# Artificial Intelligence in Oncology
## 2 Intelligent Oncology and Clinical Practice
However, the Sybil model still requires further validation work, as it is based on data from 2002-2004 obtained from predominantly Caucasian patients (92%). It is also important to take into account the technological changes in CT that have occurred since 2002, e.g., the use of scans that are now thicker than 2.5 mm. In addition. AI accurately predicted some parameters of clinical response to treatment of non-small cell lung cancer with PD-1 inhibitors [41]. The effectiveness of AI in the diagnosis of colorectal cancer was studied at different stages: blood testing, colonoscopy and examination of samples taken during colonoscopy. [PERSON] and colleagues have developed an AI model that allows radiologists to detect potentially aggressive prostate cancer using a new type of MR, multiparametric MRI. Reading scans from this type of MRI, however, require long-term learning by radiologists [42]. AI plays an important role in the detection of pancreatic cancer. This topic was addressed at the conference in 2020 entitled _AI and Early Detection of Pancreatic Cancer Virtual Summit_, organized by the Kenner Family Research Fund and the American Pancreatic Association [43]. There are many aspects to the early detection of pancreatic cancer with the help of AI. The use of AI in the early detection of pancreatic cancer includes patient diagnostic tests, radiological, laboratory and demographic data [44; 45; 46; 47; 48; 49; 50]. These procedures are then subjected to an integrative analysis, risk assessment for pancreatic cancer and diverse data to train the system. There are two ways to acquire data to train AI: centralisation and federation [44]. The centralised databases come from multiple sources and are stored in central repositories while. in the second model, data is stored in local repositories and then transferred to the central system. AI makes it possible to detect precancerous lesions of the cervix that can be removed or treated. In the search for precancerous lesions of cervical cancer with the visual inspection method, AI achieved better results than those obtained by specialists [51]. Thanks to artificial intelligence methods, it is possible to discover new biomarkers in oncology. This is especially relevant in cancer immunotherapy. However, despite an encouraging number of studies indicating the significant importance of AI in the search for predictive biomarkers, none of these studies has so far provided high-level evidence to enable their rapid implementation in clinical practice [52]. AI can help oncologists to predict the possible development of the disease. Early cancer detection includes computer aided screening, sensor based detection, molecular biomarker detection and self-diagnosis application. In turn, in cancer therapy, AI helps in drug discovery and repurposing, assisted surgery, precision medicine, clinical decision support systems and cell programming. In cancer therapy, the key issue is knowledge of the dynamics of a given disease, the possibility of its recurrence, or potential sites of dissemination [53]. AI enables doctors to plan the appropriate timing of restaging, and to select the appropriate doses of oncological drugs or radiotherapy. This is particularly evident in glioblastoma, a cancer with very poor prognosis. AI allows the use of high doses of chemotherapy and radiotherapy and reduces the risk of side effects. AI helps to avoid overtreatment and unnecessary surgeries. AI makes it possible to distinguish pre-cancerous from non-cancerous lesions. AI is also instrumental in supporting the selection of the optimal therapeutic pathway. It can analyse a patient's medical history, genetic information, and health status to recommend the most effective treatment plan. This personalised approach increases the chances of successful treatment and reduces the risk of adverse side effects.