Category: IT in Healthcare

Categories : IT in HealthcareTags :

Real World Test of AI Clinical Support Tool Improved Clinician Decisions

On By ModernMedia

Trial did not show statistically significant difference for patient outcomes but helped clinicians improve quality of notes and recommendations

A large real-world clinical trial has found that a generative AI-powered support tool used to support frontline clinicians was safe and improved the quality of clinical decision-making but did not significantly change short-term patient outcomes.

The study, published today in Nature Medicine is one of the first randomised controlled trials worldwide to test whether generative AI can improve patient-level outcomes, rather than just clinician performance or simulated cases.

The trial involved more than 9600 patients attending 16 primary care clinics in Kenya, and was delivered by experts at the University of Birmingham supported by the National Institute for Health and Care Research (NIHR) Biomedical Research Centre: Birmingham.

What this study shows is that AI can be integrated safely into real clinical workflows, without undermining patient trust or clinician autonomy – which is a critical foundation for any future impact.

Alastair Denniston, Chair of Regulatory Science and Innovation

Clinicians were randomly assigned to use an electronic medical record system with or without an integrated AI consult tool that provided real-time diagnostic and treatment suggestions. The AI system, known as ‘AI Consult’, was a large language model–based clinical decision support tool embedded directly within the existing electronic medical record system.

During consultations, the tool worked in the background by:

  • Analysing information entered by the clinician into the medical record
  • Generating context‑specific diagnostic and treatment suggestions, aligned with Kenyan national clinical guidelines
  • Flagging potential concerns using a simple colour‑coded alert system (green, yellow or red)

Clinicians retained full autonomy; they were not required to follow the AI’s advice, and retained responsibility for all diagnosis, prescribing and referral decisions. The AI interface was not visible to patients, helping preserve normal patient–clinician interaction.

Senior author Professor Bilal Mateen, Honorary Professor of Machine Learning for Health at the University of Birmingham, and Chief AI Officer at PATH, said: “This is one of the first studies to rigorously ask the hardest question about AI in healthcare: whether it actually improves outcomes for patients.

“What we found is reassuring but also sobering. The technology appears safe and clearly improves aspects of clinical decision-making, but translating those gains into measurable patient benefit is much more challenging, particularly in everyday primary care.”

Serious outcomes such as hospitalisation or death are rare in primary care, meaning extremely large studies – potentially involving more than 100 000 patients – would be needed to detect modest effects.

Professor Alastair Denniston, co-author, Professor of Regulatory Science and Innovation at the University of Birmingham and lead for health data research at the NIHR Biomedical Research Centre: Birmingham, said: “A large part of primary care is to deal with common conditions, including those that are self-limiting, where many patients require low levels of healthcare intervention. In that context, even meaningful improvements in clinical reasoning may only result in small changes in patient outcomes that are very difficult to measure.

“What this study shows is that AI can be integrated safely into real clinical workflows, without undermining patient trust or clinician autonomy – which is a critical foundation for any future impact.”

Findings: safety, quality and costs

Researchers found no statistically significant difference in treatment failure within 14 days between patients seen with AI-supported care and those receiving standard care (2.2% vs 2.0%). The study found no evidence of harm, with similar rates of hospitalisation and death in both groups.

While the AI tool did not produce measurable improvements in short-term patient outcomes, it significantly improved the quality of clinical documentation and treatment planning, as assessed by an independent panel of experienced clinicians who were blinded to whether AI had been used.

Patient satisfaction was the same in both groups, suggesting that AI support did not alter patients’ experience of care.

The study also found that, although overall antibiotic prescribing rates were similar, antibiotic‑related costs were lower in the AI‑supported group, due to more cost-conscious prescribing choices.

Although the trial was conducted in Kenya, the researchers emphasise that the findings have global relevance, including for high-income health systems.

Professor Richard Riley, Professor of Biostatistics at the University of Birmingham and senior author, said: “Robust trials like this are so important to establish the real impact of using AI in practice. They help set realistic expectations of what AI can actually contribute within existing care pathways, and helps guide where future investment and research effort should be focused. Generalisability of our findings to higher-income settings, where baseline standards of care are already high, needs to be evaluated.”

Source: The University of Birmingham

Categories : IT in HealthcareTags :

Incorrect AI Advice is a Blind Spot – Even for Doctors

On By ModernMedia

 New study highlights potential challenges for using automated tools in healthcare

Photo by Accuray on Unsplash

In experiments in which physicians made decisions about treating hypothetical patients, the physicians tended to trust incorrect advice presented as being generated by artificial intelligence (AI), even after given the opportunity to notice that patient recovery data contradicted the recommendations. Aranzazu Vinas of the University of the Basque Country, Spain, and colleagues present these findings in the open-access journal PLOS Digital Health. 

AI systems can help physicians categorise patients according to their different care needs, such as whether a patient is more or less likely to benefit from a certain treatment. Since these systems are not perfect, they are meant to be used as suggestions, with potential errors caught and corrected by physicians.

 Prior research has shown that, in general, people struggle to notice and correct mistakes made by AI. To explore how this challenge may extend to physicians, Vinas and colleagues analysed data from 223 physicians who anonymously participated in online experiments.

