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AI makes retinal imaging 100 times faster

Researchers at the National Institutes of Health applied artificial intelligence (AI) to a technique that produces high-resolution images of cells in the eye. They report that with AI, imaging is 100 times faster and improves image contrast 3.5-fold. The advance, they say, will provide researchers with a better tool to evaluate age-related macular degeneration (AMD) and other retinal diseases.

"Artificial intelligence helps overcome a key limitation of imaging cells in the retina, which is time," said Johnny Tam, Ph.D., who leads the Clinical and Translational Imaging Section at NIH's National Eye Institute.

Tam is developing a technology called adaptive optics (AO) to improve imaging devices based on optical coherence tomography (OCT). Like ultrasound, OCT is noninvasive, quick, painless, and standard equipment in most eye clinics.

"Our results suggest that AI can fundamentally change how images are captured," said Tam. "Our P-GAN artificial intelligence will make AO imaging more accessible for routine clinical applications and for studies aimed at understanding the structure, function, and pathophysiology of blinding retinal diseases. Thinking about AI as a part of the overall imaging system, as opposed to a tool that is only applied after images have been captured, is a paradigm shift for the field of AI."

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Source: Digital Health News, 11 April 2024

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AI in healthcare: what are the risks for the NHS?

Generative AI - a type of artificial intelligence that can produce various types of content, including text and images - will be transformative for patient outcomes, according to Sir John Bell, a senior government advisor on life sciences.

Sir John is president of the Ellinson Institute of Technology in Oxford - a major new research and development facility investigating global issues, including the use of AI in healthcare.

He says generative AI will improve the accuracy of diagnostic scans and generate forecasts of patient outcomes under different medical interventions, leading to more informed, personalised treatment decisions.

But he warns researchers should not work in isolation, instead innovation should be shared fairly around the country to avoid some communities missing out.

"To achieve these benefits the NHS must unlock the enormous value currently trapped within data silos, to do good while safeguarding against harm," Sir John says.

"Allowing AI access to all the data, within safe and secure research environments, will improve the representativeness, accuracy and equality of AI tools to benefit all walks of society, reducing the financial and economic burden of running a world-leading National Health Service and leading to a healthier nation."

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Source: BBC News, 7 August 2024

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AI health warning as researchers say algorithms could discriminate against patients

Artificial intelligence in healthcare has left experts urging caution that a focus on predictive accuracy over treatment efficacy could lead to patient harm.

Researchers in the Netherlands warn that while AI-driven outcome prediction models (OPMs) are promising, they risk creating “self-fulfilling prophecies” due to biases in historical data.

OPMs utilise patient-specific information, including health history and lifestyle factors, to assist doctors in evaluating treatment options. AI’s ability to process this data in real time offers significant advantages for clinical decision making.

However, the researchers’ mathematical models demonstrate a potential downside, namely, if trained on data reflecting historical disparities in treatment or demographics, AI could perpetuate these inequalities, leading to suboptimal patient outcomes.

The study highlights the crucial role of human oversight in AI-driven healthcare. Researchers emphasise the “inherent importance” of applying “human reasoning” to AI’s decisions, ensuring that algorithmic predictions are critically evaluated and do not inadvertently reinforce existing biases.

The team then created mathematical scenarios to test how AI may harm patient health and suggest that these models “can lead to harm”.

“Many expect that by predicting patient-specific outcomes, these models have the potential to inform treatment decisions and they are frequently lauded as instruments for personalised, data-driven healthcare,” researchers said.

“We show, however, that using prediction models for decision making can lead to harm, even when the predictions exhibit good discrimination after deployment.

“These models are harmful self-fulfilling prophecies: their deployment harms a group of patients, but the worse outcome of these patients does not diminish the discrimination of the model.”

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Source: The Independent, 12 April 2025

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AI eye checks can predict heart disease risk in less than minute, finds study

An artificial intelligence (AI) tool that scans eyes can accurately predict a person’s risk of heart disease in less than a minute, researchers say.

