About this Research Topic
Artificial intelligence (AI) has started to play an important role in oncologic medicine today, focusing mainly on oncologic diagnosis (by helping in cancer radiology & pathology) and radiation oncology. Many of these AI-associated devices have been approved by the FDA. It has proven to improve diagnostic accuracy and clinical decision-making. Its ability to analyze massive data leading to the personalization of treatment and predicting future models will have a big impact on how we practice oncology in the future. Future developments would include drug discovery with the help of molecular profiling, therapy administration, and follow-up strategies to potentially minimize the adverse effects of unnecessary follow-up imaging. In summary, AI has great potential to revolutionize the field of medical oncology.
Although the use of AI in medical oncology is increasing with time, most of the research is focused on its capability in breast and lung cancer. The focus should also be on collecting data for other adult and all pediatric tumors, for which there is always a lack of a large epidemiological and molecular database. Our goal is to overcome the lack of data availability due to different institutional barriers, which is another bottleneck factor in realizing the full potential of AI in medical oncology. Also, there exists a gap between the current AI research and its use due to various factors, including trust, reliability, and safety. In this issue, we would like to focus on the use of AI in medical oncology, identify potential problems, and how to overcome these barriers in utilizing AI for clinical use.
The aim of this Research Topic is to identify the use of AI in medical oncology and help in clinical decision-making for the patients leading to personalized and improved care. All articles must contribute insights on the use of AI in medical oncology, including any diagnostic, therapeutic, or surveillance algorithms. Articles that are focused on the barriers to the use of AI in medical oncology will also be considered. In addition to adult oncology, preference will also be given to articles focusing on the use of AI in adult and pediatric tumors. Articles can include brief research reports, mini-reviews, reviews, systemic reviews, original research, or case reports.
Although the use of AI in medical oncology is increasing with time, most of the research is focused on its capability in breast and lung cancer. The focus should also be on collecting data for other adult and all pediatric tumors, for which there is always a lack of a large epidemiological and molecular database. Our goal is to overcome the lack of data availability due to different institutional barriers, which is another bottleneck factor in realizing the full potential of AI in medical oncology. Also, there exists a gap between the current AI research and its use due to various factors, including trust, reliability, and safety. In this issue, we would like to focus on the use of AI in medical oncology, identify potential problems, and how to overcome these barriers in utilizing AI for clinical use.
The aim of this Research Topic is to identify the use of AI in medical oncology and help in clinical decision-making for the patients leading to personalized and improved care. All articles must contribute insights on the use of AI in medical oncology, including any diagnostic, therapeutic, or surveillance algorithms. Articles that are focused on the barriers to the use of AI in medical oncology will also be considered. In addition to adult oncology, preference will also be given to articles focusing on the use of AI in adult and pediatric tumors. Articles can include brief research reports, mini-reviews, reviews, systemic reviews, original research, or case reports.
Keywords: Artificial Intelligence, Medical Oncology, Diagnosis, Cancer Radiology, Precision Medicine
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