 The physicians were asked to imagine they had the option to treat patients for a rare disease using a not-yet-proven treatment still under development. They were told that an AI system had identified which patients were more or less likely to benefit from the treatment. The physicians then chose which patients to treat, and after being presented with data on patient recovery, rated their perceptions of how reliable the AI was.

 Crucially, the actual effectiveness of the hypothetical treatment did not align with the AI recommendations. In one experiment, the treatment was equally moderately effective for all patients, and in a second experiment, it was equally ineffective for all.

 However, in both experiments, the physicians tended to rate the AI system as reliable and apparently did not use the patient recovery data to conclude that the AI recommendations were incorrect. In the second experiment, the physicians did not realise that the treatment was entirely ineffective.

These findings highlight potential challenges for incorporating AI-based classification into healthcare. Future research could build on this study, such as by developing and testing strategies and protocols that could increase human critical thinking and detection of AI errors, in order to maximize the benefits of the human-AI collaboration while minimising potential errors.”

Lead author Aranzazu Vinas notes: ” In both experiments, physicians mostly trusted the AI’s classifications and had trouble learning from the feedback. Furthermore, in the second experiment, professionals did not notice that the treatment was completely ineffective.”

 Co-author Helena Matute adds, “People tend to say that there is always a human controlling the algorithm, but our experiments show that doctors (as well as anyone else) have problems in learning from the available evidence when it contradicts the suggestions of an algorithm.”

Co-author Fernando Blanco summarizes: “It is important to investigate the errors that humans (including doctors) make when working with algorithms, in order to learn how to minimize the problems that arise from them.”

Press Preview: https://plos.io/4wjPxSs

In your coverage please use this URL to provide access to the freely available article in PLOS Digital Health: https://plos.io/4blGKHA

Contact: Aranzazu Vinas, aranzazu.vinas@ehu.eus

Image Caption: Doctors working on an AI-support system

Image Credit: Photo by Accuray on Unsplash. Free to share under the Unsplash license.

High-Resolution Image Link: https://unsplash.com/photos/a-few-men-looking-at-a-computer-screen-S34fEzWT6eE

Categories : IT in HealthcareTags :

AI Mistakes Can Cost Doctors Time when Writing to Patients

On By ModernMedia

Errors and irrelevant details mean physicians may spend more time editing AI-drafted responses than it would take to write them, a large study of an online patient portal shows

Photo by National Cancer Institute on Unsplash

Artificial intelligence is spreading rapidly in health care, with the goal of streamlining critical but onerous clerical tasks such as note-taking and charting so that physicians and nurses can devote more time to patients. But even when AI can free up doctors to correspond with patients, it may fall short in helping them do it by introducing errors and extraneous details into their messages, according to a new Dartmouth study presented at the 2026 Annual Meeting of the Association for Computational Linguistics and published in the conference proceedings. 

The result is that physicians may spend more time editing responses than it would’ve taken to write them, the researchers report.

“We find that AI can sound like a doctor but not think like one,” says Sarah Preum, an assistant professor of computer science and the study’s co-corresponding author with Parker Seegmiller, a graduate researcher in Preum’s PersistLab at Dartmouth. 

The researchers conducted the first large-scale study of an online patient portal that uses AI to draft responses from physicians to patients. The team developed a tool that compares AI-generated replies to a dataset of real responses they developed with health care professionals from Dartmouth Health. 

They then analysed 146 000 conversations between 10 105 patients and their primary care physicians at the large rural health system, with data anonymised.

The researchers also used their tool to evaluate physician responses drafted by Claude, Gemini, and ChatGPT, as well as the three smaller commercial platforms, Llama, Aloe, and Qwen. 

“We find that AI can sound like a doctor but not think like one.” 

Sarah Preum, corresponding author and assistant professor of computer science

The team reports that AI-generated answers frequently misalign with what clinicians would actually write. This includes automated responses that are too long, don’t ask follow-up questions, and use irrelevant or inaccurate medical details.

“There are smaller studies that say, ‘Oh, AI is amazing,’ but we realised there is a gap in the existing literature of a large-scale evaluation of this technology,” Preum says. “We didn’t just want to measure a platform’s accuracy, but whether it actually helps with the workload, which in this case is measured by how much editing the physician is doing.” 

For example, the portal’s AI suggested telling a 32-year-old woman who is taking an acid reflux drug and was concerned about constant nausea that the medication might take some adjustment in diet. A physician replaced that by asking if there’s any chance she was pregnant. 

Even little changes can add up over hundreds or thousands of messages, Preum says. “You don’t want to integrate large language models into the workflow and just shift the bottleneck so that doctors are devoting their cognitive energy to playing AI janitor and fixing mistakes,” Preum says. “But if we’re not careful, that’s a likely outcome.”

The researchers show, however, that adapting AI to how individual physicians communicate can improve accuracy by 33% and reduce editing by 26%. 

“If message generation is really efficient and high quality, if it asks the right things, then it really has potential to improve efficiency,” says co-author Tim Burdick, an associate professor of community and family medicine in Dartmouth’s Geisel School of Medicine and a family medicine physician at Dartmouth Health. 

“I don’t foresee a time when the portal can respond to a patient without a clinician editing it first. But as we make the models better, we’ll be able to address portal messages much more quickly and with less mental energy,” Burdick says. 

