Should We Rely on AI to Help Avoid Bias in Patient Selection for Major Surgery?

Many regard iatrogenic injuries as consequences of diagnosis or intervention actions. But inaction-not offering indicated major surgery-can also result in iatrogenic injury. This article explores some surgeons' overestimations of operative risk based on patients' race and socioeconomic status as unduly influential in their decisions about whether to perform major cancer or cardiac surgery on some patients with appropriate clinical indications. This article also considers artificial intelligence and machine learning-based clinical decision support systems that might offer more accurate, individualized risk assessment that could make patient selection processes more equitable, thereby mitigating racial and ethnic inequity in cancer and cardiac disease.

Ethical Rationing of Personal Protective Equipment to Minimize Moral Residue During the COVID-19 Pandemic.

This article proposes systems for the fair distribution of scarce resources to healthcare providers. It builds on classic ethical structures and adapts them to the equitable distribution of personal protective equipment (PPE) to clinicians at risk of contracting novel corona virus-19 (COVID-19). The article also defines systems of allocation that are generally considered unethical and are to be avoided. We emphasize that policies must be transparent, collaborative, applied equally, and have a system of accountability. It is recognized that unless the supply of PPE is quickly replenished, or viable alternatives to traditional equipment are devised in the coming days to weeks, hospitals and healthcare systems will face the difficult task of rationing PPE to at-risk clinicians. This paper suggests an ethical framework for that process.