AI Medical Compendium Topic

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Comprehensive biobanking strategy with clinical impact at the European Cancer Moonshot Lund Center.

Journal of proteomics
This white paper presents a comprehensive biobanking framework developed at the European Cancer Moonshot Lund Center that merges rigorous sample handling, advanced automation, and multi-omic analyses to accelerate precision oncology. Tumor and blood-...

Enhancing Physician-Patient Communication in Oncology Using GPT-4 Through Simplified Radiology Reports: Multicenter Quantitative Study.

Journal of medical Internet research
BACKGROUND: Effective physician-patient communication is essential in clinical practice, especially in oncology, where radiology reports play a crucial role. These reports are often filled with technical jargon, making them challenging for patients t...

Assessing the Quality and Reliability of ChatGPT's Responses to Radiotherapy-Related Patient Queries: Comparative Study With GPT-3.5 and GPT-4.

JMIR cancer
BACKGROUND: Patients frequently resort to the internet to access information about cancer. However, these websites often lack content accuracy and readability. Recently, ChatGPT, an artificial intelligence-powered chatbot, has signified a potential p...

Federated transfer learning with differential privacy for multi-omics survival analysis.

Briefings in bioinformatics
Multi-omics data often suffer from the "big $p$, small $n$" problem where the dimensionality of features is significantly larger than the sample size, making the integration of multi-omics data for survival analysis of a specific cancer particularly ...

Machine learning predictions of tumor progression: How reliable are we?

Computers in biology and medicine
BACKGROUND: Cancer continues to pose significant challenges in healthcare due to the complex nature of tumor progression. In this digital era, artificial intelligence has emerged as a powerful tool that can potentially transform multiple aspects of c...

Learning Chemotherapy Drug Action via Universal Physics-Informed Neural Networks.

Pharmaceutical research
OBJECTIVE: Quantitative systems pharmacology (QSP) is widely used to assess drug effects and toxicity before the drug goes to clinical trial. However, significant manual distillation of the literature is needed in order to construct a QSP model. Para...

Analyzing factors influencing hospitalization costs for five common cancers in China using neural network models.

Journal of medical economics
BACKGROUND: Malignant tumors are a major global health crisis, causing 25% of deaths in China, with lung, liver, thyroid, breast, and colon cancers being the most common. Understanding the factors influencing hospitalization costs for these cancers i...

A clinical benchmark of public self-supervised pathology foundation models.

Nature communications
The use of self-supervised learning to train pathology foundation models has increased substantially in the past few years. Notably, several models trained on large quantities of clinical data have been made publicly available in recent months. This ...

Artificial intelligence utilization in cancer screening program across ASEAN: a scoping review.

BMC cancer
BACKGROUND: Cancer remains a significant health challenge in the ASEAN region, highlighting the need for effective screening programs. However, approaches, target demographics, and intervals vary across ASEAN member states, necessitating a comprehens...

New horizons at the interface of artificial intelligence and translational cancer research.

Cancer cell
Artificial intelligence (AI) is increasingly being utilized in cancer research as a computational strategy for analyzing multiomics datasets. Advances in single-cell and spatial profiling technologies have contributed significantly to our understandi...