The study shows that there are such things as “good” AI responses and provides a framework for implementing them into patient-physician portals, Preum says. These platforms are increasingly common among large health care systems and often customized, she says.

“That took us a long time to figure out, but if you’re trying to measure how effective this technology is, you need to define what a good response is,” she says. “We can only improve what we can measure and objectively evaluate.” 

The researchers created a technique called TADPOLE (Thematic Agentic Direct Preference Optimization for Learning Enhancement) that trains AI platforms using the hybrid model they constructed from physician- and AI-generated responses.

They plugged TADPOLE into the six commercial LLMs and found that drafted responses better matched physicians’ standards for precision and information quality. “That could save a busy clinician an hour or two of work a day,” Burdick says. 

Doctors and nurses today are inundated with messages from patients and caregivers who can write them online anytime, he says. An ongoing project between Burdick and the Preum Lab called PortalPal aims to streamline patient portals using AI, including by automating some steps in following up with patients to get more information.

“We’re still nowhere near the point of having clinicians removed from the workflow.”

Tim Burdick, co-author and associate professor of community and family medicine

Physicians who Burdick works with say that AI-generated drafts save about 25% of their time on shorter messages. “It’s easier to make small edits to an LLM-generated message than it is to write it from scratch,” he says. But longer drafts can include information that is not correct or accurate. 

“If you have to edit 75% of the message, you may be spending more time and energy on making changes than if you were to just write it from scratch,” Burdick says. “I would guess we need to get to where the physician is editing less than 30% of the content before it has substantial benefit.”

One advantage of AI’s verbosity is that it tends to be more empathetic and thorough than physicians crunched for time, the researchers find. For example, AI is more likely to tell a patient experiencing an upset stomach that it’s sorry to hear they’re feeling nauseated. 

This means AI could be used to help “nudge” doctors to show more understanding and care for the patient’s situation, or answer patient’s questions more effectively so that patients feel more heard, Preum says. The team produced sample responses such as showing empathy by praising patients for following a treatment plan (“You’ve been doing a great job with your tapering.”) or planning for changes in symptoms (“If you’re feeling dizzy, please call triage.”).

The researchers also find that 65% of all the portal messages they studied came from people over 55, with patients over 65 generating 24% of all messages. These figures suggest that patient portals in general should be designed to accommodate older people, Preum says.

Future work will study how much actual time doctors spend editing automated drafts. The team also plans to evaluate their training model TADPOLE from the user perspective, studying if and how it lightens a physician’s workload, and how doctors and patients rate its performance. 

“This is one of the first studies that uses real patient portal messages to establish a generative AI model. In that regard, it’s innovative and shows us that this is not a simple task,” Burdick says. “We’re still nowhere near the point of having clinicians removed from the workflow.”

Source: EurekAlert!

Categories : IT in Healthcare NeurologyTags :

Study Shows AI Can Help Clinicians Identify Brain Tumour Risks

On By ModernMedia

By Katelin Shaft

Mayo Clinic researchers and collaborators have shown that an artificial intelligence (AI) tool can analyse routine pathology slides to help clinicians classify meningiomas, the most common primary brain tumour in adults, and better understand a patient’s risk of tumour recurrence.

The study, published in The Lancet Digital Health, demonstrates that deep learning models can support the extraction of molecular and prognostic information from standard haematoxylin and eosin, or H&E, slides – the same type of tissue images already used in routine clinical care. These insights are typically obtained through DNA methylation profiling, an advanced genetic test which provides valuable diagnostic and prognostic information but can be costly, time-consuming and is unavailable in many hospitals.

“This is one of the many studies where we can harness the strength of digital pathology by capturing the last two decades of genomic and molecular knowledge into AI algorithms,” says Gelareh Zadeh, MD, PhD, chair of the Department of Neurologic Surgery at Mayo Clinic in Rochester and Chief Medical Officer for Mayo Clinic Platform.

Making advanced tumor insights more accessible

Meningiomas can vary widely in behaviour. Some grow slowly and may never return after treatment, while others are more aggressive and more likely to recur. Understanding that risk is critical for patients and care teams deciding whether additional treatment, such as radiation therapy, may be needed after surgery.

Molecular testing can help identify which tumours are more likely to recur and which may respond differently to treatment. But these tests require specialized technology and expertise, limiting access for many patients.

Using tissue samples, pathology images and clinical data from 672 patients, researchers developed and tested AI models designed to help identify patterns linked to a tumour’s biology. Drawing on multiple de-identified datasets, including data resources from Mayo Clinic Platform, the models supported classification of meningioma subtypes and recurrence risk prediction using standard pathology slides that are already part of routine patient care.

The findings suggest that, with further validation, AI-based tools could one day help clinicians obtain more detailed tumour information to inform patient care, without requiring every patient to undergo advanced genetic testing.

Helping guide treatment decisions

For patients with meningiomas, recurrence risk can influence follow-up care, imaging frequency and whether radiation therapy should be considered. The study found that AI-based predictions remained useful even after accounting for traditional clinical factors such as tumour grade, the extent to which surgery was able to remove the tumour and patient age.

Researchers also found that the AI models could identify patterns of tumour heterogeneity – differences within the same tumour – that may help explain why some tumours behave more aggressively or respond differently to treatment.

