Artificial intelligence (AI) is transforming health care across diagnostic imaging, clinical decision support, and personalized medicine. However, despite rapid technical advances, trust remains the principal bottleneck limiting clinical adoption. This report synthesizes insights from the second IEEE EMBS Workshop on AI and Healthcare, which centered on the multidimensional nature of trust in medical AI systems. Through keynote demonstrations of AI-enabled ultrasound and an interdisciplinary panel discussion spanning medicine, computational oncology, defense research, and scientific publishing, the workshop examined trust as a construct grounded not only in accuracy, but also in transparency, accountability, and interpretability. Audience polling and case-based discussions revealed that public and clinical confidence in AI is conditional and context-dependent, particularly in high-stakes decision environments. The workshop highlighted key tensions between performance and explainability, emphasized the need for explicit accountability frameworks, and underscored the importance of lifecycle oversight from data provenance to post-deployment validation. As AI becomes increasingly embedded in clinical workflows, its impact will depend less on algorithmic capability alone and more on its ability to earn and sustain trust. The discussions reinforce a critical conclusion: trustworthy healthcare AI requires validation, human-centered design, interdisciplinary collaboration, and sustained governance to evolve from promising technology to a reliable clinical partner.

The ongoing civil war in Myanmar (Burma) has severely disrupted the country's health care system, forcing widespread displacement and creating a critical shortage of medical care and prosthetic devices for refugees and migrants along the Thai border. Prohibitive costs, resource shortages, and movement constraints severely limit access to essential health care and functional prosthetics in this region. This article details a collaboration between Queen's University and the Burma Children Medical Fund (BCMF), a nongovernmental organization (NGO) that established a 3-D-printed prosthetic program in 2019 using open-source technology. Since 2023, this collaboration has facilitated a three-year student mobility program, sending researchers to Thailand to enhance BCMF's existing initiative by addressing technical limitations and improving design autonomy. Using the development of a short transradial (below-elbow) prosthetic as a case study for this collaborative framework, the team employed low-cost 3-D scanning and computer-aided design (CAD) to modify an open-source prosthetic model for better suspension and fit. Our approach prioritized local capacity building by training local staff in CAD to ensure design autonomy and implementing filament dryers to mitigate humidity-related printing errors. From 2019 to 2024, the program provided free prostheses to 76 individuals, including five recipients of the customized below-elbow design. Pilot evaluations using the Orthotics Prosthetics Users Survey (OPUS) indicated positive results, with recipients reporting high levels of functionality and satisfaction, and no reported adverse effects. Ultimately, this work demonstrates the feasibility of a framework for academic-NGO partnerships that leverages adaptable, open-source technology to deliver equitable prosthetic care in conflict-affected and resource-constrained settings.

When applied to physiological and behavioral data collected from wearable devices, artificial intelligence (AI) can identify patterns associated with a variety of conditions, including mental health events. Researchers are exploring applications such as the early detection of mental health disorders and personalized, real-time interventions. However, these technologies also come with technical challenges and ethical considerations.

Traditional procedures for diagnosing COVID-19 have mostly depended on invasive or resource-intensive technologies such as X-ray imaging, computed tomography (CT) scans, magnetic resonance imaging (MRI), and reverse transcription polymerase chain reaction (RT-PCR). Although these methods have therapeutic value, they are sometimes impractical for widespread screening, especially in settings with low resources or remote areas where access to highly skilled specialists and advanced technology is scarce. This work investigates noninvasive, AI-driven pipelines for COVID-19 detection that use cough and breath sounds as the primary inputs to address these issues. The proposed approach starts with sound acquisition and moves on to comprehensive feature extraction, focusing on image-based audio representations. The ability of various techniques, including scalograms, spectrograms, mel-spectrograms, chromagrams, wavelet spectrograms, cepstral analysis, gammatonegrams, power spectrograms, and short-time Fourier transform (STFT), to produce discriminative features that can either parallel or even enhance radiological modalities in AI-assisted systems is assessed.

The continuous positive airway pressure (CPAP) machine is a long-time staple in the treatment of obstructive sleep apnea (OSA), but not all patients can or want to use the pump-hose-and-mask apparatus. Innovators are responding with alternative neurostimulation devices to treat moderate to severe OSA.

A growing wave of new medical devices are helping to pull back the curtain on the human brain through neuromonitoring-the use of electroencephalography (EEG) and other real-time neurophysiological signals to record and analyze neural activity. Starting with the development of EEG in the 1930s, neuromonitoring evolved first into a behind-the-scenes scientific research and medical diagnostic tool. Neuromonitoring moved into the operating room starting in the late 1970s to help protect neural functions during delicate surgeries and now is expanding to use artificial intelligence algorithms as a digital "co-pilot" to help spot potential issues faster. With the advent of smaller and less expensive neuromonitoring tools, the process is moving into other clinical areas, including intensive care units and emergency departments. Companies like Ceribell and NeuroBell are alerting clinicians to "silent" seizures that might otherwise cause lasting brain damage, while other start-ups such as Kernel are developing research tools that may help guide treatments for depression and addiction.

Pulse's Industry Corner Live featured a dynamic live Q&A session between IEEE Pulse Editor-in-Chief Chad Andresen and Dr. Tom Oxley, CEO and co-founder of Synchron, a leader in minimally invasive brain-computer interface (BCI) technology. The discussion explored the intersection of neurotechnology, artificial intelligence, and the evolving landscape of entrepreneurship in the MedTech sector. Dr. Oxley shared insights into Synchron's pioneering work with endovascular BCIs, offering a less invasive alternative to traditional neurosurgical approaches, and how this technology is reshaping the possibilities for restoring communication in patients with paralysis. The conversation delved into the growing role of AI in decoding neural signals and driving clinical translation, while also addressing the regulatory, financial, and ethical challenges faced by entrepreneurs in the neurotechnology space. With candid reflections on his journey from clinician to startup founder, Oxley provided an inside look at what it takes to bring disruptive technologies from concept to clinic. This session offered a rare glimpse into the mindset of a neurotech innovator navigating the high-stakes interface of science, medicine, and industry.