Artificial intelligence is better at spotting prostate cancer than hospital doctors, a groundbreaking study found. Developed by experts, the computer system was trained and then tested on more than 10,000 prostate MRI examinations on patients. Using the AI resulted in half fewer false positives and slashed the number of clinically insignificant cancers by a fifth when compared to radiologists, the research revealed. Doctors believe it could help reduce overdiagnosis and prevent unnecessary surgery in the most common cancer among men, hugely benefitting any future screening programme. Researchers predict using AI to help read scans will be crucial in addressing the rising demand in medical imaging worldwide.

The Scandinavian leaders of AI in breast imaging presented their research at the ScreenPoint symposium at EUSOBI 2022 in Malmo, Sweden. Dr Kristina Lang presented the MASAI trial, the first prospective randomized controlled trial on the use of AI in breast screening as an alternative for double reading. Based on her previous retrospective studies, she is convinced that AI could lead to a more efficient and more effective screening programme. In the MASAI trial at Unilabs/Skane University Hospital Malmo, women are randomly assigned to a control arm where exams are double read as usual, or to the AI-based intervention arm: Transpara triages screening exams based on risk for malignancy and assigns 90% of all screening cases to single reading, and 10% to double reading. In addition, the top 1% most suspicious cases are automatically recalled.

The NHS is to use artificial intelligence to detect, screen and treat people at risk of hepatitis C under plans to eradicate the disease by 2030. Hepatitis C often does not have any noticeable symptoms until the liver has been severely damaged, which means thousands of people are living with the infection – known as the silent killer – without realising it. Left untreated, it can cause life-threatening damage to the liver over years. But with modern treatments now available, it is possible to cure the infection. Now health chiefs are launching a hi-tech screening programme in England in a fresh drive to identify thousands of people unaware they have the virus.

Objective To examine the accuracy of artificial intelligence (AI) for the detection of breast cancer in mammography screening practice. Design Systematic review of test accuracy studies. Data sources Medline, Embase, Web of Science, and Cochrane Database of Systematic Reviews from 1 January 2010 to 17 May 2021. Eligibility criteria Studies reporting test accuracy of AI algorithms, alone or in combination with radiologists, to detect cancer in women’s digital mammograms in screening practice, or in test sets. Reference standard was biopsy with histology or follow-up (for screen negative women). Outcomes included test accuracy and cancer type detected. Study selection and synthesis Two reviewers independently assessed articles for inclusion and assessed the methodological quality of included studies using the QUality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. A single reviewer extracted data, which were checked by a second reviewer. Narrative data synthesis was performed. Results Twelve studies totalling 131 822 screened women were included. No prospective studies measuring test accuracy of AI in screening practice were found. Studies were of poor methodological quality. Three retrospective studies compared AI systems with the clinical decisions of the original radiologist, including 79 910 women, of whom 1878 had screen detected cancer or interval cancer within 12 months of screening. Thirty four (94%) of 36 AI systems evaluated in these studies were less accurate than a single radiologist, and all were less accurate than consensus of two or more radiologists. Five smaller studies (1086 women, 520 cancers) at high risk of bias and low generalisability to the clinical context reported that all five evaluated AI systems (as standalone to replace radiologist or as a reader aid) were more accurate than a single radiologist reading a test set in the laboratory. In three studies, AI used for triage screened out 53%, 45%, and 50% of women at low risk but also 10%, 4%, and 0% of cancers detected by radiologists. Conclusions Current evidence for AI does not yet allow judgement of its accuracy in breast cancer screening programmes, and it is unclear where on the clinical pathway AI might be of most benefit. AI systems are not sufficiently specific to replace radiologist double reading in screening programmes. Promising results in smaller studies are not replicated in larger studies. Prospective studies are required to measure the effect of AI in clinical practice. Such studies will require clear stopping rules to ensure that AI does not reduce programme specificity. Study registration Protocol registered as PROSPERO CRD42020213590. No additional data available.

Scientists are currently analysing how AI could be integrated into the NHS' systems, although IT experts say there is not enough evidence to justify leveraging the technology yet. The UK's chartered institute for IT – the BCS – says that the scientific community has to be more transparent about the readiness of AI for use in serious medical procedures, citing the "immaturity of most AI tools". Expanding on this point, Dr Philip Scott, chair of the BCS health and care executive, said: "While AI methods are promising, there is not yet enough scientific evidence to justify adoption in a cancer screening programme. "Unfortunately, there is so much hype about AI that some people treat it like magic. Most AI in healthcare is early stage and not shown to work clinically.

