Prediction of chemotherapy-related complications in pediatric oncology patients: artificial intelligence and machine learning implementations

Complications and comorbidities associated with antineoplastic chemotherapy: Rethinking drug design and delivery for anticancer therapy

Despite the considerable advancements in chemotherapy as a cornerstone modality in cancer treatment, the prevalence of complications and pre-existing diseases is on the rise among cancer patients along with prolonged survival and aging population. The relationships between these disorders and cancer are intricate, bearing significant influence on the survival and quality of life of individuals with cancer and presenting challenges for the prognosis and outcomes of malignancies. Herein, we review the prevailing complications and comorbidities that often accompany chemotherapy and summarize the lessons to learn from inadequate research and management of this scenario, with an emphasis on possible strategies for reducing potential complications and alleviating comorbidities, as well as an overview of current preclinical cancer models and practical advice for establishing bio-faithful preclinical models in such complex context.

Artificial intelligence in rare disease diagnosis and treatment

Artificial intelligence (AI) utilization in health care has grown over the past few years. It also has demonstrated potential in improving the efficiency of diagnosis and treatment. Some types of AI, such as machine learning, allow for the efficient analysis of vast datasets, identifying patterns, and generating key insights. Predictions can then be made for medical diagnosis and personalized treatment recommendations. The use of AI can bypass some conventional limitations associated with rare diseases. Namely, it can optimize traditional randomized control trials, and may eventually reduce costs for drug research and development. Recent advancements have enabled researchers to train models based on large datasets and then fine-tune these models on smaller datasets typically associated with rare diseases. In this mini-review, we discuss recent advancements in AI and how AI can be applied to streamline rare disease diagnosis and optimize treatment.