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To promote effective translational medicine, academia must take the lead in creating a conducive environment for the first steps into translation, urge Roch Ogier and colleagues.
With the growing number of efficient gene therapies on the market, now is the time to take actions to ensure reasonable pricing of gene therapy products. Among these, we propose to incentivize gene therapy companies to adopt a status that translates their corporate social responsibility into concrete commitments.
Cardiometabolic disease is the leading cause of death and disability in the world, driven in part by the rise in unhealthy diets, poor air quality and other byproducts of economic development. A new economic model is needed, one that places people rather than profits at its center.
As artificial intelligence moves into the realm of clinical trials, consideration is needed on whether the current CONSORT and SPIRIT reporting statements are sufficient to ensure transparency.
Emerging virtual reality systems offer intriguing therapeutic possibilities, but their development and use should be guided by ethical priorities that account for the specific vulnerabilities of patients.
A crucial consideration for the clinical application of any burgeoning science is when the understanding of that field can be implemented without the risk of unforeseen harm. In our opinion, the need for caution is particularly urgent with respect to increasingly prevalent applications of microbiome science to medicine.
The human microbiome has captured the attention of both researchers and the lay press. Given its emerging role in health and disease, it is imperative that studies are communicated well so that in turn the public does not misinterpret the findings.
It has been only recently that scientists come to realize the existence of a lymphatic system within the central nervous system. However, an Italian anatomist had already found and described the system in fine detail over 200 years ago.
In this Comment, we provide guidelines for reinforcement learning for decisions about patient treatment that we hope will accelerate the rate at which observational cohorts can inform healthcare practice in a safe, risk-conscious manner.
Here we argue that now is the time to create smarter healthcare systems in which the best treatment decisions are computationally learned from electronic health record data by deep-learning methodologies.
