Critical Appraisal and Future Challenges of Artificial Intelligence and Anticancer Drug Development.
Emmanuel ChamoreyJocelyn GalBaharia MograbiGérard MilanoPublished in: Pharmaceuticals (Basel, Switzerland) (2024)
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.
Keyphrases
- artificial intelligence
- machine learning
- big data
- clinical trial
- deep learning
- end stage renal disease
- data analysis
- chronic kidney disease
- newly diagnosed
- molecular docking
- healthcare
- public health
- peritoneal dialysis
- prognostic factors
- emergency department
- mental health
- patient reported outcomes
- case report
- drug induced