# Artificial Intelligence in Oncology
## 2 Intelligent Oncology and Clinical Practice  
AI plays a special role in oncology. The growing role of AI in oncology is best illustrated by the publication by [PERSON] et al., who performed a bibliometric analysis of publications on AI in prostate cancer in the years 1987-2022 [20]. The authors found that, in the last 7 years of the analyzed period (2015-2022), there was a sharp increase in publications on this topic, especially in the United States, Canada and the United Kingdom. [PERSON] et al. introduced a new term \"intelligent oncology\" to paraphrase the concept of \"artificial intelligence\", replacing the word \"intelligence\" with \"oncology\" to describe the interdisciplinary fusion of different medical disciplines with AI [21]. This holistic and structured concept is the fusion of AI with oncology to help create a chain of oncology care, including cancer prevention, screening, early diagnosis, and treatment [21]. [PERSON] and [PERSON] identified three areas where AI may be particularly useful in oncology [22]. These are early diagnosis of cancer, therapy, and prediction, also covering three domains: cancer incidence, recurrence and survival. However, there are also some other areas where AI is of special importance, namely cancer screening and clinical trials. The largest application of AI in oncology is in diagnostics (more than 80%), particularly radiology and pathology. AI algorithms can process and interpret complex imaging data from X-rays, CT scans, and MRIs much more quickly and more accurately than humans. These algorithms can detect subtle patterns and anomalies that might be missed by the human eye, leading to early and accurate diagnosis [23]. Generative Adversarial Networks (GANs), containing two interoperating artificial neural networks, a generator and a discriminator, enable the creation of new radiological images, detecting changes previously overlooked by radiologists [24, 25]. In addition, GANs improve the education of radiologists and can be used in scientific research. A novelty in the use of AI in oncology is the use of the term metaverse, or \"medical technology and AI\" (MeTAI). In this method, radiological scans of a patient are first simulated using virtual machines to obtain the best result before the actual scan is analyzed. Based on the result, the actual scan is performed [26, 27, 28]. The role of AI in the early detection of cancer is most commonly employed in breast cancer, lung cancer and colorectal cancer. [PERSON] et al. drew attention to the key role of AI in the early radiological detection of cancers, especially breast cancer, lung cancer, lymph node metastasis, and colorectal cancer detection [29]. The most widely developed AI technologies address the five key cancers: breast, lung, prostate, pancreas and cervix [30]. Cancer diagnosis using AI focuses mainly on gene characterisation of tumours and tumour imaging techniques [31]. Based on AI algorithms, it is possible to determine how often a patient should be screened for breast cancer to avoid its development in the next 5 years. One of the AI models used in breast cancer oncology is Mirai [32]. This model was validated in seven hospitals across five countries [33]. The Mirai model is more modern and effective than the two earlier models developed by [PERSON] and colleagues and [PERSON] and co-workers [34; 35; 36]. The Mirai was better at predicting the development of breast cancer [32]. In lung cancer diagnosis, AI allows the detection of precancerous lesions that mimic cancer from actual cancerous lesions. This makes it possible to reduce the number of false positives, thereby reducing the costs of further diagnostics and stress for patients. Sybil is a model used in prediction of the risk of lung cancer when low-dose CT scans are made [37]. So far, the results of lung cancer screening are disappointing. In the United States, for example, less than 10% of the eligible population is screened [38; 39; 40]. The use of the Sybil model makes it possible to assess lung cancer risk based on only one CT scan.