Federated data access and federated learning: improved data sharing, AI model development, and learning in intensive care

The imperative of diversity and equity for the adoption of responsible AI in healthcare

Artificial Intelligence (AI) in healthcare holds transformative potential but faces critical challenges in ethical accountability and systemic inequities. Biases in AI models, such as lower diagnosis rates for Black women or gender stereotyping in Large Language Models, highlight the urgent need to address historical and structural inequalities in data and development processes. Disparities in clinical trials and datasets, often skewed toward high-income, English-speaking regions, amplify these issues. Moreover, the underrepresentation of marginalized groups among AI developers and researchers exacerbates these challenges. To ensure equitable AI, diverse data collection, federated data-sharing frameworks, and bias-correction techniques are essential. Structural initiatives, such as fairness audits, transparent AI model development processes, and early registration of clinical AI models, alongside inclusive global collaborations like TRAIN-Europe and CHAI, can drive responsible AI adoption. Prioritizing diversity in datasets and among developers and researchers, as well as implementing transparent governance will foster AI systems that uphold ethical principles and deliver equitable healthcare outcomes globally.

AI-powered drug discovery for neglected diseases: accelerating public health solutions in the developing world

The emergence of artificial intelligence (AI) in drug discovery represents a transformative development in addressing neglected diseases, particularly in the context of the developing world. Neglected diseases, often overlooked by traditional pharmaceutical research due to limited commercial profitability, pose significant public health challenges in low- and middle-income countries. AI-powered drug discovery offers a promising solution by accelerating the identification of potential drug candidates, optimising the drug development process, and reducing the time and cost associated with bringing new treatments to market. However, while AI shows promise, many of its applications are still in their early stages and require human validation to ensure the accuracy and reliability of predictions. Additionally, AI models are limited by the availability of high-quality data, which is often sparse in regions where neglected diseases are most prevalent. This viewpoint explores the application of AI in drug discovery for neglected diseases, examining its current impact, related ethical considerations, and the broader implications for public health in the developing world. It also highlights the challenges and opportunities presented by AI in this context, emphasising the need for ongoing research, ethical oversight, and collaboration between public health stakeholders to fully realise its potential in transforming global health outcomes.

Evolving concepts in intracranial pressure monitoring - from traditional monitoring to precision medicine

A wide range of acute brain injuries, including both traumatic and non-traumatic causes, can result in elevated intracranial pressure (ICP), which in turn can cause further secondary injury to the brain, initiating a vicious cascade of propagating injury. Elevated ICP is therefore a neurological injury that requires intensive monitoring and time-sensitive interventions. Patients at high risk for developing elevated ICP undergo placement of invasive ICP monitors including external ventricular drains, intraparenchymal ICP monitors, and lumbar drains. These monitors all generate an ICP waveform, but each has its own unique caveats in monitoring and accuracy. Current ICP monitoring and management clinical guidelines focus on the mean ICP derived from the ICP waveform, with standard thresholds of treating ICP greater than 20 ​mmHg or 22 ​mmHg applied broadly to a wide range of patients. However, this one-size fits all approach has been criticized and there is a need to develop personalized, evidence-based and possibly multi-factorial precision-medicine based approaches to the problem. This paper provides historical and physiological context to the problem of elevated ICP, provides an overview of the challenges of the current paradigm of ICP management strategies, and discusses advances in ICP waveform analysis, emerging non-invasive ICP monitoring techniques, and applications of machine learning to create predictive algorithms.