The man who ruined mathematics The incompleteness theorem is accepted as part of the mathematical canon today, but columnist Jacob Aron says it was a bombshell when Kurt Gödel first introduced it. Gödel’s seminal work directly contradicted one of the great minds of mathematics and limited the field forever

The incompleteness theorem is accepted as part of the mathematical canon today, but columnist Jacob Aron says it was a bombshell when Kurt Gödel first introduced it. Gödel’s seminal work directly contradicted one of the great minds of mathematics and limited the field forever

Ubuntu 26.04 LTS Beta Shows You There's Potential in the Stable Release Canonical has opened up Resolute Raccoon for testing, and the beta shows promise.

Ubuntu 26.04 LTS is arriving soon, and its beta release shows us what to expect.

itsfoss.com/news/ubuntu-...

Patient and Clinician Attitudes Toward #Mobile #Health Apps: Qualitative Study Background: #Mobile #Health (#mHealth) apps are widely available, and some have proven safe and effective for management of specific chronic conditions. Despite a high degree of interest, the potential of these technologies has yet to be realized. Patient and clinician attitudes are key factors that influence the adoption of #mHealth apps but remain poorly understood, particularly in the United States. Objective: This study aimed to identify both patient and clinician attitudes that can influence recommending and adopting #mHealth apps. Methods: Using well-established technology adoption and implementation science frameworks, this study included a deductive content analysis using a rapid qualitative analytic method. Semistructured interviews were conducted with patients and clinicians to identify technical and material, social and personal, and policy and organizational factors that can influence the recommendation or adoption of #mHealth apps. The interviews and data analysis were performed between September 2023 and August 2024. Results: Participants included 20 clinicians (n=12, 60% general internists) with a mean time in practice of 17 (SD 11.6) years, and 28 patients with a mean age of 59 (SD 12.1) years. A total of 7 categories related to patients’ and clinicians’ attitudes toward #mHealth apps emerged: (1) apps as tools to improve #Health by extending care, (2) the role of apps in enhancing the patient–clinician relationship, (3) the need for simplicity and efficiency in #App design, (4) the influence of prior experience with #mHealth apps, (5) comfort with technology, (6) recommendations from trusted sources, and (7) education and hands-on experience. Although similar factors were considered by patients and clinicians, their views about older adults’ interest and ability to use #mHealth apps differed. Conclusions: Understanding patient and clinician views about #mHealth apps provides critical insights for developing approaches to facilitate their use. These findings suggest patients and clinicians share similar views about the benefits of #mHealth apps. Nonetheless, clinicians’ perceptions about older patients’ interest and ability to use #mHealth apps may negatively impact recommendation of #mHealth apps and subsequent adoption by older adults.

New in JMIR mhealth: Patient and Clinician Attitudes Toward #Mobile #Health Apps: Qualitative Study

We are setting out to develop some new recommendations (TRIPOD-CODE) to provide guidance on reporting the availability and structure of code for predictive AI healthcare tools

Watch this space, and read the protocol here

link.springer.com/article/10.1...

#transparency #code #reproducibility

Smartphone addiction has negative impacts on student learning and overall academic performance.

📊 New JCMC study: In emergent critical cesarean delivery, intraoperative hypotension (MAP <65 mmHg)—across multiple metrics—is independently associated with postoperative AKI (~14%).

Highlights the importance of tight BP control

🔗 link.springer.com/article/10.1...

Assistant professor in Medical imaging with specialization in imaging technologies in vivo Subject field Medical imaging with specialization in imaging technologies in vivo. Subject description Medical imaging with specialization in imaging technologies in vivo refers to imaging of the livi

My colleagues are hiring an assistant professor at KTH in medical imaging. #jobs #vacancy #academia kth.varbi.com/en/what:job/...

Look Ma, No Hands: This Flying Umbrella Follows You Anywhere An engineer built a flying umbrella that uses computer vision to track your every move and hover overhead, hands-free, in the rain.

