The increase in cancer cases, the democratization of healthcare, even the recent pandemic, are some of the numerous reasons pointing out that there is a great need in leveraging AI technology in patients’ care practice. However, while AI has demonstrated the capability to be a valuable companion to practitioners, with respect to meeting accuracy levels, removing bias, and increasing diagnostic throughput, its adoption to clinical practice are still slow.
While the problem is more complex, in this issue, we will focus on two critical aspects of adoption, namely explainability and multimodal data integration, as these impact the understanding of the decision process of medical data analysis, and in particular, in cancer patients care. Integrating different modalities in an interpretable way enhances cancer understanding and paves the way for personalized patient care.
Recognized experts from both the AI development and the clinical domain are welcome to share and review the scientific progress and discuss challenges in adopting AI in clinical practice and the impact of the focus technologies in closing the gap.
In this Research Topic we welcome submissions on the following aspects:
• Innovative technological solution in the areas of either explainability and or multimodal cancer patient data integration.
• Meta-analyses of evaluating and/or leveraging AI technologies in clinical practice.
• Perspectives on the value of explainability in adopting AI technologies in clinical practice.
• Perspectives on the value of integrating multimodal cancer patient data in better understanding the disease progression and/or attaining personalized patient treatment.
Topic Editor J.L.R is the Director and shares capital holder of AEMEC. All other Topic Editors declare no competing interests with regards to the Research Topic subject.
The increase in cancer cases, the democratization of healthcare, even the recent pandemic, are some of the numerous reasons pointing out that there is a great need in leveraging AI technology in patients’ care practice. However, while AI has demonstrated the capability to be a valuable companion to practitioners, with respect to meeting accuracy levels, removing bias, and increasing diagnostic throughput, its adoption to clinical practice are still slow.
While the problem is more complex, in this issue, we will focus on two critical aspects of adoption, namely explainability and multimodal data integration, as these impact the understanding of the decision process of medical data analysis, and in particular, in cancer patients care. Integrating different modalities in an interpretable way enhances cancer understanding and paves the way for personalized patient care.
Recognized experts from both the AI development and the clinical domain are welcome to share and review the scientific progress and discuss challenges in adopting AI in clinical practice and the impact of the focus technologies in closing the gap.
In this Research Topic we welcome submissions on the following aspects:
• Innovative technological solution in the areas of either explainability and or multimodal cancer patient data integration.
• Meta-analyses of evaluating and/or leveraging AI technologies in clinical practice.
• Perspectives on the value of explainability in adopting AI technologies in clinical practice.
• Perspectives on the value of integrating multimodal cancer patient data in better understanding the disease progression and/or attaining personalized patient treatment.
Topic Editor J.L.R is the Director and shares capital holder of AEMEC. All other Topic Editors declare no competing interests with regards to the Research Topic subject.
