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Ask the editor: 60 seconds with Dr Felix Holl

BMJ Group recently launched BMJ Connections Digital Health & AI, an open-access journal that sits alongside BMJ Digital Health & AI. Both journals publish original research, but the Connections journ …

Mobile-accessible deep learning-based self-assessment tool for measles screening in low-resource settings

What is already known on this topic Measles is highly contagious and poses serious health risks, particularly to young children. Global vaccination coverage declined in 2023, with only 83% receiving t …

Priorities for artificial intelligence education: clinicians’ perspectives

Clinicians are most likely to be motivated to learn about AI framed around its liability implications and determining appropriate confidence in AI algorithms, as these are perceived as important and c …

The impact of skin tone on performance of pulse oximeters used by NHS England COVID Oximetry @home scheme: measurement and diagnostic accuracy study

Arterial haemoglobin oxygen saturation (SaO2) indicates the fraction of oxygenated haemoglobin, relative to total haemoglobin, in arterial blood and provides a useful indicator of hypoxaemia (low bloo …

Déjà vu in healthcare AI: lessons from the world’s pioneer AI clinical decision support system

Recent advances in artificial intelligence (AI) have renewed interest in the possibility of computers assisting, or even replacing, doctors in making clinical decisions. However, computerised clinical …

Impact of acoustic and informational noise on AI-generated clinical summaries

The CAIS generally excelled at capturing a doctor-patient consultation and producing accurate clinical summaries; however, error rate (especially omissions) increased notably when acoustic noise was i …

Clinical AI Scribes in primary care: accuracy, error severity and implications for clinical practice

The CAISs demonstrate high levels of summarisation accuracy. However, there is great disparity between the currently available CAIS products and, while some perform well, none are perfect. Clinicians …

Predicting cardiovascular events from routine mammograms using machine learning

A deep learning algorithm based on only mammographic features and age predicted cardiovascular risk with performance comparable to traditional cardiovascular risk equations. Risk assessments based on …

Early clinical evaluation of a machine-learning system for risk prediction of trauma-induced coagulopathy in the prehospital setting

Early after injury, an ML system performs well compared with expert prehospital clinicians in the prediction of TIC and blood transfusion. The study suggests that ML systems may augment clinical risk …

“How long until I am seen, doc?” Modelling paediatric emergency department waiting times to make personalised predictions

Tailored models created using routine data can be used to give individualised predictions for wait times in paediatric ED, which could be given to patients with the aim of managing expectations and im …

Transforming women’s health through innovation

This BMJ Collection, developed in partnership with Bill and Melinda Gates Foundation, consists of 11 papers. It provides analysis and commentary on the growing global women's health innovation movemen …

Ask our Chief Technology Officer: Ian Mulvany

Ian Mulvany, chief technology officer at BMJ Group, is convinced that AI will transform processes related to the creation of knowledge, particularly academic papers. Wiley on AI recently interviewed M …

Smartphone barrier: uncovering the digital divide in mHealth prevention among disadvantaged middle-aged and older-aged UK communities

People with low socioeconomic position (SEP) are under-represented in research1 2 leading not only to reduced generalisability of findings to these groups, but also to participation and retention bias …

Can generative AI assess PTSD? A clinical validation study of transcribed and direct audio input modalities

Artificial intelligence (AI) has become increasingly prevalent across multiple domains.1 2 In healthcare, generative AI has been evaluated for its potential in clinical applications.3 4 Example applic …

Impact of the Flo Cycle Tracking App on menstrual knowledge and health in low-income and middle-income countries: a longitudinal study

Over two billion people menstruate worldwide, and many lack the resources, social support and accurate information required to manage their menstrual health and hygiene (MHH) safely and with dignity.1 …

Facilitating innovation in healthcare: insights from the frontline of adoption

Innovation has long shaped healthcare but the art of adopting innovation remains a challenge. In the UK’s National Health Service (NHS), a system founded on the principles of continuous improvement an …

Unintended consequences of using ambient scribes in general practice

Abi Eccles and colleagues argue that better evidence is crucial to ensure widespread adoption of ambient scribes does not compromise quality of care Ambient scribes have rapidly attracted internationa …

Ask our researcher: Fiona Mckenzie on the unexpected drivers of innovation

Original Research: Facilitating innovation in healthcare: insights from the front-line of adoption. A recent study published in BMJ Innovations examined the factors that drive the adoption and scaling …

This year is unleashing a series of devastating blows to women’s health worldwide. Cuts to foreign aid. Denial of abortion access and reproductive autonomy. Targeted attacks on hospitals, including ma …

Reimagining women’s health is a global imperative

Imagine a world where women and girls do not just survive—they thrive. A world where women live longer, healthier lives because of more rigorous and inclusive research and policies. Where health syste …

Ask the editor: 60 seconds with Dr Felix Holl

Mobile-accessible deep learning-based self-assessment tool for measles screening in low-resource settings

Priorities for artificial intelligence education: clinicians’ perspectives

The impact of skin tone on performance of pulse oximeters used by NHS England COVID Oximetry @home scheme: measurement and diagnostic accuracy study

Déjà vu in healthcare AI: lessons from the world’s pioneer AI clinical decision support system

Impact of acoustic and informational noise on AI-generated clinical summaries

Clinical AI Scribes in primary care: accuracy, error severity and implications for clinical practice

Predicting cardiovascular events from routine mammograms using machine learning

Early clinical evaluation of a machine-learning system for risk prediction of trauma-induced coagulopathy in the prehospital setting

“How long until I am seen, doc?” Modelling paediatric emergency department waiting times to make personalised predictions

Transforming women’s health through innovation

Ask our Chief Technology Officer: Ian Mulvany

Smartphone barrier: uncovering the digital divide in mHealth prevention among disadvantaged middle-aged and older-aged UK communities

Can generative AI assess PTSD? A clinical validation study of transcribed and direct audio input modalities

Impact of the Flo Cycle Tracking App on menstrual knowledge and health in low-income and middle-income countries: a longitudinal study

Facilitating innovation in healthcare: insights from the frontline of adoption

Unintended consequences of using ambient scribes in general practice

Ask our researcher: Fiona Mckenzie on the unexpected drivers of innovation

Upending women’s health

Reimagining women’s health is a global imperative

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