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Reimagining cancer treatments in the era of generative AI
1Ted Rogers School of Information Technology Management, Toronto Metropolitan University, Toronto, ON, Canada.
Abstract:
Significant advances in the treatment of cancer have been achieved as reflected by the ever-expanding space of cancer therapeutics being available to cancer patients. Often, however, it is not clear which patient would respond to which drug and what combination of drugs will improve patient outcomes. Furthermore, while many of these drugs are initially effective, therapeutic resistance is often inevitable due to the evolving nature of cancer. Generative artificial intelligence (GenAI) powered by the increasingly large amount of accumulating clinical, molecular, and radiomics data about cancer patients and their treatments may serve as the kernel of rapid learning decision-support systems that could enable personalized cancer treatments to counter therapeutic resistance and overcome the shortcomings of the current standard of care. This perspective is explored in the context of current advances of AI applications in oncology and the potential of GenAI learning and inferencing capabilities to support patient-tailored dynamic cancer treatments. A discussion of this vision is elaborated with respect to issues pertinent to GenAI use in real-world clinical settings, including clinical validation, data curation, and sharing, large language model hallucinations as well as ethical concerns and considerations such as privacy, bias, transparency, and accountability.
Insights
Generative artificial intelligence (GenAI) can personalize cancer treatments by analyzing patient data to predict drug response and overcome resistance. This technology offers a path toward improved cancer care and decision support.
Area of Science:
- Oncology
- Artificial Intelligence
- Computational Biology
Background:
- Cancer therapeutics have advanced significantly, yet patient response and drug resistance remain challenges.
- Current treatment selection often lacks personalization, leading to suboptimal outcomes.
- The evolving nature of cancer frequently results in inevitable therapeutic resistance.
Purpose of the Study:
- To explore the potential of Generative Artificial Intelligence (GenAI) in personalizing cancer treatments.
- To investigate how GenAI can address challenges in current cancer care, including therapeutic resistance.
- To discuss the integration of GenAI into clinical settings for dynamic, patient-tailored cancer therapies.
Main Methods:
- Review of current AI applications in oncology.
- Exploration of GenAI's learning and inferencing capabilities using clinical, molecular, and radiomics data.
- Discussion of GenAI's role in developing rapid learning decision-support systems.
Main Results:
- GenAI holds promise for creating personalized cancer treatment strategies.
- GenAI can potentially overcome therapeutic resistance and improve patient outcomes.
- GenAI can support dynamic treatment adjustments based on evolving patient data.
Conclusions:
- GenAI can serve as a core component of advanced decision-support systems for personalized oncology.
- Addressing challenges like data curation, clinical validation, and ethical considerations is crucial for real-world GenAI implementation.
- GenAI offers a transformative potential for the future of cancer treatment and patient care.
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