The researchers note that additional prospective studies are needed before the AI models can be used routinely in clinical care. Still, they say the findings lay the groundwork for more accessible, personalised care for patients with meningiomas – and potentially for similar AI approaches in other cancers.

As with any clinical decision-support tool, the researchers emphasise that these models would require rigorous evaluation, validation and ongoing physician oversight before being considered for routine care. “The aim is to make these algorithms readily and simply accessible for use globally, improving patient care across many healthcare settings,” says Dr Zadeh.

For a complete list of authors, disclosures and funding, review the publication.

Source: Mayo Clinic

Categories : Expert Opinion IT in HealthcareTags :

Opinion Piece: Healthtech is Only as Strong as the Hands that Shape It

On By ModernMedia

By Vishal Barapatre, Group Chief Technology Officer at In2IT Technologies

| 17 June 2026

Healthcare is investing heavily in technology, but outcomes do not always improve at the same rate or deliver the desired effect. The issue is rarely a lack of tools. More often, it comes down to the way those tools are designed, connected, and maintained. Health technology, often referred to as Healthtech, delivers real value not just when systems exist, but also when expert IT partners shape them to turn health data into meaningful, useful intelligence throughout the entire care journey.

In many healthcare environments, technology has accelerated rapidly over the past decade. Hospitals and clinics have introduced electronic medical records, diagnostic platforms, and telehealth systems, with enormous potential, the benefits are often uneven when systems operate in isolation or fail to align with the realities of clinical workflows. The difference between technology investment and measurable clinical improvement often lies in the design and integration that happens behind the scenes.

The promise of healthtech lives in the data

At its core, healthtech is about data. This includes how data is captured, stored, presented, and analysed to support better patient care. Every interaction between a patient and the healthcare system generates information that can guide more informed decisions. From recognising early signals for preventive care, tracking progress during rehabilitation, to ensuring complete and accurate information during operative procedures, effective use of data underpins every stage of the healthcare journey.

Yet data alone is not enough. Without systems designed to bring clarity to complexity, information becomes fragmented, inconsistent, and largely underused. This is where expert IT partners are essential. They do not just implement platforms; they create the right conditions for data to support better patient care.

Another challenge lies in the diversity of healthcare data sources. Clinical records, laboratory results, imaging systems, wearable devices, and patient engagement platforms, all generate valuable information. However, without thoughtful integration and governance, these data streams can quickly become disconnected. When aligned, they allow clinicians to see a more complete picture of a patient’s health, enabling earlier intervention and more personalised treatment decisions.

Intuition does not happen by accident

There is a growing expectation that healthtech should feel intuitive, where insights emerge naturally without creating additional friction in already demanding clinical environments. However, intuitive technology does not happen by chance. It results from thoughtful choices about structure, integration, and user experience.

What needs to be understood is that a system’s value is not determined by its technical features but by how well it fits into clinical workflows. The data must be available at the right time, in the right context, and in a way that supports judgment instead of overwhelming it. Without this insight and expertise, even the most advanced systems may become obstacles instead of assets.

This is particularly important in high-pressure healthcare environments where time is limited, and decisions are critical. If systems require excessive navigation, duplicate data entry, or complicated interfaces, clinicians may spend more time interacting with technology than with the patients. Well-designed systems quietly support decision-making rather than compete for attention, ensuring that technology strengthens clinical practice of disrupting it.

Continuity of care requires continuity of systems

To add, preventive, rehabilitative, and operative care are often treated as separate areas, yet they are part of a single patient journey. The true value of healthtech emerges when data flows smoothly across these stages, creating continuity instead of hand-offs.

This continuity does not happen on its own. It relies on systems that preserve data integrity over time, integrate seamlessly across different care settings, and evolve as patient needs evolve. Without it, technology investments risk becoming isolated solutions rather than truly transformative tools.

When healthcare providers can access consistent patient information across departments and care phases, they gain a more holistic understanding of health outcomes. This continuity helps reduce redundant tests, prevent information gaps, and support coordinated treatment plans. Over time, it contributes to a healthcare environment where patients’ experiences feel more connected and less fragmented.

Trust is built behind the scenes

To truly be transformative, healthcare must rely on trust between patients and clinicians, as well as between clinicians and the systems they depend on. Yet, this trust is fragile, as a single system failure, data inconsistency, or security issue can erode confidence across a healthcare facility.

Trust is built on reliability, resilience, and strong governance. Systems must perform well under pressure, safeguard sensitive information, and evolve safely over time. Although this foundation work often goes unnoticed, its impact is felt every time clinicians use technology with confidence and ease.

The growing digitisation of healthcare has also made cybersecurity and data protection essential pillars of trust. Healthcare data is among the most sensitive information an organisation can manage. Protecting it requires robust security architecture, continuous monitoring, and governance practices that evolve as threats emerge. When these safeguards are embedded into the system architecture, healthcare organisations can innovate confidently without compromising patient privacy.

The real differentiator is partnership, not platforms

As healthtech continues to evolve, access to tools will be less of a significant differentiator. What will matter more is how those tools are shaped, connected, and sustained. Technology alone cannot provide better care. It requires partners who understand both the technical and human aspects of healthcare.

This is where an IT partner can navigate and guide healthcare organisations through complexity, turning possibilities into practice and ambitions into results. Their role is not just supportive but foundational in demonstrating the true value of healthtech. The future of healthcare will belong not to those who adopt the most technology, but to those who build it wisely.

