AI Tools for Cognitive Support in Professional Settings

Title AI in Cardiology: A Tool, Not a Replacement One-Sentence Summary This paper debates the extent to which artificial intelligence will substitute for cardiologists, presenting arguments that AI will enhance many tasks but cannot replace the essential human elements of clinical judgment, accountability, and the physician-patient relationship. Overview The paper presents a balanced debate on the future role of artificial intelligence (AI) in cardiology. The “pro” argument suggests that AI’s capabilities in medical education, diagnostic imaging, and personalized care are advancing rapidly and could surpass human performance in these domains. It highlights AI’s potential to automate tasks, synthesize vast amounts of data, and improve efficiency. Conversely, the “contra” argument emphasizes…

Original Title: Early Recalls and Clinical Validation Gaps in Artificial Intelligence-Enabled Medical Devices Journal: JAMA health forum DOI: 10.1001/jamahealthforum.2025.3172 AI Medical Device Recalls and Validation Gaps Overview Artificial intelligence-enabled medical devices (AIMDs) are increasingly common in clinical practice, yet many receive US Food and Drug Administration (FDA) clearance through an accelerated pathway that does not require prospective human testing. This raises concerns about their performance and safety after entering the market. This study investigated the frequency of recalls among AIMDs and examined whether recalls were associated with two key factors: the lack of premarket clinical validation and the type of manufacturer (publicly traded vs. privately held). Researchers analyzed 950 FDA-cleared…

Original Title: Biomarkers Journal: Alzheimer's & dementia : the journal of the Alzheimer's Association DOI: 10.1002/alz70856_107467 Overview The study investigates the utility of spontaneous speech as a non-invasive biomarker for Alzheimer's disease by developing an automated analysis pipeline. Utilizing the ADReSS 2020 Challenge dataset, which comprises audio recordings from 108 training participants and 48 testing participants performing the Cookie Theft picture description task, the researchers explored the transition from raw audio to diagnostic classification. The methodology involved transcribing audio using commercial tools like OpenAI Whisper and AssemblyAI, followed by the generation of semantic vector embeddings using large language models. These embeddings were then used to train machine learning classifiers, including…

Original Title: Basic Science and Pathogenesis Journal: Alzheimer's & dementia : the journal of the Alzheimer's Association DOI: 10.1002/alz70855_107083 Overview This research addresses the challenge of predicting the progression of Alzheimer’s disease and related dementias using survival analysis. While deep learning models offer high predictive performance, their complex architectures often obscure the biological factors driving their outputs. To resolve this, the authors introduce the Neural Additive Deep Clustering Survival Machines (NADCSM) framework. This model utilizes data from the Alzheimer’s Disease Neuroimaging Initiative, specifically focusing on AV45 Florbetapir PET imaging, genotyping, and demographic information to track the transition from mild cognitive impairment to early Alzheimer’s disease. The framework models survival times…

Original Title: The regulation of artificial intelligence in intensive care units: from narrow tools to generalist systems Journal: NPJ digital medicine DOI: 10.1038/s41746-026-02535-3 Overview Intensive care units represent highly data-intensive environments in healthcare, requiring continuous monitoring and rapid decision-making. While artificial intelligence has been explored for decades, its formal regulation as a medical device began in 1995. By May 2025, the number of approved artificial intelligence-enabled medical devices reached 1,016 in the United States. Many of these tools are designed for narrow, single-task applications such as interpreting radiological images or predicting sepsis. The emergence of generative artificial intelligence and large language models marks a shift toward generalist systems capable of…

Original Title: Biomarkers Journal: Alzheimer's & dementia : the journal of the Alzheimer's Association DOI: 10.1002/alz70856_100574 Overview Alzheimer’s disease is a heterogeneous condition often occurring alongside cerebral small vessel disease. This study examines 262 individuals across two cohorts: the longitudinal TRIAD cohort, representing a low burden of small vessel disease, and the MITNEC-C6 cohort, which includes real-world patients with mixed dementia and moderate-to-severe vascular lesions. Using a deep learning segmentation tool and the Subtype and Stage Inference algorithm, the research team identified distinct imaging-derived subtypes based on amyloid deposition, white matter hyperintensities, perivascular spaces, and diffusion markers. The study tracked 202 individuals at baseline, with follow-ups at two and three…