About this Research Topic
The integration of artificial intelligence (AI) into healthcare has opened new avenues for precision medicine, especially in the field of cancer diagnosis and prognosis. One of the critical advancements in this domain is the development of Explainable AI (XAI), which offers a significant improvement over traditional "black-box" AI models by providing transparency in decision-making processes. In cancer diagnostics, AI algorithms often process vast amounts of data from medical imaging, genomics, and clinical records to assist healthcare professionals in making informed decisions. However, without transparency, clinicians may struggle to fully trust AI-generated results, hindering its clinical adoption. This is where XAI plays a crucial role.
XAI provides insights into the rationale behind AI-driven predictions, making it easier for clinicians to understand how an AI system arrived at a particular diagnosis or prognosis. This transparency is particularly important in precision cancer diagnosis, where treatment decisions need to be tailored to individual patients based on their unique genetic makeup, tumour characteristics, and medical history. By explaining the reasoning behind predictive models, XAI enhances clinicians' confidence in AI systems, facilitating more accurate and personalized treatment strategies.
The scope of using XAI in cancer diagnosis extends to multiple areas. It can help in interpreting medical images (such as MRI, CT scans, or histopathological images) to detect early signs of cancer. Moreover, XAI can be employed in predictive modelling, where it assesses the risk of cancer recurrence or patient survival rates based on a combination of biological and clinical factors. Importantly, XAI can highlight which specific factors (e.g., genetic mutations, biomarker levels) contributed most to the AI model’s prediction, offering actionable insights for oncologists to design effective therapies. Furthermore, the special issue also explores how harnessing Explainable AI, integrated with VR-based diagnostic tools, can revolutionize precision cancer diagnosis and prognosis by providing immersive, interactive, and intuitive insights for clinicians.
In cancer prognosis, XAI can assist in predicting disease progression and treatment outcomes. By making the AI model’s reasoning visible, healthcare providers can validate and trust the predictions, ultimately leading to more reliable prognostic assessments. This added layer of transparency not only improves the interpretability of AI models but also aligns with the ethical imperative of making AI systems accountable in medical practice.
The following research topic will explore the role of Explainable AI (XAI) in enhancing trust and transparency in breast cancer diagnostics. By clarifying the decision-making processes of AI tools used to analyze mammograms, pathology slides, and genetic data, XAI improves clinicians' understanding and confidence in AI-generated recommendations, ultimately supporting more accurate and personalized patient care.
Please note: Manuscripts consisting solely of bioinformatics, computational analysis, or predictions of public databases which are not accompanied by validation (independent clinical or patient cohort, or biological validation in vitro or in vivo, which are not based on public databases) are not suitable for publication in this journal.
XAI provides insights into the rationale behind AI-driven predictions, making it easier for clinicians to understand how an AI system arrived at a particular diagnosis or prognosis. This transparency is particularly important in precision cancer diagnosis, where treatment decisions need to be tailored to individual patients based on their unique genetic makeup, tumour characteristics, and medical history. By explaining the reasoning behind predictive models, XAI enhances clinicians' confidence in AI systems, facilitating more accurate and personalized treatment strategies.
The scope of using XAI in cancer diagnosis extends to multiple areas. It can help in interpreting medical images (such as MRI, CT scans, or histopathological images) to detect early signs of cancer. Moreover, XAI can be employed in predictive modelling, where it assesses the risk of cancer recurrence or patient survival rates based on a combination of biological and clinical factors. Importantly, XAI can highlight which specific factors (e.g., genetic mutations, biomarker levels) contributed most to the AI model’s prediction, offering actionable insights for oncologists to design effective therapies. Furthermore, the special issue also explores how harnessing Explainable AI, integrated with VR-based diagnostic tools, can revolutionize precision cancer diagnosis and prognosis by providing immersive, interactive, and intuitive insights for clinicians.
In cancer prognosis, XAI can assist in predicting disease progression and treatment outcomes. By making the AI model’s reasoning visible, healthcare providers can validate and trust the predictions, ultimately leading to more reliable prognostic assessments. This added layer of transparency not only improves the interpretability of AI models but also aligns with the ethical imperative of making AI systems accountable in medical practice.
The following research topic will explore the role of Explainable AI (XAI) in enhancing trust and transparency in breast cancer diagnostics. By clarifying the decision-making processes of AI tools used to analyze mammograms, pathology slides, and genetic data, XAI improves clinicians' understanding and confidence in AI-generated recommendations, ultimately supporting more accurate and personalized patient care.
Please note: Manuscripts consisting solely of bioinformatics, computational analysis, or predictions of public databases which are not accompanied by validation (independent clinical or patient cohort, or biological validation in vitro or in vivo, which are not based on public databases) are not suitable for publication in this journal.
Keywords: Explainable AI (XAI), Precision medicine, Cancer diagnosis, AI in healthcare, Medical imaging, Predictive modeling, Cancer prognosis
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