Categories : IT in HealthcareTags :

AI Language Models Struggle with Basic Hospital Data Tasks, Study Finds

On By ModernMedia

Nine leading AI models were tested on simple administrative queries drawn from real-world emergency department records—and most failed unless paired with code-generation tools.

A new study finds that large language models (LLMs), used with straightforward prompting, perform poorly on routine number-crunching tasks that hospital administrators depend on every day to track patients and allocate resources. The findings were published this week in the open-access journal PLOS Digital Health by Eyal Klang of the Icahn School of Medicine at Mount Sinai, New York, USA, and colleagues.

Hospitals rely on structured electronic health record (EHR) data to monitor patient counts and resources and to generate administrative reports. These tasks are currently handled by data analysts using programming languages, creating delays when staff need fast answers. AI tools known as large language models, such as GPT-4o and Llama, have been proposed to simplify that process.

In the new study, researchers evaluated nine leading LLMs on two basic administrative tasks—counting patients meeting a condition and filtering records based on multiple criteria—using data drawn from 50 000 real emergency department visits at the Mount Sinai Health System.

The researchers found that straightforward prompting—asking the model a plain question like “how many patients in this table were admitted?”—produced uniformly poor results across all models. Chain-of-thought reasoning, in which the model is prompted to show step-by-step work before giving an answer, offered only modest improvements that degraded sharply as table size increased. Even GPT-4o, the top-performing model, saw accuracy drop from roughly 95% on the smallest datasets to below 60% on larger ones under chain-of-thought conditions.

A tool-based approach—where models were asked to generate code that was then executed—substantially improved accuracy for the most capable models, with GPT-4o and Qwen-2.5-72B achieving near-perfect performance. However, distilled DeepSeek models, optimised for speed and efficiency, struggled even with this approach. One model, Llama-3.1-8B, failed to produce usable output in the majority of trials and was excluded from further analysis.

“Our findings indicate that without using a tool-based strategy, current LLMs are unsuitable for standalone use even on minimally complex administrative tasks in clinical settings,” says Benjamin Glicksberg. “Structured data tasks in clinical workflows will require agentic approaches that combine LLMs with code execution to ensure accuracy and consistency.”

Provided by PLOS

Categories : IT in HealthcareTags :

Much Medical Information Provided by Popular Chatbots is Inaccurate and Incomplete

On By ModernMedia

Half of answers to evidence based questions “somewhat” or “highly” problematic

A substantial amount of medical information provided by 5 popular chatbots is inaccurate and incomplete, with half of the answers to clear evidence based questions “somewhat” or “highly” problematic, show the results of a study published in the open access journal BMJ Open.

Continued deployment of these chatbots without public education and oversight risks amplifying misinformation, warn the researchers.

Generative AI chatbots have been rapidly adopted across research, education, business, marketing and medicine, with many people using them like search engines, including for everyday health and medical queries, explain the researchers.

To gauge the level of accuracy provided in areas of health and medicine already prone to misinformation, and therefore with consequences for everyday health behaviour, the researchers probed 5 publicly available and popular generative AI chatbots in February 2025: Gemini (Google); DeepSeek (High-Flyer); Meta AI (Meta); ChatGPT (OpenAI); and Grok (xAI).

Each chatbot was prompted with 10 open ended and closed questions in each of 5 categories of cancer, vaccines, stem cells, nutrition, and athletic performance. The prompts were designed to resemble common ‘information-seeking’ health and medical queries and misinformation tropes online and in academic discourse. 

And they were developed to ‘strain’ models towards misinformation or contraindicated advice—a strategy increasingly used for stress testing AI chatbots and picking up behavioural vulnerabilities, note the researchers.

Closed prompts required chatbots to provide pre-defined responses, often with one correct answer, that aligned with the scientific consensus. Open ended prompts typically required chatbots to generate multiple responses in list form.

Responses were categorised as non-, somewhat, or highly problematic, using objective pre-defined criteria. A problematic response was defined as one that could plausibly direct lay users to potentially ineffective treatment or come to harm if followed without professional guidance.

The information was scored for accuracy and completeness, and particular attention was given to whether a chatbot presented a false balance between science and non-science based claims, regardless of the strength of the evidence.

Each response was also graded on readability, ranging from whether it was written in easy, plain English, to difficult, academic language, using the Flesch Reading Ease score.

Half (50%) the responses were problematic: 30% were somewhat, and 20% were highly problematic. 

Prompt type was influential: open-ended prompts, for example, produced 40 highly problematic responses—significantly more than expected—and 51 non-problematic responses—significantly fewer than expected. The opposite was true of closed prompts.

While the quality of responses didn’t differ significantly among the 5 chatbots, Grok
generated significantly more highly problematic responses than would be expected (29/50; 58%). Gemini generated the fewest highly problematic responses and the most non-problematic ones.

The chatbots performed best in the area of vaccines and cancer, and worst in the area of stem cells, athletic performance, and nutrition. 

Answers were consistently expressed with confidence and certainty, with few caveats or disclaimers. Out of the total 250 questions, there were only two refusals to answer, both of which came from Meta AI in response to queries about anabolic steroids and alternative cancer treatments.

