The first international standards for the design and reporting of clinical trials involving artificial intelligence have been announced in a move experts hope will tackle the issue of overhyped studies and prevent harm to patients. While the possibility that AI could revolutionise healthcare has fuelled excitement, in particular around screening and diagnosis, researchers have previously warned that the field is strewn with poor-quality research. Now an international team of experts has launched a set of guidelines under which clinical trials involving AI will be expected to meet a stringent checklist of criteria before being published in top journals. The new standards are being simultaneously published in the BMJ, Nature Medicine and Lancet Digital Health, expanding on existing standards for clinical trials – put in place more than a decade ago for drugs, diagnostic tests, and other interventions – to make them more suitable for AI-based systems. Prof Alastair Denniston of the University of Birmingham, an expert in the use of AI in healthcare and member of the team, said the guidelines were crucial to making sure AI systems were safe and effective for use in healthcare settings.

The first international standards for the design and reporting of clinical trials involving artificial intelligence have been announced in a move experts hope will tackle the issue of overhyped studies and prevent harm to patients. While the possibility that AI could revolutionise healthcare has fuelled excitement, in particular around screening and diagnosis, researchers have previously warned that the field is strewn with poor-quality research. Now an international team of experts has launched a set of guidelines under which clinical trials involving AI will be expected to meet a stringent checklist of criteria before being published in top journals. The new standards are being simultaneously published in the BMJ, Nature Medicine and Lancet Digital Health, expanding on existing standards for clinical trials – put in place more than a decade ago for drugs, diagnostic tests, and other interventions – to make them more suitable for AI-based systems. Prof Alastair Denniston of the University of Birmingham, an expert in the use of AI in healthcare and member of the team, said the guidelines were crucial to making sure AI systems were safe and effective for use in healthcare settings.

For years, many artificial intelligence enthusiasts and researchers have promised that machine learning will change modern medicine. Thousands of algorithms have been developed to diagnose conditions like cancer, heart disease and psychiatric disorders. Now, algorithms are being trained to detect COVID-19 by recognizing patterns in CT scans and X-ray images of the lungs. Many of these models aim to predict which patients will have the most severe outcomes and who will need a ventilator. The excitement is palpable; if these models are accurate, they could offer doctors a huge leg up in testing and treating patients with the coronavirus. But the allure of AI-aided medicine for the treatment of real COVID-19 patients appears far off.

The U.S. is experiencing a chronic shortage of medical professionals. Of course, this includes physicians. But psychiatrists will also be in scant supply. In five years, the U.S.'s supply of psychiatrists will be 15,600 short of the demand from patients, according to a 2017 study from the National Council for Behavioral Health. Although the technology is relatively new, AI applications have already been implemented in medical settings to help diagnose diseases, clarify treatment plans, and study radiology images.

Years from now, there may be a shortage of psychiatrists in the United States as their mental health system is already overburdened and the growth in demand for the services of psychiatrists outpaces supply, according to a 2017 report from the National Council for Behavioral Health. Some proponents say that, by then, artificial intelligence, an unlikely tool, may be ready to help the mental health practitioners mitigate the impact of the deficit. Artifical intelligence and medicine are made for each other, it has shown promise in diagnosing disease, interpreting images and even zeroing in on treatment plans. Even though psychiatry is a unique human field in many ways, requiring emotional intelligence and perception that computers can't stimulate, even here, AI could have an impact. The field could benefit from AI's ability to analyze data and pick up on patterns and warning signs that are very subtle, humans might never notice them.

British researchers, through a new scientific review, have found that artificial intelligence (AI) can potentially diagnose a disease based on a medical image and are on par with human healthcare professionals. However, inadequate quality studies in this regard mean that the real potential of the use of artificial intelligence in the diagnosis area is yet to be known. The research team has been headed by Professor Alastair Denniston of the University Hospitals Birmingham NHS Foundation Trust. His team's work is said to be the first systematic review of existing research into the technology, analysing its capability in comparison to healthcare professionals. Denniston and his team scanned through various medical databases for all studies published between January 2012 and early June 2019 to compile their study. The findings of the work have been published in The Lancet Digital Health journal.

Artificial intelligence is on a par with human experts when it comes to making medical diagnoses based on images, a review has found. The potential for artificial intelligence in healthcare has caused excitement, with advocates saying it will ease the strain on resources, free up time for doctor-patient interactions and even aid the development of tailored treatment. Last month the government announced £250m of funding for a new NHS artificial intelligence laboratory. However, experts have warned the latest findings are based on a small number of studies, since the field is littered with poor-quality research. One burgeoning application is the use of AI in interpreting medical images – a field that relies on deep learning, a sophisticated form of machine learning in which a series of labelled images are fed into algorithms that pick out features within them and learn how to classify similar images.