Artificial Intelligence: Unveiling New Opportunities in Cancer Research

C.E. Credits: P.A.C.E. CE Florida CE


Artificial Intelligence (AI) is a computer program designed to perform operations typical of human intelligence, such as self-learning. AI is taking over different fields in science; we already see it everywhere, including clinical research and precision medicine, driving exponential advances in healthcare. At every stage of clinical research, researchers can rely on AI for developing proposals, recruiting healthy subjects or patients, disseminating results, and discovering new drugs. The global healthcare-focused AI market continues to grow exponentially, reaching an estimated value of $5 billion in 2020 over the last ten years, and is expected to continue to rise to an estimated $44.5 billion in 2026. AI has helped the discovery of new therapies for the treatment and timely detection of difficult-to-treat diseases such as cancer. The application of AI in cancer research can be variable; for example, when specific symptoms are entered into an AI system, it can help in disease prognosis and suggest possible treatment options. Another way AI can help cancer research is by recruiting and selecting subjects or participants in a study. AI speeds up research in drug discovery and increases precision at every step; having drugs faster that are not rejected in the phase 4 clinical trial is crucial for cancer research. Implementing AI in cancer research can be slow and laborious as regulatory hurdles can interfere with integrating AI tools. Privacy issues and ethical dilemmas could decelerate AI-based advancements in cancer research. To bridge this gap, a Laboratory Information Management System (LIMS) plays a critical role. A cloud-based LIMS minimizes challenges by anonymizing sensitive patient data, storing informed consent of participants, and assigning role-based access to users, thereby making implementation of AI in clinical research easy.

Learning Objectives:
1. Get an overview of the applications of Artificial Intelligence (AI) in clinical and cancer research.
2. Describe the challenges in implementing AI in cancer research. 
3. Discuss how a LIMS increases the possibility of applying AI to cancer research with ease.

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