Reference quality was poor, with an average completeness score of 40%. Chatbot hallucinations and fabricated citations meant that no chatbot provided a fully accurate reference list. 

All readability scores were graded as ‘difficult’, equivalent in complexity to suitability for a college graduate.

The researchers acknowledge that they only assessed 5 chatbots and that commercial AI is rapidly evolving, so their findings might not be universally applicable. And not all real-world queries are deliberately adversarial, an approach they took which may have overstated the prevalence of problematic content.

Nevertheless, “Our findings regarding scientific accuracy, reference quality, and response readability highlight important behavioural limitations and the need to re-evaluate how AI chatbots are deployed in public-facing health and medical communication,” they point out. 

“By default, chatbots do not access real-time data but instead generate outputs by inferring statistical patterns from their training data and predicting likely word sequences. They do not reason or weigh evidence, nor are they able to make ethical or value-based judgments,” they explain.

“This behavioural limitation means that chatbots can reproduce authoritative-sounding
but potentially flawed responses.” 

The data chatbots draw on also includes Q&A forums and social media, and scientific content is typically limited to open access or publicly available articles, which comprise only 30–50% of published studies. While this enhances conversational fluency, it  may come at the cost of scientific accuracy, advise the researchers.

“As the use of AI chatbots continues to expand, our data highlight a need for public education, professional training, and regulatory oversight to ensure that generative AI supports, rather than erodes, public health,” they conclude.

Source: BMJ Group

Categories : IT in Healthcare Lab Tests and ImagingTags :

Deepfake X-Rays Fool Radiologists and AI

On By ModernMedia

Findings raise concerns about cybersecurity and diagnostic trust

Anatomy-matched real and GPT-4o-generated radiographs: (A) real and (B) GPT-4o-generated posteroanterior chest radiographs, (C) real and (D) GPT-4ogenerated lateral cervical spine radiographs, (E) real and (F) GPT-4o-generated posteroanterior hand radiographs, and (G) real and (H) GPT-4o-generated lateral lumbar spine radiographs. The pairs demonstrate that GPT-4o can produce radiographically plausible images across different anatomic regions.
https://doi.org/10.1148/radiol.252094 ©RSNA 2026

Neither radiologists nor multimodal large language models (LLMs) are able to easily distinguish AI-generated “deepfake” X-ray images from authentic ones, according to a study published in Radiology. The findings highlight the potential risks associated with AI-generated X-ray images, along with the need for tools and training to protect the integrity of medical images and prepare health care professionals to detect deepfakes.

The term “deepfake” refers to a video, photo, image or audio recording that appears real but has been created or manipulated using AI.

“Our study demonstrates that these deepfake X-rays are realistic enough to deceive radiologists, the most highly trained medical image specialists, even when they were aware that AI-generated images were present,” said lead study author Mickael Tordjman, MD, post-doctoral fellow, Icahn School of Medicine at Mount Sinai, New York. “This creates a high-stakes vulnerability for fraudulent litigation if, for example, a fabricated fracture could be indistinguishable from a real one. There is also a significant cybersecurity risk if hackers were to gain access to a hospital’s network and inject synthetic images to manipulate patient diagnoses or cause widespread clinical chaos by undermining the fundamental reliability of the digital medical record.”

Seventeen radiologists from 12 different centers in six countries (United States, France, Germany, Turkey, United Kingdom and United Arab Emirates) participated in the retrospective study. Their professional experience ranged from 0 to 40 years. Half of the 264 X-ray images in the study were authentic, and the other half were generated by AI. Radiologists were evaluated on two distinct image sets, with no overlapping between the datasets. The first dataset included real and ChatGPT-generated images of multiple anatomical regions. The second dataset included chest X-ray images—half authentic and the other half created by RoentGen, an open-source generative AI diffusion model developed by Stanford Medicine researchers.

When radiologist readers were unaware of the study’s true purpose, yet asked after ranking the technical quality of each ChatGPT image if they noticed anything unusual, only 41% spontaneously identified AI-generated images. After being informed that the dataset contained synthetic images, the radiologists’ mean accuracy in differentiating the real and synthetic X-rays was 75%.

Individual radiologist performance in accurately detecting the ChatGPT-generated images ranged from 58% to 92%. Similarly, the accuracy of four multimodal LLMs—GPT-4o (OpenAI), GPT-5 (OpenAI), Gemini 2.5 Pro (Google), and Llama 4 Maverick (Meta)—ranged from 57% to 85%. Even ChatGPT-4o, the model used to create the deepfakes, was unable to accurately detect all of them, though it identified the most by a considerable margin compared to Google and Meta LLMs.

Radiologist accuracy in detecting the RoentGen synthetic chest X-Rays ranged from 62% to 78% and the LLM models’ performance ranged from 52% to 89%.

There was no correlation between a radiologist’s years of experience and their accuracy in detecting synthetic X-ray images. However, musculoskeletal radiologists demonstrated significantly higher accuracy than other radiology subspecialists.

Spotting the Risks in Synthetic Imaging

“Deepfake medical images often look too perfect,” Dr. Tordjman said. “Bones are overly smooth, spines unnaturally straight, lungs overly symmetrical, blood vessel patterns excessively uniform, and fractures appear unusually clean and consistent, often limited to one side of the bone.”

