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Critical Appraisal and Future Challenges of Artificial Intelligence and Anticancer Drug Development
Emmanuel Chamorey1, Jocelyn Gal1, Baharia Mograbi2
1Epidemiology and Biostatistics Department, Centre Antoine Lacassagne, University Côte d'Azur, 33 Avenue de Valombrose, 06189 Nice, France.
Abstract:
The conventional rules for anti-cancer drug development are no longer sufficient given the relatively limited number of patients available for therapeutic trials. It is thus a real challenge to better design trials in the context of new drug approval for anti-cancer treatment. Artificial intelligence (AI)-based in silico trials can incorporate far fewer but more informative patients and could be conducted faster and at a lower cost. AI can be integrated into in silico clinical trials to improve data analysis, modeling and simulation, personalized medicine approaches, trial design optimization, and virtual patient generation. Health authorities are encouraged to thoroughly review the rules for setting up clinical trials, incorporating AI and in silico methodology once they have been appropriately validated. This article also aims to highlight the limits and challenges related to AI and machine learning.
Insights
Artificial intelligence (AI) offers a solution to challenges in anti-cancer drug development by enabling faster, more cost-effective in silico trials. These AI-driven trials use fewer, more informative patients, improving trial design and drug approval processes.
Area of Science:
- Oncology
- Computational Biology
- Clinical Trial Design
Background:
- Conventional anti-cancer drug development faces limitations due to small patient populations for clinical trials.
- Designing effective trials for new anti-cancer drug approval is a significant challenge.
Purpose of the Study:
- To explore the potential of artificial intelligence (AI)-based in silico trials in revolutionizing anti-cancer drug development.
- To highlight the benefits and challenges of integrating AI into clinical trial methodologies.
Main Methods:
- AI integration into in silico clinical trials for enhanced data analysis and modeling.
- Utilizing AI for personalized medicine, trial design optimization, and virtual patient generation.
Main Results:
- AI-powered in silico trials can be conducted faster and at a lower cost.
- These trials can incorporate fewer but more informative patients, improving efficiency.
Conclusions:
- AI and in silico methodologies offer a promising approach to overcome current limitations in anti-cancer drug development.
- Health authorities should consider validating and incorporating AI into clinical trial regulations.
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