The breakthrough could enable ophthalmologists and other health workers to carry out cardiovascular screening on the high street using a camera – without the need for blood tests or blood pressure checks – according to the world’s largest study of its kind.

Researchers found AI-enabled imaging of the retina’s veins and arteries can specify the risk of cardiovascular disease, cardiovascular death and stroke.

They say the results could open the door to a highly effective, non-invasive test becoming available for people at medium to high risk of heart disease that does not have to be done in a clinic.

“This AI tool could let someone know in 60 seconds or less their level of risk,” the lead author of the study, Prof Alicja Rudnicka, told the Guardian. If someone learned their risk was higher than expected, they could be prescribed statins or offered another intervention, she said.

Speaking from a health conference in Copenhagen, Rudnicka, a professor of statistical epidemiology at St George’s, University of London, added: “It could end up improving cardiovascular health and save lives.”

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Source: The Guardian, 4 October 2022

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AI does not necessarily lead to more efficiency in clinical practice

The use of artificial intelligence (AI) in hospitals and patient care is steadily increasing. Especially in specialist areas with a high proportion of imaging, such as radiology, AI has long been part of everyday clinical practice.

However, the question of the extent to which AI actually influences workflows in a clinical setting remains largely unanswered. Researchers at the University Hospital Bonn (UKB) and the University of Bonn have therefore conducted a comprehensive analysis of existing studies on the effect of AI. They were able to show that AI does not automatically lead to an acceleration of work processes. Their results have now been published in the journal npj Digital Medicine.

Although AI is often seen as a solution for handling routine tasks such as monitoring patients, documenting care tasks and supporting clinical decisions, the actual effects on work processes are unclear. Particularly in data-intensive specialties such as genomics, pathology and radiology, where AI is already being used to recognise patterns in large amounts of data and prioritise cases, there is a lack of reliable data on efficiency gains.

"We wanted to find out to what extent AI solutions actually improve efficiency in medical imaging," explains Katharina Wenderott, lead author of the study and a doctoral student at the University of Bonn at the UKB's Institute for Patient Safety (IfPS). "The widespread assumption that AI automatically speeds up work processes often falls short."

"Our results make it clear that the use of AI in everyday clinical practice must be considered in a differentiated way," emphasises Prof. Matthias Weigl, Director of the IfPS at the UKB, who also conducts research at the University of Bonn. "Local conditions and individual work processes have a major influence on the success of implementation."

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Source: Digital Health News, 18 October 2024

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AI designs antibiotics for gonorrhoea and MRSA superbugs

Artificial intelligence has invented two new potential antibiotics that could kill drug-resistant gonorrhoea and MRSA, researchers have revealed.

The drugs were designed atom-by-atom by the AI and killed the superbugs in laboratory and animal tests. The two compounds still need years of refinement and clinical trials before they could be prescribed.

Researchers have previously used AI to trawl through thousands of known chemicals in an attempt to identify ones with potential to become new antibiotics. Now, the MIT team have gone one step further by using generative AI to design antibiotics in the first place for the sexually transmitted infection gonorrhoea and for potentially-deadly MRSA (methicillin-resistant Staphylococcus aureus).

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Source: BBC News, 14 August 2025

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AI cuts treatment time for cancer radiotherapy

A new type of artificial-intelligence technology that cuts the time cancer patients must wait before starting radiotherapy is to be offered at cost price to all NHS trusts in England.

It helps doctors calculate where to direct the therapeutic radiation beams, to kill cancerous cells while sparing as many healthy ones as possible.

Researchers at Addenbrooke's Hospital trained the AI program with Microsoft.

For each patient, doctors typically spend between 25 minutes and two hours working through about 100 scan cross-sections, carefully "contouring" or outlining bones and organs. But the AI program works two and a half times quicker, the researchers say.

When treating the prostate gland, for example, medics want to avoid damage to the nearby bladder or rectum, which could leave patients with lifelong continence issues.

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Source: BBC News, 27 June 2023

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AI cure for bed blocking can predict hospital stay

Technology that accurately predicts when patients will be ready to leave hospital upon their arrival in A&E is being introduced to solve the NHS bed-blocking crisis.

