In this issue of PET Clinics, guest editors Drs. Arman Rahmim, Kuangyu Shi, and Babak Saboury bring their considerable expertise to the topic of Computational and AI Methods toward Successful Applications of Theranostics. The field of nuclear medicine is currenting undergoing a renaissance, directly related to theranostics applications, where we can “see what we treat and treat what we see” using radiopharmaceuticals that target important disease biomarkers with high sensitivity. This special issue focuses on computational and AI methods tools and techniques available and under development to enable optimal use of theranostic applications.
What Is Implementation Science: And Why It Matters for Bridging the Artificial Intelligence Innovation-to-Application Gap in Medical Imaging Toward Integrated Clinical-Computational Nuclear Medicine Large Language Models Are Reshaping Patient Data Management and Clinical Practice in Nuclear Medicine Seeing More, Treating Smarter: Role of Long-axial Field-of-view PET/CT in the Evolution of Theranostics Advances in SPECT and PET Reconstruction for Theranostics: From Diagnosis to Therapy Artificial Intelligence for Simplified Patient-centered Dosimetry in Radiopharmaceutical Therapies An Overview of Physiologically Based Pharmacokinetic (PBPK) and Population Pharmacokinetic (PopPK) Models: Applications to Radiopharmaceutical Therapies for Analysis and Personalization Verification, Validation, and Uncertainty Quantification (VVUQ) of Physiologically Based Pharmacokinetic Models for Theranostic Digital Twins: Toward Reliable Model-Informed Treatment Planning for Radiopharmaceutical Therapies Mathematical and Computational Nuclear Oncology: Toward Optimized Radiopharmaceutical Therapy via Digital Twins Quantitative and Computational Radiobiology for Precision Radiopharmaceutical Therapies A Brief History of Digital Twin Technology Toward Digital Twins for Optimal Radioembolization Radiopharmaceutical Therapy and Immunotherapy Combinations Utilizing Cancer Evolution and Computational Modeling in Metastatic Prostate Cancer
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