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An automatic computer vision analysis approach can assist in the diagnosis of rare genetic disorders by using ordinary photographs of patients.

Metabolite analysis of plasma from enteric fever patients define signals of organism specific host–pathogen interactions and provides opportunities for new diagnostics.

Children with a parent of cancer have a higher rate of hospital contact for injury, especially immediately after the cancer diagnosis.

Applying deep learning technology for the large-scale curation of symptoms from unstructured EHR clinical notes accurately predicts the differential signals of COVID-19 diagnosis over the week preceding typical PCR testing.

The relationship between blood groups and disease is detangled, without preconceived ideas, using population-wide data, with millions of participants and lifetime follow-up, identifying not only previously known relationships but also new insights.

A robust machine learning diagnostic platform utilizing exosome proteins to distinguish various human cancers in diverse biological fluids.

Ending the TB epidemic requires scaling up access to new TB tools so that they can benefit the patients in low- and middle-income countries that need them the most.

Younger age at diagnosis of breast cancer was associated with higher risks of incident myocardial infarction and heart failure.

Investigating standard lag times prior to the pandemic and identifying key methodological considerations in lag time research can guide future investigations into the impact of time to care for patients with cancer.

A comprehensive comparison of the two types of pancreatic cancer symptom trajectories can in combination leverage a fuller potential of large health data and ultimately aid the detection of early risk factors for pancreatic cancer.