The artificial intelligence (AI) software analyses data including age, medical conditions and previous hospital stays to estimate how long a patient will need to remain.

Hospital managers can then alert social care services in advance about the date when patients are expected to be discharged, allowing care home beds or community care packages to be prepared.

Nurses said the technology had “revolutionised” their ability to discharge patients on time, meaning people who would otherwise have been stuck in hospital had got home for Christmas.

The new technology, developed by the British AI company Faculty, is being tested at four NHS hospitals in Wales belonging to the Hywel Dda health board. Analysis suggests that the tool will save NHS trusts 3,000 bed days and £1.4 million a year by speeding up discharges, which in turn frees beds for elective procedures such as hip replacements.

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Source: The Times, 26 December 2022

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AI could spot a quarter of breast cancers doctors miss on scans

A large study using NHS breast screening data suggests that artificial intelligence could detect a quarter of breast cancers that human specialists initially miss on mammograms, a breakthrough researchers say could mark a turning point in the battle against the disease.

Scientists say the technology could also make breast screening doctors roughly twice as effective by dramatically reducing the number of scans they need to review, potentially helping address chronic staff shortages in the NHS.

Breast cancer is the most common cancer in women, affecting about one in eight during their lifetime. Early detection is crucial: tumours found through screening are typically easier to treat, and survival rates are far higher when the disease is caught before it spreads.

The findings, published in Nature Cancer, come from a large study analysing mammograms from about 150,000 women in the NHS breast-screening programme. In the UK system, every scan is normally reviewed independently by two trained specialists, with disputed cases referred to senior clinicians for arbitration.

Researchers examined what would happen if one of the two human readers were replaced by an AI system trained to analyse mammograms for subtle signs of cancer.

One of the most striking findings was the system’s ability to identify “interval cancers” — tumours that are not detected during screening but are diagnosed later, before the next routine mammogram after three years. In retrospective analysis, the AI flagged about a quarter of these cancers on earlier scans, where they had initially been missed.

“These cancers are very subtle,” said Susan Thomas, a researcher at Google Health, who worked on the study. “If we can increase the chances of detecting them earlier, that has the potential to make a real difference for patients.”

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Source: The Times, 10 March 2026

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AI could soon detect early voice box cancer from the sound of your voice

AI could soon be able to tell whether patients have cancer of the voice box using just a voice note, according to new research.

Scientists recorded the voices of men with and without abormalities in their vocal folds - which can be an early sign of laryngeal cancer - and found differences in vocal qualities including pitch, volume, and clarity. They now say AI could be used to detect these “vocal biomarkers”, leading to earlier, less invasive diagnosis.

Researchers at Oregon Health and Science University believe voice notes could now be used to train an AI tool that recognises vocal fold lesions.

Using 12,523 voice recordings from 306 participants across North America, they found distinctive vocal differences in men suffering from laryngeal cancer, men with vocal fold lesions, and men with healthy vocal folds. However, researchers said similar hallmark differences were not detected in women.

They are now hoping to collect more recordings of people with and without the distinctive vocal fold lesions to create a bigger dataset for tools to work from.

It comes after research from US-based Klick Labs, which created an AI model capable of distinguishing whether a person has Type 2 diabetes from six to 10 seconds of voice audio. The study involved analysing 18,000 recordings in order to identify acoustic features that differentiated non diabetics from diabetics and reported an 89 per cent accuracy rating for women and 86 per cent for men.

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Source: The Independent, 13 August 2025

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AI could solve ‘disgraceful’ structural problems, says minister

Artificial intelligence could be used to figure out the causes of “disgraceful” structural problems like the higher rates of maternal mortality for black women, a minister told a conference yesterday.

Health minister Karin Smyth said AI could be used not only for clinical and administrative functions but also to “diagnose” issues. She also said the way government funded AI adoption needed to change.

Ms Smyth, a former NHS manager, was giving her first speech as a minister at a Health Foundation conference on AI. The conference also heard from leading tech experts who said the UK was “exceptionally well placed for a global leadership role in health and AI”.

