AI technologies for immunotherapy optimization face data quality and safety concerns hindering routine clinical adoption

Artificial intelligence (AI) is a transformative technology that has captivated the medical world with its potential to optimize cancer treatment and enhance precision oncology. In cancer diagnosis and treatment, various AI technologies have already provided high-level data examination and analytics that preceding innovations were not capable of. Cancer immunotherapy is a treatment that seeks to boost the immune system to recognize and eradicate tumors. It is a field that is constantly evolving, serving as a fertile environment where AI technologies can accelerate discovery and personalize its regimens. In recent years, AI has played an increased role in the optimization of immunotherapy delivery and drug development. Traditional machine learning and its subfield of deep learning algorithms have already impacted response prediction and related tasks, such as patient stratification for immune checkpoint blockade treatment and identifying potent T-cells in the laboratory to develop effective cellular therapies. Additionally, recently developed technologies such as generative AI (gen AI) and foundation models have expanded upon traditional AI algorithms with new applications such as treatment plan generation and adverse event prediction. As innovations such as agentic AI and the model context protocol (MCP) become increasingly available, efficiency and success in immunotherapy development and delivery could further improve. That said, some challenges must be overcome for AI to reach its full potential in immunotherapy. These include concerns related to data quality control, patient safety, and addressing ethical dilemmas. In this article, we briefly review available state-of-the-art AI technologies for immunotherapy and highlight their capabilities. Then, we examine the current AI applications in immunotherapy including cell therapies, checkpoint inhibitors, and cancer vaccines, covering a diverse array of technologies over a wide range of applications. We analyze the datasets used, performance metrics, and downstream tasks, and highlight existing limitations. Subsequently, we discuss some of the obstacles that have prevented AI from routine clinical adoption. Finally, we envision the future of AI in immunotherapy that may include a framework involving an orchestration of multiple specialized AI agents with a human in the loop.