Latest Posts
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Ethics and governance of artificial intelligence for health: Guidance on large multi-modal models
Large multi-modal models (LMMs) are a fast-growing type of generative AI that can take in different types of data, such as text, images and video, and produce varied responses. The World Health Organization (WHO) has issued guidance on their use in health, with over 40 recommendations for governments, technology companies and healthcare providers. The guidance…
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AI-based diabetes care: risk prediction models and implementation concerns
AI is increasingly being used to predict a person’s risk of developing type 2 diabetes, which could help delay or prevent the disease through earlier, more personalised prevention. This editorial discusses a scoping review of 40 studies on AI models for diabetes risk prediction. It found that models combining electronic health records with other types of data,…
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UnitedHealth lawsuit over alleged AI-driven denials of care moves forward
In November 2023, the families of two deceased patients filed a proposed class action lawsuit in the US against UnitedHealth Group, one of the country’s largest health insurers, and its subsidiary naviHealth. They allege the…
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Fairness and bias correction in machine learning for depression prediction across four study populations
A study found that standard machine learning approaches often exhibit biased behaviours in predicting depression across different populations. It also demonstrated that both standard and novel post-hoc-bias mitigation techniques can effectively reduce unfair bias, though…
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Predicting cardiovascular events from routine mammograms using machine learning
Cardiovascular risk is often underassessed in women, and many women have screening mammograms in midlife, just when their risk of cardiovascular disease starts to rise. Researchers led by The George Institute for Global Health developed…
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Can AI fight sex and gender bias in healthcare?
AI is transforming medicine, but it is mostly developed by men and trained on data that prioritises men’s bodies and health needs. In this commentary, researchers from the George Institute for Global Health argue that this embeds sex and gender biases that can put women and nonbinary patients at risk. For example, natural language processing models used in psychiatry have shown significant gender biases, and tools that screen for suicide risk may make mistakes if trained mainly on text written by men, since men and women express distress differently. The authors also argue that AI could help reduce these inequities…
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Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations
AI systems now match expert performance in many medical imaging tasks, but there is growing concern that they may reflect and amplify human bias. This is especially worrying in the case of underdiagnosis, when AI wrongly labels a person who has a disease as healthy, which can delay their access to care. This study…
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New-Onset Illness Anxiety Disorder After Helicobacter Pylori Infection: A Case Report
In this interesting case study from 2024, a 21-year-old woman developed illness anxiety disorder (IAD), formerly known as hypochondria, following a Helicobacter pylori infection. In the case, even after the H. pylori infection was successfully treated,…
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Bias Mitigation in Primary Health Care Artificial Intelligence Models: Scoping Review
AI models in primary care can help identify quickly and accurately the people who should receive care, but they risk perpetuating or amplifying biases against diverse groups. This scoping review looked at the strategies and methods…
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Addressing AI Algorithmic Bias in Health Care
Biased AI algorithms can result in some groups of patients not receiving appropriate care, potentially leading to significant harm. In one example, an algorithm designed to identify patients who needed extra care used healthcare costs to…