Current Status and Future Directions: The Application of Artificial Intelligence/Machine Learning for Precision Medicine

Abstract

Technological innovations, such as artificial intelligence (AI) and machine learning (ML), have the potential to expedite the goal of precision medicine, especially when combined with increased capacity for voluminous data from multiple sources and expanded therapeutic modalities; however, they also present several challenges. In this communication, we first discuss the goals of precision medicine, and contextualize the use of AI in precision medicine by showcasing innovative applications (e.g., prediction of tumor growth and overall survival, biomarker identification using biomedical images, and identification of patient population for clinical practice) which were presented during the February 2023 virtual public workshop entitled “Application of Artificial Intelligence and Machine Learning for Precision Medicine,” hosted by the US Food and Drug Administration (FDA) and University of Maryland Center of Excellence in Regulatory Science and Innovation (M‐CERSI). Next, we put forward challenges brought about by the multidisciplinary nature of AI, particularly highlighting the need for AI to be trustworthy. To address such challenges, we subsequently note practical approaches, viz., differential privacy, synthetic data generation, and federated learning. The proposed strategies – some of which are highlighted presentations from the workshop – are for the protection of personal information and intellectual property. In addition, methods such as the risk‐based management approach and the need for an agile regulatory ecosystem are discussed. Finally, we lay out a call for action that includes sharing of data and algorithms, development of regulatory guidance documents, and pooling of expertise from a broad‐spectrum of stakeholders to enhance the application of AI in precision medicine.

Machine learning in onco-pharmacogenomics: a path to precision medicine with many challenges

“…Supervised learning, unsupervised learning and reinforcement learning are three main types of machine learning approaches ( Van Der Lee and Swen, 2023 ). A more classical classification based on the model built using this approach is divided into supervised, unsupervised and semi-supervised models based on the type of input data, i.e., whether it is labeled, unlabeled or a combination of both ( Koteluk et al, 2021 ; Naik et al, 2023 ). A graphical representation of the concept is shown in Figure 3A .…”

Advances in Precision Medicine Approaches for Colorectal Cancer: From Molecular Profiling to Targeted Therapies

“…These technologies can decipher intricate patterns within genomic and proteomic information, facilitating the identification of subtle correlations and predictive biomarkers. AI-driven algorithms hold the potential to enhance the accuracy of patient stratification, predict treatment responses, and uncover previously unrecognized molecular subtypes of colorectal cancer, thereby guiding clinicians toward more effective therapeutic interventions. , Despite these optimiztic prospects, challenges persist on the path to realizing the full potential of precision medicine in colorectal cancer. One notable hurdle involves the need for robust and standardized methodologies for biomarker discovery and validation.…”