Abstract Machine learning techniques have great potential to improve medical diagnostics,
offering ways to improve accuracy, reproducibility and speed, and to ease workloads for …

Artificial intelligence (AI) is rapidly resha** cancer research and personalized clinical
care. Availability of high-dimensionality datasets coupled with advances in high …

Background Artificial intelligence (AI) research in healthcare is accelerating rapidly, with
potential applications being demonstrated across various domains of medicine. However …

The number of publications on deep learning for cancer diagnostics is rapidly increasing,
and systems are frequently claimed to perform comparable with or better than clinicians …

Artificial intelligence (AI) can transform health care practices with its increasing ability to
translate the uncertainty and complexity in data into actionable—though imperfect—clinical …

In the past decade, advances in precision oncology have resulted in an increased demand
for predictive assays that enable the selection and stratification of patients for treatment. The …

Histopathological images contain rich phenotypic information that can be used to monitor
underlying mechanisms contributing to disease progression and patient survival outcomes …

Deep learning for digital pathology is hindered by the extremely high spatial resolution of
whole-slide images (WSIs). Most studies have employed patch-based methods, which often …

Cancer is an aggressive disease with a low median survival rate. Ironically, the treatment
process is long and very costly due to its high recurrence and mortality rates. Accurate early …

Machine Learning in Medicine In this view of the future of medicine, patient–provider
interactions are informed and supported by massive amounts of data from interactions with …