Artificial intelligence is revolutionizing cancer research with advanced tools

Significant advances have been made in understanding the molecular and cellular mechanisms of tumor progression, but challenges remain. Traditional imaging techniques such as MRI, CT, and mammography are limited by the need for professional curation, which is time-consuming. Cancer-associated genetic alterations can serve as diagnostic, prognostic, and predictive biomarkers, but their translation into clinical practice is hampered by genetic alterations. metastasistreatment responses and resistance.

While new therapeutic strategies are effective, they face problems arising from the heterogeneity of cancer. Artificial intelligence (AI) provides solutions to these challenges with extensive applications in drug development, cancer prediction, diagnosis, and analysis of next-generation sequencing data. Artificial intelligence algorithms can identify genetic mutations or signatures for early cancer detection and targeted therapies. However, developing and implementing accurate AI models in clinical settings is challenging due to data heterogeneity, biases, and privacy concerns. Despite these, artificial intelligence has improved clinical decision-making.

Artificial intelligence, a collection of methods and techniques, has become increasingly important in cancer research; In this review, various AI methods were discussed in detail, including their advantages and limitations. The review provides an overview of the use of these methods over the past decade, as well as guidelines for incorporating AI models into clinical settings and the potential of pre-trained language models in personalizing cancer care strategies. AI methods discussed include machine learning (ML), which encompasses unsupervised and supervised learning. Supervised learning, which includes regression and classification, is widely used in cancer research. Traditional machine learning models, such as Bayesian networks, support vector machines, and random forests, continually combine data to produce results. Deep learning, a subset of ML, uses multiple hidden layers to identify complex patterns in data. Another artificial intelligence algorithm, natural language processing (NLP), targets narrative texts to extract useful information for decision-making.

AI models in cancer research use multiple omics and clinical information from a variety of sources; classification is the most common task. These models are validated and evaluated using receiver operating characteristic analysis, which calculates area under the curve (AUC), sensitivity, specificity, and precision. Artificial intelligence methods have been developed to process large volumes of data, which requires more cloud computing and storage power. The review also discusses the application of AI in drug development, where models predict drug responses using multi-omics data. Additionally, artificial intelligence has been used to extract information from electronic health records and eliminate the challenge of analyzing complex data.

Despite progress, there are limitations in applying artificial intelligence in cancer research. Choosing the appropriate algorithm is complex and depends on the data type and complexity. Integrating AI into clinical environments requires detailed application descriptions and transparency of algorithms. Monitoring the quality of AI tools is crucial for strong performance. The review highlights the need for greater transparency and guidance on software review, cost-effectiveness, retraining of datasets and the conditions required to use AI systems.

In conclusion, AI has significantly impacted cancer research, and addressing the challenges and validating AI-generated results could lead to the future of oncology research. The review highlights the advancement of AI methods in cancer-related applications and the potential of non-invasive AI tools for explainable AI, personalized medicine, and early cancer detection. As AI continues to evolve, it has great potential to revolutionize cancer detection and improve patient outcomes.