YouTuber I Build Stuff created a flying umbrella that uses computer vision to track your every move and hover overhead, hands-free, in the rain.

Is there an association between low #etCO2 and postoperative pulmonary complications? New from Nasa et al.

www.bjanaesthesia.org.uk/article/S0007-0912(25)00...

PREFER-IT: A transdisciplinary co-created framework to realise inclusive medical AI www.medrxiv.org/content/10.1101/2025.11....

Effectiveness of Physical Activity Interventions Utilizing Wearables and Smartphone Applications for Individuals with Cardiovascular Diseases and Stroke: A Systematic Review and Meta-analysis www.medrxiv.org/content/10.1101/2025.11....

Thousands of biomedical engineers came together in Copenhagen for #EMBC2025.

Watch the full recap on our YouTube channel and get ready for #EMBC2026 in Toronto! www.youtube.com/watch

Predicting #hypotension from arterial waveforms remains a challenge, even with the assistance from #AI. More work is required is required to develop reliable prediction models. www.bjanaesthesia.org/article/S0007-0912(25)00...

DermaDashboard: Bridging the Gap Between FHIR Standards and Clinical Usability Objective: The complexity of the Fast Healthcare Interoperability Resources (FHIR) standard limits its direct usability for clinicians despite its transformative potential in healthcare data management. To bridge this gap, we aimed to describe the development of an interactive dashboard enabling non-technical users to intuitively build and analyze #Oncologic #Patient cohorts. By leveraging FHIR, we aimed to enhance data accessibility and interoperability in clinical practice. Methods: DermaDashboard builds on a Structured Query Language (SQL) database using a relational FHIR model, which ensures data compliance with the FHIR schema. A materialized view was assembled and optimized performance by providing only relevant data. The user interface was built with Grafana and supports intuitive data exploration. We applied DermaDashboard to the use case of melanoma, demonstrating its utility in real-world #Oncologic cohort analyses. Results: DermaDashboard was successfully built and integrated into the clinical environment, identifying 3,949 melanoma #Patients and corresponding to 82,783 electronic health records. The primary FHIR resources used were #Patient, DiagnosticReport, and QuestionnaireResponse, and captured 54 data attributes, including demographics, histological classifications, genetic mutations, clinical and pathological staging, treatments, and procedures. Clinicians can filter the data using 29 variables to create specific subcohorts. The dashboard also enables operational insights by tracking annual trends in procedures and drug administrations. Conclusions: DermaDashboard enhances data accessibility for non-technical clinical users while showcasing the power of FHIR standardization in healthcare applications. By enabling #Oncological insights and identifying cohort discrepancies, it enhances both clinical decision-making and data quality.

New in JMIR Cancer: DermaDashboard: Bridging the Gap Between FHIR Standards and Clinical Usability

Up to ten postdoc fellows in technologies for digital transformation | Digital Futures The programme aims to provide networking opportunities and career development to enhance the future careers of successful postdoc fellows. Purpose Digital Futures postdoc fellowships aim to support ta...

Considering applying for a PostDoc in machine learning for patient data? Contact me for a project together with Karolinska University Hospital and submitting an application to KTH's DigitalFutures initiative!

#hiring #postdoc #jobs #phd #engineering

www.digitalfutures.kth.se/call/up-to-t...

Did you also check how quick HR measurements respond to changes in HR or just steady state measurements?

Student thesis that I supervised is published: Automated Dietary Analysis Using Computer Vision and Large Language Models: An iOS Prototype urn.kb.se/resolve?urn=...

Security researchers located 37 separate “easy to exploit” vulnerabilities in #NASA’s core Flight System, which would have enabled them to hack into satellites. It’s time for the #space industry to up its #cybersecurity game.
spectrum.ieee.org/satellite-ha...

