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The Rise of Generalist Foundation Models and Quantum Computing in Oncology
1Department of Radiation Biology, Institute for Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway.
Technology in Cancer Research & Treatment
|August 6, 2026
Summary
Generalist Medical AI (GMAI) and Quantum Oncology (QO) offer a new framework to overcome limitations of traditional AI in cancer care. This convergence promises enhanced data processing, drug discovery, and precision oncology, while addressing ethical concerns.
Area of Science:
- Oncology
- Artificial Intelligence
- Quantum Computing
Background:
- Traditional Artificial Intelligence (AI) and Deep Learning (DL) in oncology face limitations including task-specificity, high data needs, and interpretability issues.
- Lack of clinical criteria and physician involvement in Explainable AI (XAI) development hinder trust, ethical integration, and GDPR compliance.
- Current AI/DL tools are constrained by task-specificity, hyperparameter tuning, and large data requirements, limiting clinical application.
Purpose of the Study:
- To propose a framework linking clinical demands with Generalist Medical AI (GMAI) and Quantum Oncology (QO).
- To discuss the potential of Quantum Computing (QC) to enhance GMAI for oncology applications like data processing, imaging, drug discovery, and genomics.
- To address structural/technical trade-offs and provide recommendations for safe clinical translation of AI in oncology.
Main Methods:
- Review of existing literature on AI, DL, GMAI, and Quantum Computing in oncology.
- Analysis of the convergence of GMAI and Quantum Computing for advanced oncology tasks.
- Discussion of frameworks like CLAIM and FUTURE-AI for ethical and safe clinical implementation.
Main Results:
- GMAI, utilizing foundation models and self-supervised learning, can address diverse clinical tasks.
- Quantum Computing's unique properties (superposition, entanglement) can significantly improve AI efficiency in medical imaging, drug discovery, and genomic analysis.
- The convergence into Quantum Oncology (QO) offers a path towards scalable, sustainable, and ethical precision oncology.
Conclusions:
- The integration of GMAI and QC, forming Quantum Oncology, presents a paradigm shift for cancer care.
- This approach can overcome limitations of classical AI, enabling more efficient and effective oncology solutions.
- Safe bedside translation requires clinician involvement and adherence to ethical and privacy guidelines.
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