Artificial intelligence (AI) is being used in cancer research to identify patterns and relationships in large volumes of data, including images, that cannot be perceived by the human brain. This technology is being used to assist clinicians in screening asymptomatic patients at risk of cancer, investigating and triaging symptomatic patients, and more effectively diagnosing cancer recurrence. AI-based systems can help pathologists diagnose cancer more accurately and consistently, reducing case error rates. Predictive AI models can also estimate the likelihood of a person developing cancer by identifying risk factors. Big data, combined with AI, can enable medical experts to develop customized treatments for cancer patients.
In addition to improving diagnosis accuracy, AI is also being used in drug discovery and development for cancer therapy. Machine learning algorithms are being used to evaluate vast volumes of data for precise and effective diagnosis, making AI a potentially helpful tool for cancer diagnosis. AI is also being used in precision medicine to develop customized treatments for cancer patients based on their individual characteristics and genetic makeup. However, there are also challenges and limitations to the use of AI in cancer research and diagnosis, including the need for large, high-quality datasets and the potential for bias in the algorithms. Despite these challenges, the potential applications of AI in cancer research and diagnosis are vast, and the technology is rapidly reshaping the field of oncology.