An artificial intelligence (#AI)–Based Framework for Predicting Emergency Department Overcrowding: Development and Evaluation Study Background: Emergency department (ED) overcrowding remains a critical challenge, leading to delays in #patient care and increased operational strain. Current hospital management strategies often rely on reactive decision-making, addressing congestion only after it occurs. However, effective #patient flow management requires early identification of overcrowding risks to allow timely interventions. Machine learning (ML)–based predictive modeling offers a solution by forecasting key #patient flow measures, such as waiting count, enabling proactive resource allocation and improved hospital efficiency. Objective: The aim of this study is to develop ML models that predict ED waiting room occupancy (waiting count) at 2 temporal resolutions. The first approach is the hourly prediction model, which estimates the waiting count exactly 6 hours ahead at each prediction time (eg, a 1 PM prediction forecasts 7 PM). The second approach is the daily prediction model, which forecasts the average waiting count for the next 24-hour period (eg, a 5 PM prediction estimates the following day’s average). These predictive tools support resource allocation and help mitigate overcrowding by enabling proactive interventions before congestion occurs. Methods: Data from a partner hospital’s ED in the southeastern United States were used, integrating internal and external sources. Eleven different ML algorithms, ranging from traditional approaches to deep learning architectures, were systematically trained and evaluated on both hourly and daily predictions to determine the models that achieved the lowest prediction error. Experiments optimized feature combinations, and the best models were tested under high #patient volume and across different hours to assess temporal accuracy. Results: The best hourly prediction performance was achieved by time series vision transformer plus (TSiTPlus) with a mean absolute error (MAE) of 4.19 and a mean squared error (MSE) of 29.36. The overall hourly waiting count had a mean of 18.11 and a SD (σ) of 9.77. Prediction accuracy varied by time of day, with the lowest MAE at 11 PM (2.45) and the highest at 8 PM (5.45). Extreme case analysis at (mean + 1σ), (mean + 2σ), and (mean + 3σ) resulted in MAEs of 6.16, 10.16, and 15.59, respectively. For daily predictions, an explainable convolutional neural network plus (XCMPlus) achieved the best results with an MAE of 2.00 and a MSE of 6.64. The daily waiting count had a mean of 18.11 and a SD of 4.51. Both models outperformed traditional forecasting approaches across multiple evaluation metrics. Conclusions: The proposed prediction models effectively forecast ED waiting count at both hourly and daily intervals. The results demonstrate the value of integrating diverse data sources and applying advanced modeling techniques to support proactive resource allocation decisions. The implementation of these forecasting tools within hospital management systems has the potential to improve #patient flow and reduce overcrowding in emergency care settings. The code is available in our GitHub repository. Trial Registration:

New JMIR MedInform: An artificial intelligence (#AI)–Based Framework for Predicting Emergency Department Overcrowding: Development and Evaluation Study

The Economist is hiring a science and technology correspondent We’re looking for a writer to join us in London for 12 months

Are you an good writer with a passion for explaining the world around you?

We are looking for a science and technology correspondent based in our London office. Experience in journalism is not required. Apply here by September 28th:

⚠️Cardiovascular diseases.
⚠️Cancer.
⚠️Chronic respiratory diseases.
⚠️Diabetes.

They are silent and deadly.

Every year these diseases claim millions of lives.

Bold policies & healthier environments can stop these #SilentKillers in their tracks.

Change is within our reach 👉 bit.ly/UNGAHLM4 #UNGA

Towards reliable prediction of intraoperative hypotension: a cross-center evaluation of deep learning-based and MAP-derived methods - Journal of Clinical Monitoring and Computing Intraoperative hypotension (IOH) is associated with an increased risk of heart and kidney complications. Although AI tools aim to predict IOH, their real-world reliability is often overstated due to b...

Our latest research 🧪 has now been published in the Journal of Clinical Monitoring and Computing! 🎉

Title: Towards reliable prediction of intraoperative hypotension: a cross-center evaluation of deep learning-based and MAP-derived methods

link.springer.com/article/10.1...

