AIMC Topic: Medical Oncology

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Systematic reviews of machine learning in healthcare: a literature review.

Expert review of pharmacoeconomics & outcomes research
INTRODUCTION: The increasing availability of data and computing power has made machine learning (ML) a viable approach to faster, more efficient healthcare delivery.

Historical perspective and future directions: computational science in immuno-oncology.

Journal for immunotherapy of cancer
Immuno-oncology holds promise for transforming patient care having achieved durable clinical response rates across a variety of advanced and metastatic cancers. Despite these achievements, only a minority of patients respond to immunotherapy, undersc...

Imaging in interventional oncology, the better you see, the better you treat.

Journal of medical imaging and radiation oncology
Imaging and image processing is the fundamental pillar of interventional oncology in which diagnostic, procedure planning, treatment and follow-up are sustained. Knowing all the possibilities that the different image modalities can offer is capital t...

Medical imaging and multimodal artificial intelligence models for streamlining and enhancing cancer care: opportunities and challenges.

Expert review of anticancer therapy
INTRODUCTION: Artificial intelligence (AI) has the potential to transform oncologic care. There have been significant developments in AI applications in medical imaging and increasing interest in multimodal models. These are likely to enable improved...

Novel research and future prospects of artificial intelligence in cancer diagnosis and treatment.

Journal of hematology & oncology
Research into the potential benefits of artificial intelligence for comprehending the intricate biology of cancer has grown as a result of the widespread use of deep learning and machine learning in the healthcare sector and the availability of highl...

Open science practices need substantial improvement in prognostic model studies in oncology using machine learning.

Journal of clinical epidemiology
OBJECTIVE: To describe the frequency of open science practices in a contemporary sample of studies developing prognostic models using machine learning methods in the field of oncology.

Translation of tissue-based artificial intelligence into clinical practice: from discovery to adoption.

Oncogene
Digital pathology (DP), or the digitization of pathology images, has transformed oncology research and cancer diagnostics. The application of artificial intelligence (AI) and other forms of machine learning (ML) to these images allows for better inte...

One label is all you need: Interpretable AI-enhanced histopathology for oncology.

Seminars in cancer biology
Artificial Intelligence (AI)-enhanced histopathology presents unprecedented opportunities to benefit oncology through interpretable methods that require only one overall label per hematoxylin and eosin (H&E) slide with no tissue-level annotations. We...