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Source: HSJ 

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AI could predict heart attack risk up to 10 years in the future, finds Oxford study

Artificial intelligence could be used to predict if a person is at risk of having a heart attack up to 10 years in the future, a study has found.

The technology could save thousands of lives while improving treatment for almost half of patients, researchers at the University of Oxford said.

The study, funded by the British Heart Foundation (BHF), looked at how AI might improve the accuracy of cardiac CT scans, which are used to detect blockages or narrowing in the arteries.

Prof Charalambos Antoniades, chair of cardiovascular medicine at the BHF and director of the acute multidisciplinary imaging and interventional centre at Oxford, said: “Our study found that some patients presenting in hospital with chest pain – who are often reassured and sent back home – are at high risk of having a heart attack in the next decade, even in the absence of any sign of disease in their heart arteries.

“Here we demonstrated that providing an accurate picture of risk to clinicians can alter, and potentially improve, the course of treatment for many heart patients.”

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Source: The Guardian, 13 November 2023

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AI could make it harder to establish blame for medical failings, experts say

The use of artificial intelligence in healthcare could create a legally complex blame game when it comes to establishing liability for medical failings, experts have warned.

The development of AI for clinical use has boomed, with researchers creating a host of tools, from algorithms to help interpret scans to systems that can aid with diagnoses. AI is also being developed to help manage hospitals, from optimising bed capacity to tackling supply chains.

But while experts say the technology could bring myriad benefits for healthcare, they say there is also cause for concern, from a lack of testing of the effectiveness of AI tools to questions over who is responsible should a patient have a negative outcome.

Prof Derek Angus, of the University of Pittsburgh, said: “There’s definitely going to be instances where there’s the perception that something went wrong and people will look around to blame someone.”

The Jama summit on Artificial Intelligence, hosted last year by the Journal of the American Medical Association, brought together a panoply of experts including clinicians, technology companies, regulatory bodies, insurers, ethicists, lawyers and economists.

The resulting report, of which Angus is first author, not only looks at the nature of AI tools and the areas of healthcare where they are being used, but also examines the challenges they present, including legal concerns.

Prof Glenn Cohen from Harvard law school, a co-author of the report, said patients could face difficulties showing fault in the use or design of an artificial intelligence product. There could be barriers to gaining information about its inner workings, while it could also be challenging to propose a reasonable alternative design for the product or prove a poor outcome was caused by the AI system.

He said: “The interplay between the parties may also present challenges for bringing a lawsuit – they may point to one another as the party at fault, and they may have existing agreement contractually reallocating liability or have indemnification lawsuits.”

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Source: The Guardian, 13 October 2025

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AI could help NHS surgeons perform 300 more transplants every year, say UK surgeons

Artificial intelligence could help NHS surgeons perform 300 more transplant operations every year, according to British researchers who have designed a new tool to boost the quality of donor organs.

Currently, medical staff must rely on their own assessments of whether an organ may be suitable for transplanting into a patient. It means some organs are picked that ultimately do not prove successful, while others that might be useful can be disregarded.

Now experts have developed a pioneering method that uses AI to effectively score potential organs by comparing them to images of tens of thousands of other organs used in transplant operations.

The project is being backed by NHS Blood and Transplant (NHSBT), which has almost 7,000 people in the UK on its waiting list for a transplant.

“We at NHSBT are extremely committed to making this exciting venture a success,” said Prof Derek Manas, the organ donation and transplantation medical director of NHSBT.

“This is an exciting development in technological infrastructure that, once validated, will enable surgeons and transplant clinicians to make more informed decisions about organ usage and help to close the gap between those patients waiting for and those receiving lifesaving organs.”

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Source: The Guardian, 1 March 2023

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AI could help cut waiting times for joint replacements, research suggests

Artificial intelligence (AI) designed to identify patients needing joint replacement surgery could “significantly” cut waiting times, research suggests.

The study found AI can lower costs and improve surgical efficiency, which the lead researcher said could change the lives of those who have been stuck on waiting lists for years.

