Abstract
Artificial intelligence (AI) is transforming the medical research and clinical workflow by enhancing oncology clinical applications. AI-based tools are emerging as key role players in advancing precision oncology by improving oncology clinical applications in cancer risk prediction, early detection and diagnosis and accurate prognosis. Although there are challenges with every newly developed technology, efforts and significant investments have been placed to ensure the success of this technology. Additionally, the introduction of sophisticated AI-medical devices demonstrates the fundamental role that AI holds to offer in oncology. Several AI-tools have illustrated high performance towards cancer care and management in various parts of the world. While risk prediction, early detection, diagnosis and accurate prognosis are a work in progress in some cancer types, this remains a challenge in various cancers. However, AI-based tools can advance human efforts with the overall aim of improving oncology patient outcome through personalised care. This chapter will focus on AI-based tools in advancing oncology personalised care by improving risk prediction, early detection and diagnosis, and accurate prognosis. Challenges in the application of AI-based tools from bench to bedside will also be discussed, while providing an overview of AI-based tools for predicting clinically relevant parameters in advancing precision oncology.
| Original language | English |
|---|---|
| Title of host publication | Artificial Intelligence and Precision Oncology |
| Subtitle of host publication | Bridging Cancer Research and Clinical Decision Support |
| Publisher | Springer Nature |
| Pages | 293-312 |
| Number of pages | 20 |
| ISBN (Electronic) | 9783031215063 |
| ISBN (Print) | 9783031215056 |
| DOIs | |
| Publication status | Published - 1 Jan 2023 |
| Externally published | Yes |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- Accurate prognosis
- Artificial intelligence
- Clinical applications
- Deep learning (DL)
- Diagnosis
- Early detection
- Precision oncology
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