Calibrating a machine learning model with data from a local setting is key to predicting psychosis outcomes. Clinicians also need to understand an algorithm’s limitations and disclose clinically and ethically relevant information to patients.

Gene editing with CRISPR/Cas9 raises concerns about equitable access to therapies that could limit research participation by minority group members. These concerns can be addressed through public education, transparency, and stakeholder partnerships.

Rare genetic disease research has something to teach precision medicine about addressing some patients’ limited access to treatment. Health disparities exacerbated by high costs and limited availability of drugs can, perhaps, be mitigated when patient activism accelerates drug development.

Though the National Aeronautics and Space Administration’s collection of disaggregated genetic data for occupational surveillance and research raises numerous privacy concerns, the Genetic Information Nondiscrimination Act of 2008 allows genetic information to be used to develop personal pharmaceuticals.

Questions about data privacy need to be addressed when research institutions negotiate with companies developing mobile health applications. Commercial terms of use and data sharing notifications should be reviewed before use in human subject research settings.

Social and behavioral data contained in electronic health records are essential for studying health disparities. Can researchers avoid bias when collecting, analyzing, and using such data?

This letter responds to readers’ recent letter to the editor.

More frequent use of robotic-assisted surgeries means we need to ask more questions about care quality and equity, informed consent, and conflicts of interest.

Medicine is a service industry, the product of which is health care, and its practitioners deserve remuneration. But to some, the notion of medicine as a road to personal wealth is an example of free-market economics gone awry.