Recommended solutions to clearly distinguish real and fake images and help prevent tampering include implementing advanced digital safeguards, such as invisible watermarks that embed ownership or identity data directly into the images and automatically attaching technologist-linked cryptographic signatures when the images are captured.

“We are potentially only seeing the tip of the iceberg,” Dr. Tordjman said. “The logical next step in this evolution is AI-generation of synthetic 3D images, such as CT and MRI. Establishing educational datasets and detection tools now is critical.”

The study’s authors have published a curated deepfake dataset with interactive quizzes for educational purposes.

For More Information

Access the Radiology study, “The Rise of Deepfake Medical Imaging: Radiologists’ Diagnostic Accuracy in Detecting ChatGPT-generated Radiographs,” and the related editorial, “The Democratization of Deceit: Seeing Is No Longer Believing.”

Source: Radiological Society of North America

Categories : IT in HealthcareTags :

The Next Leap for AI Scribes Provides Eyes in the Clinic

On By ModernMedia

Vision-enabled artificial intelligence (AI) medical scribes could increase the accuracy of patient notes and save valuable time for clinicians

The introduction of vision-enabled artificial intelligence (AI) to medical scribes – the recording devices used by doctors to document meetings with patients in real-time – could increase the accuracy of patient notes and save valuable time for clinicians.

A Flinders University study, published in npj Digital Medicine, has found that AI medical scribes already reduce some administrative work that takes time away from patients, but these devices have the capacity to do more when fitted with visual recording apparatus.

Researchers from Flinders’ College of Medicine and Public Health found that a vision-enabled AI scribe, employing a combination of Google’s Gemini model and Ray-Ban Meta smart glasses, substantially improved the documentation accuracy of pharmacist-patient consultations and reduced omissions and errors in clinical notes.

“AI scribes are already helping clinicians by listening to consultations, but healthcare involves far more than spoken words,” says research author Bradley Menz, an academic pharmacist in Flinders’ College of Medicine and Public Health.

“A lot of clinically important information is visual. Important visual cues during consultations include patients’ medicine containers, prescriptions and devices, as well as their body language. When an AI system can use both what it hears and what sees in these consultations, it captures more of the details that matter for patient care.”

In the study, 10 clinical pharmacists recorded 110 ‘mock’ medication-history interviews, which contained more than 100 different medicine containers, including tablets, capsules, injections and creams.

Researchers wore Meta AI Ray-Ban glasses to record the interview before passing the video footage through to the AI scribe, which was developed using Google’s Gemini AI model.

An AI scribe that analysed both video and audio achieved 98% accuracy, compared with 81 per cent  when the same system processed only audio information.

A significant benefit was capturing medication strength and form, which are crucial details for safe dosing. The AI scribe with video input captured this information 97% of the time, while audio-only recordings fell to 28%.

“This is an augmented tool, not a replacement for clinical judgement,” says Mr Menz. “The clinician still needs to review and sign off the document.

“The AI scribe can contain a verification step, take screenshots of medication packages, and generate a full spoken transcript, giving the health professional a much stronger basis for checking what the AI has produced.”

Senior author, Associate Professor Ashley Hopkins, says the study may point to the next stage of AI scribe usage in health care.

“AI scribes have gained traction because they reduce the burden of documentation and give clinicians more time with their patients. These findings suggest that the next step – when the scribe can see as well as hear – produces a more accurate and complete draft,” says Associate Professor Hopkins. “This means less time editing AI-documentation and even more time focusing on patient care.

“These findings suggest the next step may be that all scribe systems can interpret visual information as well as speech, which could open the door to wider clinical uses.”

The authors say the study has some limitation and underlines the need for human oversight and careful governance before these tools are adopted more broadly. The paper also highlights privacy, consent, data security and workflow integration as important issues that will need to be addressed as vision-enabled AI scribes move closer to practice.

Source: Flinders University

Categories : IT in HealthcareTags :

Healthcare Under Attack: Why Cybersecurity is now Critical Care

On By ModernMedia
Photo by Nahel Abdul on Unsplash

By Kerissa Varma, Microsoft Chief Security Advisor, Africa

Africa’s healthcare sector is facing a silent emergency. Many healthcare operators, facilities and doctors across Africa already grapple with the challenges of under-resourced environments, an uneven distribution of resources and massive demand for services. Now, healthcare administrators must turn their attention to a relatively new and extremely urgent concern. While doctors fight to save lives, cybercriminals are infiltrating hospitals, laboratories, and clinics, turning life-saving environments into digital battlegrounds.

A growing epidemic

World Health Organization director-general Tedros Adhanom Ghebreyesus noted that the digital transformation of healthcare, combined with the high value of health data, has made the sector a prime target for cybercriminals, commenting that “At best, these attacks cause disruption and financial loss. At worst, they undermine trust in the health systems on which people depend, and even cause patient harm and death.”

Recent attacks have exposed the fragility of Africa’s medical infrastructure. In May 2025, Mediclinic Southern Africa was hit by a cyber extortion attack, compromising sensitive HR data. Later in 2025, Lancet Laboratories faced a regulatory penalty for failing to notify patients about data breaches under South Africa’s POPIA law, while a ransomware strike on the National Health Laboratory Service disrupted blood test processing nationwide, delaying critical care for millions.

