remodeling of Orthopedic Diseases, Dalian 116000, China
| | - Zhonghai Li
- Department of Orthopedics, First Affiliated Hospital of Dalian Medical University, Dalian 116600, China
- Key Laboratory of Molecular Mechanism for Repair and Remodeling of Orthopedic Diseases, Dalian 116000, China
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