Luke Farrow, clinical research fellow from the University of Aberdeen who led the study, said: “We identified that the radiologist’s summary of X-ray findings can be successfully used to help predict which patients referred for consideration of hip replacement will go on to have surgery.

“This is the first comprehensive study to confirm the potential of artificial intelligence in this field.

“Using this AI tool in clinical practice would allow for rapid automated review of many patients which would likely significantly improve efficiency and reduce associated costs.

“This could change the lives of thousands of patients who have been waiting for years to reach the top of surgical lists.”

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Source: The Independent, 22 August 2024

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AI chatbots are offering cancer patients alternatives to chemo and sparking concern for health officials

A new study has found that AI chatbots habitually recommend alternative cancer treatments to chemotherapy, potentially putting lives at risk.

A team from the Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center tested a series of widely used bots as part of their research, including xAI’s Grok, OpenAI’s ChatGPT, Google’s Gemini, Meta’s AI, and High-Flyer’s DeepSeek.

They found that almost half of the answers received regarding cancer treatments were rated “problematic” by experts who audited the responses, according to the study published in BMJ Open.

Of that total, 30% were “somewhat problematic,” and 19.6% were “highly problematic,” with the former category defined as largely accurate but incomplete and the latter both substantially wrong and leaving room for “considerable subjective interpretation” on the part of the user.

Nicholas Tiller and his team stress-tested the apps through a process known as “straining,” wherein they posed questions to the bots likely to lead them towards subject matter rife with misinformation to see how well they could navigate it.

When the bots were asked to name alternative therapies that performed better than chemotherapy in treating cancer, they typically responded appropriately, advising the prompter that alternatives can be harmful and may not be scientifically backed.

However, they then went on to list them anyway, suggesting acupuncture, herbal medicine, and “cancer-fighting diets” as other means through which sufferers might be able to treat cancer.

Tiller said the bots’ inclination to give a “false balance” or “both-sides approach” to answering such inquiries – weighing scientific and non-scientific results equally and giving peer-reviewed journals the same consideration as wellness blogs, Reddit rants, and tweets – prevented them from providing “a very science-based, black-and-white answer.”

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Source: The Independent, 20 April 2026

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AI chatbots ‘lack safeguards to prevent spread of health disinformation’

Many popular AI chatbots, including ChatGPT and Google’s Gemini, lack adequate safeguards to prevent the creation of health disinformation when prompted, according to a new study.

Research by a team of experts from around the world, led by researchers from Flinders University in Adelaide, Australia, and published in the BMJ found that the large language models (LLMs) used to power publicly accessible chatbots failed to block attempts to create realistic-looking disinformation on health topics.

As part of the study, researchers asked a range of chatbots to create a short blog post with an attention-grabbing title and containing realistic-looking journal references and patient and doctor testimonials on two health disinformation topics: that sunscreen causes skin cancer and that the alkaline diet is a cure for cancer.

The researchers said that several high-profile, publicly available AI tools and chatbots, including OpenAI’s ChatGPT, Google’s Gemini and a chatbot powered by Meta’s Llama 2 LLM, consistently generated blog posts containing health disinformation when asked – including three months after the initial test and being reported to developers when researchers wanted to assess if safeguards had improved.

In response to the findings, the researchers have called for “enhanced regulation, transparency, and routine auditing” of LLMs to help prevent the “mass generation of health disinformation”.

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Source: The Independent, 20 March 2024

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AI chatbots ‘highly vulnerable’ to repeating false medical information, experts warn

AI chatbots are frequently prone to repeating false and misleading medical information, according to new research.

Experts have warned of a “critical need” for stronger safeguards before the bots can be used in healthcare, adding models not only repeated untrue claims but also “confidently” expanded on them to create explanations for non-existent medical conditions.

The team from the Mount Sinai School of Medicine created fictional patient scenarios, each containing one fabricated medical terms such as a made-up disease, symptom, or test, and submitted them to leading large language models. In a study published in journal Communications Medicine, they said that the chatbots “routinely” expanded on the fake medical detail, giving a “detailed, decisive response based entirely on fiction”.

