Cancer is one of the defining health challenges of the twenty-first century. According to the Global Cancer Observatory (GLOBOCAN) 2022 estimates, there were 19.98 million new cancer cases and 9.74 million cancer deaths worldwide, and approximately one in five people will develop cancer during their lifetime, while around one in nine men and one in 12 women die from it[
1]. Lung cancer remained the most commonly diagnosed cancer and the leading cause of cancer death globally, followed by female breast, colorectal, prostate, stomach, and liver cancers in different combinations of incidence and mortality[
1]. If current rates persist, lung cancer alone could reach 4.62 million new cases and 3.55 million deaths by 2050[
2]. More broadly, the cancer burden is not only enormous but also deeply unequal. By 2050, global cancer incidence is projected to rise to about 35.3 million cases and deaths to 18.5 million, with countries with a low Human Development Index expected to experience nearly a tripling of both cases and deaths[
3]. Population ageing is a major driver of this trend: in 2020, 64% of all new cancers and 71.3% of cancer deaths occurred in adults aged 60 years or older, and these numbers are expected to rise substantially over the next two decades[
4]. China, which carries one of the heaviest national cancer burdens, had an estimated 4.82 million new cases and 2.57 million deaths in 2022; lung, colorectal, thyroid, liver, and stomach cancers were the most common, whereas lung, liver, stomach, colorectal, and esophageal cancers were the leading causes of cancer death[
5]. Beyond the human toll, cancers are projected to cost the global economy 25.2 trillion international dollars between 2020 and 2050[
6]. These figures make the challenge unmistakable: better cancer care will depend not only on new therapies, but also on faster detection, more precise diagnosis, smarter risk stratification, and more equitable clinical decision-making.
At the same time, oncology is becoming one of the most data-intensive areas in medicine. Artificial intelligence (AI) in oncology is moving beyond pure algorithm development toward clinical integration, with applications spanning detection, diagnosis, prognosis, and treatment across imaging, genomics, pathology, and clinical records[
7]. Radiomics can support outcome prediction, treatment-response assessment, and non-invasive characterization of tumor biology from routine images[
8]. Multimodal AI can combine radiology, histology, genomics, and electronic health records to build more robust models and uncover new biomarkers or therapeutic targets[
9]. Large language models and multimodal foundation models are further expanding the possibilities for precision oncology[
10]. But the field also needs a reality check: what matters now is not simply whether a model performs well in a retrospective dataset, but whether it is interpretable, validated across populations, equitable, safe, and useful in real clinical workflows[
11–
13]. This is exactly where
Intelligent Clinical Oncology aims to contribute. In essence, the journal is interested in basic, translational, and clinical studies that use AI, machine learning, deep learning, data science, robotics, smart devices, cancer big data, clinical decision-support systems, knowledge graphs, wearable and mobile technologies, augmented reality and virtual reality, telehealth, and related intelligent tools to solve real problems in cancer screening, diagnosis, pathology, radiation oncology, drug discovery, treatment selection, response assessment, and prognostic evaluation. We welcome original research, including randomized controlled trials, observational studies, and diagnostic accuracy studies, as well as systematic reviews and meta-analyses, review articles, case reports, study protocols, communications, rapid reports, correspondence, and letters to the editor; invited contributions include editorials, concept articles, spotlights, perspectives, mini-reviews, narrative medicine, commentaries, and opinions. Put simply, if a study helps clinicians find cancer earlier, classify it more precisely, choose therapy more rationally, monitor patients more intelligently, or move an algorithm from the computer screen to meaningful patient benefit, it belongs in
Intelligent Clinical Oncology. We hope this journal will become a meeting place for clinicians, scientists, and technologists who share the same goal: turning intelligent innovation into better cancer care.
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