Call for papers - AI in oncology: personalized treatment and predictive modeling

Guest Editors

Okyaz Eminaga, MD, PhD, AI VOBIS, Stanford University, USA
Fredrik Strand, MD, PhD, MSc(Eng), Karolinska Institutet, Karolinska University Hospital, Sweden

Submission Status: Open | Submission Deadline: 19 September 2025

BMC Artificial Intelligence is calling for submissions to our Collection, AI in oncology: personalized treatment and predictive modeling. This Collection will gather research on the application of AI in oncology, focusing on personalized treatment strategies and predictive modeling. We invite researchers to contribute their work on AI-powered diagnosis, precision medicine, predictive modeling, and other AI applications in oncology, with the aim of advancing our understanding and improving patient outcomes in the field of cancer care.

New Content Item This Collection supports and amplifies research related to SDG 3: Good Health & Well-Being and SDG 9: Industry, Innovation & Infrastructure

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Meet the Guest Editors

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Okyaz Eminaga, MD, PhD, AI VOBIS, Stanford University, USA

Okyaz Eminaga is a distinguished physician-scientist and innovator in the fields of medical artificial intelligence, biomedical informatics, and digital health. With over 14 years of research experience and more than 100 scientific contributions, he has received multiple prestigious awards, including recognition for his work in medical AI. Okyaz has held various roles at Stanford University, including visiting scholar, postdoctoral fellow, and research instructor, and currently serves as a project manager and business specialist. His expertise spans developing AI-driven solutions for healthcare, including digital pathology, clinical workflow optimization, and AI-driven 6P oncology. As founder of AI VOBIS, R&D factory for medical AI and Digital Health, Dr Eminaga leverages his medical background and informatics expertise to drive healthcare innovation. He is passionate about bridging clinical needs with cutting-edge technology and has collaborated with cross-functional teams to streamline IT infrastructure, enhance operational efficiency, and develop transformative AI tools.

Fredrik Strand, MD, PhD, MSc(Eng), Karolinska Institutet, Karolinska University Hospital, Sweden

Fredrik Strand is a Swedish breast radiologist and associate professor at Karolinska Institutet, Stockholm. He holds MD, PhD, and MSc(Eng) degrees. Fredrik is head of the research and education committee at the Swedish society of breast imaging. His academic research evolves around exploring AI for cancer imaging. He and his team are involved in developing new AI models, performing reader studies, evaluating external models, and supporting hospitals in implementing AI. The primary focus is on mammography images, but increasingly both MRI and ultrasound images are being incorporated. He is the principal investigator in two clinical trials using AI in the screening workflow: ScreenTrustCAD and ScreenTrustMRI. Fredrik is also leading the multicenter VAI-B, the Validation platform for AI in Breast imaging in which mammograms are collected from several hospitals with the purpose of validating existing AI systems for breast cancer detection or other use cases. The long-term aim is to leverage AI on multi-modal images to improve outcome for women with breast cancer through earliest detection and optimal treatment.

About the Collection

BMC Artificial Intelligence is calling for submissions to our Collection, AI in oncology: personalized treatment and predictive modeling.

The integration of artificial intelligence (AI) in oncology has revolutionized the landscape of cancer diagnosis, treatment, and prognosis. AI-powered predictive modeling and personalized treatment approaches have shown great promise in improving patient outcomes and optimizing healthcare resources. From AI-powered diagnosis to deep learning-based predictive modeling, the application of AI in oncology has the potential to transform the way we understand and manage malignant diseases.

Recent advances have demonstrated the efficacy of AI in identifying biomarkers, enabling precision medicine, and enhancing cancer screening and early detection. Moreover, AI-driven predictive modeling has facilitated more accurate prognostic assessments, leading to tailored treatment strategies and improved patient care.

Looking ahead, continued research in AI in oncology holds the promise of further refining personalized treatment approaches, enhancing the accuracy of predictive models, and expanding the application of AI in cancer research and clinical practice. Future advances may include the development of AI algorithms that can integrate diverse data sources to provide comprehensive insights into cancer biology, progression, and response to treatment.

We invite contributions that examine a wide range of topics relating to the application of AI in oncology, including but not limited to:

AI-powered diagnosis in oncology
AI algorithms for understanding cancer biology and progression
Integration of diverse data sources for comprehensive insights
AI applications in cancer screening and early detection
Precision medicine and biomarker identification
Predictive modeling for cancer prognosis
Personalized treatment approaches

All manuscripts submitted to this journal, including those submitted to collections and special issues, are assessed in line with our editorial policies and the journal’s peer review process. Reviewers and editors are required to declare competing interests and can be excluded from the peer review process if a competing interest exists.

Please email Alison Cuff, the editor for BMC Artificial Intelligence, (alison.cuff@biomedcentral.com) if you would like more information before you submit.

This Collection supports and amplifies research related to SDG 3: Good Health & Well-Being and SDG 9: Industry, Innovation & Infrastructure

There are currently no articles in this collection.

Submission Guidelines

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This Collection welcomes submission of original Research Articles. Should you wish to submit a different article type, please read our submission guidelines to confirm that type is accepted by the journal. Articles for this Collection should be submitted via our submission system, Snapp. During the submission process you will be asked whether you are submitting to a Collection, please select "AI in oncology: personalized treatment and predictive modeling" from the dropdown menu.

All manuscripts submitted to this journal, including those submitted to collections and special issues, are assessed in line with our editorial policies and the journal’s peer review process. Reviewers and editors are required to declare competing interests and can be excluded from the peer review process if a competing interest exists.