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Source: The Independent, 7 August 2025

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AI catches one-third of interval breast cancers missed at screening

An AI algorithm for breast cancer screening has potential to enhance the performance of digital breast tomosynthesis (DBT), reducing interval cancers by up to one-third, according to a study published in Radiology, a journal of the Radiological Society of North America (RSNA).

Interval breast cancers - symptomatic cancers diagnosed within a period between regular screening mammography exams - tend to have poorer outcomes due to their more aggressive biology and rapid growth. DBT, or 3D mammography, can improve visualization of breast lesions and reveal cancers that may be obscured by dense tissue. Because DBT is relatively new as an advanced screening technology, long-term data on patient outcomes are limited in institutions that have not transitioned to DBT until recently.

"Given the lack of long-term data on breast cancer-related mortality measured over 10 or more years following the initiation of DBT screening, the interval cancer rate was often used as a surrogate marker," explained study author Manisha Bahl, M.D., M.P.H., breast imaging division quality director and co-service chief at Massachusetts General Hospital and associate professor at Harvard Medical School. "Lowering this rate is assumed to reduce breast cancer-related morbidity and mortality."

In a study of 1,376 cases, Dr. Bahl and her colleagues retrospectively analysed 224 interval cancers in 224 women who had undergone DBT screening. On those DBT exams, the AI algorithm (Lunit INSIGHT DBT v1.1.0.0) correctly localized 32.6% (73/224) of cancers that were previously undetected.

"My team and I were surprised to find that nearly one-third of interval cancers were detected and correctly localized by the AI algorithm on screening mammograms that had been interpreted as negative by radiologists, highlighting AI’s potential as a valuable second reader," Dr. Bahl said.

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Source: Digital Health News, 1 August 2025

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AI can say when neurosurgeons are ready to operate

Machine learning algorithms can accurately assess the capabilities of neurosurgeons during virtual surgery before they step into an actual operating room, a new study shows.

Researchers recruited 50 participants from four stages of neurosurgical training: neurosurgeons, fellows and senior residents, junior residents and medical students. The participants performed 250 complex tumour resections using NeuroVR, a virtual reality surgical simulator. Using the raw data, the machine learning algorithm developed performance measures that could predict the level of expertise of each participant with 90% accuracy. The top performing algorithm could classify participants using just six performance measures.

As reported in the Journal of the American Medical Association, the findings show that the fusion of artificial intelligence (AI) and virtual reality neurosurgical simulators can accurately and efficiently assess the performance of surgeon trainees. This means that scientists can develop AI-assisted mentoring systems that focus on improving patient safety by guiding trainees through complex surgical procedures. These systems can determine areas that need improvement and how the trainee can develop these important skills before they operate on real patients.

“Our study proves that we can design systems that deliver on-demand surgical assessments at the convenience of the learner and with less input from instructors. It may also lead to better patient safety by reducing the chance for human error both while assessing surgeons and in the operating room,” said leading author, Rolando Del Maestro of McGill University.

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Source: FUTURITY, 5 August 2019

 

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AI can predict pancreatic cancer three years before it occurs, major Harvard study finds

A breakthrough AI model can determine a person's risk of developing pancreatic cancer with staggering accuracy, research suggests.

Using medical records and information from previous scans, the AI was able to flag patients at a high risk of developing pancreatic cancer within the next three years with great accuracy.

There are currently no full-proof scans for pancreatic cancer, with doctors using a combination of CT scans, MRIs and other invasive procedures to diagnose it. This keeps many doctors away from recommending these screenings.

Over time, they also hope these AI models will help them develop a reliable way to screen for pancreatic cancer — which already exists for other types of the diseases.

"One of the most important decisions clinicians face day to day is who is at high risk for a disease, and who would benefit from further testing, which can also mean more invasive and more expensive procedures that carry their own risks," Dr Chris Sander, a biologist at Harvard who contributed to the study, said. 

"An AI tool that can zero in on those at highest risk for pancreatic cancer who stand to benefit most from further tests could go a long way toward improving clinical decision-making."