When typing becomes tracking: Study reveals widespread silent keystroke interception - Help Net Security Researchers reveal website keystroke tracking that captures what users type, even without form submission, raising privacy concerns.

Don't type in website forms if you do not plan to submit! Or accidentally type a password in the wrong box. Then, your data may end where not intended! #Web #Security www.helpnetsecurity.com/2025/09/11/w...

World’s Best Smart Hospitals 2026 Smart hospitals utilize advanced technology including AI and automation to improve patient care and streamline workflow.

Karolinska University Hospital on place 11 on Newsweek's list over smartest hospital. AI being one major category. Great to hear that when I collaborate with Karolinska on several AI projects.
rankings.newsweek.com/worlds-best-...

KTH Center for Sports Engineering invites to online seminars on the latest research and developments in engineering in sports.

The topic for the first seminar will be AI in Sports (to be held tomorrow Thursday at 17:00 CEST over Zoom). See this link:
www.kth.se/sports-engin... #sporttech #ML #AI 🧪

”The success of [remote patient monitoring] depends less on the technology itself and more on program design, including targeting high-risk patients and having a responsive clinical team.”

Do heart rate monitors reflect your instantaneous rate during intense workouts? How well do these devices keep up when your heart rate spikes or drops suddenly—like during sprints, interval training, or recovery? The delay can be substantial it turns out! #SportTech www.kth.se/sports-engin...

The StatistiCal analysis and repOrting of cardiac output Method comPARison studiEs (COMPARE) statement provides a framework for designing, performing, and reporting cardiac output method comparison studies. Read the special article by Saugel et al.: ow.ly/pL8v50WEKnm

Evaluating a Customized Version of ChatGPT for Systematic Review Data Extraction in Health Research: Development and #usability Study Background: Systematic reviews are essential for synthesising research in health sciences, yet they are resource-intensive and prone to human error. The data extraction phase, where key details of studies are identified and recorded in a systematic manner, may benefit from the application of automation processes. Recent advancements in artificial intelligence (#AI) (AI), specifically Large Language Models (LLMs) like ChatGPT, may streamline this process. Objective: This study aims to develop and evaluate a custom Generative Pre-Training Transformer (GPT), named Systematic Review Extractor Pro, for automating the data extraction phase of systematic reviews in health research Methods: OpenAI's GPT Builder was used to create a GPT tailored to extract information from academic manuscripts. The Role, Instruction, Steps, End goal, and Narrowing (RISEN) framework was used to inform prompt engineering for the GPT. A sample of 20 studies across two distinct systematic reviews was used to evaluate the GPT's performance in extraction. Agreement rates between the GPT outputs and human reviewers were calculated for each study subsection. Results: Mean time for human extraction was 36 minutes per study, compared to 26.6 seconds for the GPT plus 13 minutes of human review. The GPT demonstrated high overall agreement rates with human reviewers, achieving 91.45% for review 1 and 89.31% for review 2. It was particularly accurate in extracting study (review 1: 95.25; review 2: 90.83%) and participant (review 1: 95.03%; review 2: 90.00%) characteristics, with lower performance observed in more complex areas such as methodological characteristics (87.07%) and statistical results (77.50%). The GPT correct when the human reviewer was incorrect in 14 instances (3.25%) in review 1 and four instances (1.16% in review 2). Conclusions: The custom GPT significantly reduced extraction time and shows evidence that it can extract data with high accuracy, particularly participant and study characteristics. It was most effective in extracting information such as study and participant characteristics. This tool may offer a viable option for researchers seeking to reduce resource demands during the extraction phase, though more research is needed to evaluate test-retest reliability, performance across broader review types, and accuracy extracting statistical data. The tool in the current study has been made open access.

JMIR Formative Res: Evaluating a Customized Version of ChatGPT for Systematic Review Data Extraction in Health Research: Development and #usability Study #ChatGPT #HealthResearch #SystematicReview #AI #DataExtraction