UK researchers have secured government funding to study the use of artificial intelligence for breast cancer screening in NHS hospitals. The work builds on previous research which showed that artificial intelligence could be as effective as human radiologists in spotting breast cancer from X-ray images. Backed by funding through the Artificial Intelligence in Health and Care Award, the next stages of the project aim to further assess the feasibility of the AI system to see how the technology could be integrated into the national screening programme in the future to support clinicians. The partnership, which includes Imperial College London, Google Health, Imperial College Healthcare NHS Trust, St George's Hospitals NHS Foundation Trust, and the Royal Surrey NHS Foundation Trust builds on previous work, in which the researchers trained the algorithm on depersonalised patient data and mammograms from patients in the UK and US. The findings, published in Nature in January 2020, showed the AI system was able to correctly identify cancers from the images with a similar degree of accuracy to expert radiologists, and demonstrated potential to assist clinical staff in practice.

The NHS has introduced a revolutionary new app to help diagnose Alzheimer's Disease. It takes only five minutes to complete and is more accurate than established pen-and-paper tests. The test is currently done on iPads at a general practice or hospital ward but it could soon be conducted at home on a smart phone – paving the way for the nation's first widespread screening programme for Alzheimer's and other forms of dementia within the next few years. It is hoped it will identify people at high-risk of developing the disease up to 15 years before symptoms appear, so that steps can be taken to slow its progression. The test uses artificial intelligence to assess a person's brain function by showing them large numbers of black and white photographs and asking them to identify which ones contain an animal.

Background/aims Human grading of digital images from diabetic retinopathy (DR) screening programmes represents a significant challenge, due to the increasing prevalence of diabetes. We evaluate the performance of an automated artificial intelligence (AI) algorithm to triage retinal images from the English Diabetic Eye Screening Programme (DESP) into test-positive/technical failure versus test-negative, using human grading following a standard national protocol as the reference standard. Methods Retinal images from 30 405 consecutive screening episodes from three English DESPs were manually graded following a standard national protocol and by an automated process with machine learning enabled software, EyeArt v2.1. Screening performance (sensitivity, specificity) and diagnostic accuracy (95% CIs) were determined using human grades as the reference standard. Results Sensitivity (95% CIs) of EyeArt was 95.7% (94.8% to 96.5%) for referable retinopathy (human graded ungradable, referable maculopathy, moderate-to-severe non-proliferative or proliferative). This comprises sensitivities of 98.3% (97.3% to 98.9%) for mild-to-moderate non-proliferative retinopathy with referable maculopathy, 100% (98.7%,100%) for moderate-to-severe non-proliferative retinopathy and 100% (97.9%,100%) for proliferative disease. EyeArt agreed with the human grade of no retinopathy (specificity) in 68% (67% to 69%), with a specificity of 54.0% (53.4% to 54.5%) when combined with non-referable retinopathy. Conclusion The algorithm demonstrated safe levels of sensitivity for high-risk retinopathy in a real-world screening service, with specificity that could halve the workload for human graders. AI machine learning and deep learning algorithms such as this can provide clinically equivalent, rapid detection of retinopathy, particularly in settings where a trained workforce is unavailable or where large-scale and rapid results are needed.

Eyenuk, Inc., a global artificial intelligence (AI) medical technology and services company and the leader in real-world applications for AI Eye Screening, announced that it has successfully fulfilled the contract awarded by Public Health England (PHE) to use Eyenuk's EyeArt AI Eye Screening System to grade 60,000 patient image sets from 6 different National Health Service (NHS) Diabetic Eye Screening Programmes in England. Diabetic retinopathy (DR) is a vision-threatening complication of diabetes and a leading cause of preventable vision loss globally.1 In England, an estimated 4.6 million are living with diabetes, one-third of whom are at risk of developing DR. Diabetes has become a growing health concern as the number of people diagnosed with diabetes in the U.K. has more than doubled in the last 20 years.2 The U.K. has been leading the world in diabetic retinopathy screening, achieving patient uptake rates of over 80% (screening nearly 2.5 million diabetes patients annually),3 as compared with most parts of the world where typically less than half of diabetes patients receive annual eye screening.4 As a result, diabetic retinopathy is no longer the leading cause of blindness in the working age group in England.5

LOS ANGELES--(BUSINESS WIRE)-- Eyenuk, Inc., a global artificial intelligence (AI) medical technology and services company and the leader in real-world applications for AI Eye Screening, announced that it has successfully fulfilled the contract awarded by Public Health England (PHE) to use Eyenuk's EyeArt AI Eye Screening System to grade 60,000 patient image sets from 6 different National Health Service (NHS) Diabetic Eye Screening Programmes in England. Diabetic retinopathy (DR) is a vision-threatening complication of diabetes and a leading cause of preventable vision loss globally.1 In England, an estimated 4.6 million are living with diabetes, one-third of whom are at risk of developing DR. Diabetes has become a growing health concern as the number of people diagnosed with diabetes in the U.K. has more than doubled in the last 20 years.2 The U.K. has been leading the world in diabetic retinopathy screening, achieving patient uptake rates of over 80% (screening nearly 2.5 million diabetes patients annually),3 as compared with most parts of the world where typically less than half of diabetes patients receive annual eye screening.4 As a result, diabetic retinopathy is no longer the leading cause of blindness in the working age group in England.5