M-Tiba, a Kenyan digital health platform managed by CarePay and backed by Safaricom, suffered a significant cyberattack and data breach in late 2025, while earlier this year Pharmacie.ma, a Moroccan pharmaceutical platform, was reportedly the target of an alleged data leak incident that allegedly involved the unauthorised export of a customer database. And recent research indicates that Nigeria’s private healthcare sector is now one of the most targeted on the African continent, with attacks increasing at an alarming rate.

Many incidents also go unreported, as hospitals and healthcare facilities rarely disclose them publicly, yet these incidents are not isolated, with ransomware dominating the threat landscape. Africa’s healthcare sector is heavily targeted by cybercriminals, with healthcare organisations facing an average of 3575 weekly attacks in 2025, a 38% surge from the previous year, with encryption of patient data, temporary loss of access to hospital systems and the risk of data appearing on the dark web cited as potential impacts.

Why healthcare is a prime target

The healthcare industry in Africa, particularly in the public sector, is working with legacy systems, fragmented infrastructure, and underfunded IT teams, all of which combine to make the sector an easy target for unscrupulous bad actors.

Many medical institutions are adopting open-source AI tools for diagnostics and patient management. While cost-effective, these platforms often lack enterprise-grade security, leaving sensitive data exposed. Combined with fragmented storage of paper and electronic patient records – often unencrypted and scattered across multiple systems – the risk of breaches multiplies.

Hospitals and healthcare facilities cannot afford downtime. Every minute offline risks lives, making them more likely to pay ransoms in an attempt to regain control of their systems. Cyber insurers  indicate that in 2 of 5 cases of a ransom being paid, data and operations still cannot be recovered. Additionally, in instances where some or all of the seized data is recovered after paying a ransom, the attacker goes on to request further payments.

Medical records are also a premium target for cybercriminals. In the USA, researchers found that patient records, insurance details, and research data fetch premium prices on the dark web – up to 10 times higher than financial data, according to cybersecurity analysts. A single stolen medical record can sell for $260–$310, compared to $30–$50 for a credit card, because unlike credit cards, medical records never expire and medical information cannot be easily changed, making it useful for years. Medical records frequently include personal identifiers, insurance details, and sometimes biometric data, enabling identity theft and fraud, while criminals use medical data for fake insurance claims, prescription fraud, and targeted scams. Microsoft believes cybersecurity needs to be embedded into every technology implementation. This should be a key priority, especially with sensitive medical data and operations.

How healthcare can use modern technology safely

As Africa’s healthcare systems digitise and embrace AI, protecting the digital lifeline must become as critical as protecting the physical one. Key steps can secure healthcare organisations and facilities like laboratories and diagnostic services’ systems.

Include cybersecurity in your resilience planning

Medical professionals and healthcare facilities often prioritise the resilience of physical capabilities. Power backups, multiple devices should equipment fail, and a standby roster in the event of a practitioner being unavailable are all practices that save lives. Equally cybersecurity and safeguarding online systems needs to be built into the overall resilience planning of medical facilities and services.

Investing in cybersecurity technology that can quickly identify and contain attacker activity before it leads to system downtime or data theft can save lives. Having a response plan that is practiced and maintained in the event of a cyber breach and ensuring strong data backups could mean the difference between a total failure of health services or a minor incident. Ensuring incident response plans are aligned with local compliance laws such as South Africa’s POPIA, and Kenya and Nigeria’s Data Protection Acts is critical for healthcare providers to meet both their resilience and compliance objectives.

Prepare for AI-driven attacks that are going to increase attacker speed and success

Threat actors are increasingly exploiting the interconnectedness of modern software ecosystems and operational structures to conduct malicious activity, so regular auditing of third-party integrations, especially those involving AI or cloud services, is critical.

Adversaries are using AI to scale and tailor operations, with AI-driven phishing being 4.5x more effective than traditional phishing. However, in equal measure, AI is transforming cyber defence – it automates response and containment, detects threats faster and more accurately, and identifies detection gaps and adapts to attacker behaviour. Healthcare organisations should invest in AI-driven threat detection for faster response and anomaly detection and must also take steps to secure AI models and data pipelines by implementing robust access controls, vulnerability scanning, and regular patching for open-source tools.

Remote and wider access to patient records requires strong identity practices

As both patients and medical professionals start accessing patient records digitally, strong means of identification, verification and authentication are critical. The Microsoft Digital Defense Report 2025 notes that the abuse of valid accounts is a frequent occurrence, with malicious actors gaining access to user credentials (usernames and passwords) and using them to infiltrate systems without triggering traditional security alerts. Therefore, organisations must deploy phishing-resistant multifactor authentication (MFA) and conditional access to strengthen user defences.

Invest in people and skills

People are at the heart of robust cybersecurity measures, so it is vital to train staff against common tactics such as phishing, which is the most common entry point for attackers, and apply role-based access controls for both clinical and research data to prevent privilege misuse.

Cybersecurity is no longer an IT issue – it’s a patient safety issue. Healthcare services and providers must treat digital resilience with the same urgency as infection control. By investing in comprehensive cybersecurity strategies and leveraging AI-powered defences, Africa’s healthcare sector can position itself as a crucial front line against emerging threats and help build stronger, more resilient digital ecosystems.