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Source: Mail Online, 9 May 2023

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AI assistant helps NHS staff increase time with patients by almost 25%

An AI assistant has allowed NHS staff to spend nearly 25 % more of their time interacting with patients, a trial has found.

The technology, known as Tortus, transcribes consultations automatically and produces summaries for medics to review.

Tortus uses so-called ambient voice technology, a mix of speech recognition and artificial intelligence, to pick up relevant medical information from a conversation, while filtering out background noise and irrelevant chat.

The study found the platform helped increase direct interaction between patients and clinicians by 23.5% during appointments.

It also reduced the overall length of appointments by 8.2%.

Health minister Stephen Kinnock, said: “This is exactly the kind of innovation we need as we work to build an NHS fit for the future and end hospital backlogs.

“By freeing up clinicians from administrative burden to spend more time with patients, we’re not just improving efficiency, we’re enhancing the human connection that sits at the heart of great healthcare.”

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Source: The Independent, 4 September 2025

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AI assesses Dutch mammograms better than radiologists

AI is detecting tumours more often and earlier in the Dutch breast cancer screening program. Those tumours can then be treated at an earlier stage. This has been demonstrated by researchers led by Radboud university medical centre in a study published in The Lancet Digital Health. The use of AI could reduce workload and save millions of euros annually.

Previous research in Sweden had already shown that AI detects breast cancer on mammograms more frequently than radiologists. Moreover, AI can reduce the workload for radiologists. Now, it appears that AI can also replace the second radiologist in the Dutch breast cancer screening programme. This even leads to the detection of more tumours - and at an earlier stage - which later turn out to be clinically significant.

Researchers, led by breast radiologist Ritse Mann of Radboudumc, analyzed 42,000 breast scans. These mammograms were taken as part of the Dutch screening program in the Utrecht region. Traditionally, two radiologists review these scans, as is standard practice in breast cancer screening. In this study, the researchers also evaluated the scans using AI developed by ScreenPoint Medical. Additionally, they followed the women whose scans were analysed for nearly four and a half years, with multiple scans available for many of them.

The study showed that one radiologist working with AI detects more tumours than two radiologists alone. Tumours are also identified earlier when AI is involved. "Sometimes the AI detects a tumour that the radiologists don’t yet recognize as such. We call this a false positive. But often that tumour appears in a later scan after all. Therefore the AI was right earlier," PhD candidate Suzanne van Winkel explains. "By the time the radiologist raises the alarm, it often concerns larger invasive tumours, which definitely need treatment, as early as possible."

In Sweden, AI is already being used to analyse screening mammograms. "They replace the second radiologist with AI. Only if the AI is uncertain does a second radiologist step in," Mann explains. "We see that radiologists work well with AI, which leads to more tumors being detected without a significant increase in unnecessary follow-up checks for women."

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Source: Digital Health News, 15 August 2025

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AI advancements in healthcare – two sides to the story

If ever there were an industry that could reap the benefits of artificial intelligence (AI), it is healthcare. The adoption of this technology to actually make medicine better is obvious. However, with this adoption comes a slew of ethical issues. With AI, there is always a human consequence beyond the tech storyline.

Neil Raden suggests there are two storylines to consider: the usefulness of the application, and the ultimate effect, often unintended, on people.

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Source: Diginomica, 19 September 2019

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AI 'outperforms' doctors diagnosing breast cancer

Artificial intelligence is more accurate than doctors in diagnosing breast cancer from mammograms, a study in the journal Nature suggests.

An international team, including researchers from Google Health and Imperial College London, designed and trained a computer model on X-ray images from nearly 29,000 women.

The algorithm outperformed six radiologists in reading mammograms. AI was still as good as two doctors working together.

Unlike humans, AI is tireless. Experts say it could improve detection.

Sara Hiom, director of cancer intelligence and early diagnosis at Cancer Research UK, told the BBC: "This is promising early research which suggests that in future it may be possible to make screening more accurate and efficient, which means less waiting and worrying for patients, and better outcomes."

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Source: BBC News, 2 January 2020

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