Sep 2026· WArtificial Intelligence in Cancer· 93 references
Colorectal Cancer Screening and Detection
Abstract
The use of artificial intelligence (AI) models for gastric cancer prevention shows promise beyond traditional diagnostic approaches. Modern digital technologies can be applied to global healthcare. The implementation of deep machine learning has significantly increased the efficiency of computer vision. The introduction of AI is very effective in the field of diagnostic recognition of pathology in endoscopic, radiological and histological images. Robotic surgery has good development prospects. Also, many methods of treating diseases are implemented using AI technologies. Comparative studies of the effectiveness of a human doctor and AI have a high level of evidence. Comparative studies report high levels of performance for AI vs human clinicians in selected tasks, although evidence varies by task and dataset. AI demonstrates higher efficiency than 5-10 highly qualified experts in many areas of medicine. The main areas of medicine actively use AI: Diagnostic recognition of X-ray computed tomographic and magnetic resonance images, endoscopic and histological patterns. The use of AI has begun in the field of targeted treatment. The development of robot-associated surgery continues. There is a good prospect for using AI not only for recognizing X-ray computer images, in endoscopy, histology and targeted treatment, but also for subjective methods of examining patients. For example, the development of AI models for questioning patients by correspondence or conversation between the interlocutor - a doctor and the interlocutor-a patient has begun. The number of assistants (interlocutors, digital agents) can be more than two. The language of communication can also be any.
GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations of such models.
Xiaotian Zhang, Chun-yan Li, Yi Zong et al.· arXiv.org· 216 citations· ⚡17
Empirically, PRISM reduces the end-to-end time for data selection and model tuning to just 30% of conventional pipelines, and achieves this efficiency while simultaneously enhancing performance, surpassing models fine-tuned on the full dataset across eight multimodal and three language understanding benchmarks.
Jinhe Bi, Yifan Wang, Danqi Yan et al.· arXiv.org· 73 citations· ⚡4
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· EUROMICRO Conference on Soft...· 64 citations· ⚡6
This paper designs Markov decision processes (MDPs) for different combinatorial problems and proposes to train conditional GFlowNets to sample from the solution space and demonstrates that GFlowNet policies can efficiently find high-quality solutions.
Dinghuai Zhang, H. Dai, Esmeralda S. Whitammer et al.· Advances in Neural Informati...· 59 citations· ⚡8
An empirical study on the current state of practice in artificial intelligence ethics is conducted by means of a multiple case study of five case companies, which indicates a gap between research and practice in the area.
Ville Vakkuri, Kai-Kristian Kemell, Joni Kultanen et al.· arXiv.org· 56 citations· ⚡6