Artificial intelligence (AI) and machine learning (ML) are rapidly transforming clinical decision support systems (CDSSs) in intensive care units (ICUs), where vast amounts of real-time data present both an opportunity and a challenge for timely clinical decision-making. Here, we trace the evolution of machine intelligence in critical care. This technology has been applied across key ICU domains such as early warning systems, sepsis management, mechanical ventilation, and diagnostic support. We highlight a transition from rule-based systems to more sophisticated machine learning approaches, including emerging frontier models. While these tools demonstrate strong potential to improve predictive performance and workflow efficiency, their implementation remains constrained by concerns around transparency, workflow integration, bias, and regulatory challenges. Ensuring the safe, effective, and ethical use of AI in intensive care will depend on validated, human-centered systems supported by transdisciplinary collaboration, technological literacy, prospective evaluation, and continuous monitoring.

Background/Objectives: Artificial intelligence (AI), particularly large language models (LLMs), has demonstrated versatility in various applications but faces challenges in specialized domains like neurology. This study evaluates a specialized LLM’s capability and trustworthiness in complex neurological diagnosis, comparing its performance to neurologists in simulated clinical settings. Methods: We deployed GPT-4 Turbo (OpenAI, San Francisco, CA, US) through Neura (Sciense, New York, NY, US), an AI infrastructure with a dual-database architecture integrating “long-term memory” and “short-term memory” components on a curated neurological corpus. Five representative clinical scenarios were presented to 13 neurologists and the AI system. Participants formulated differential diagnoses based on initial presentations, followed by definitive diagnoses after receiving conclusive clinical information. Two senior academic neurologists blindly evaluated all responses, while an independent investigator assessed the verifiability of AI-generated information. Results: AI achieved a significantly higher normalized score (86.17%) compared to neurologists (55.11%, p < 0.001). For differential diagnosis questions, AI scored 85% versus 46.15% for neurologists, and for final diagnosis, 88.24% versus 70.93%. AI obtained 15 maximum scores in its 20 evaluations and responded in under 30 s compared to neurologists’ average of 9 min. All AI-provided references were classified as relevant with no hallucinatory content detected. Conclusions: A specialized LLM demonstrated superior diagnostic performance compared to practicing neurologists across complex clinical challenges. This indicates that appropriately harnessed LLMs with curated knowledge bases can achieve domain-specific relevance in complex clinical disciplines, suggesting potential for AI as a time-efficient asset in clinical practice.

Brain tissue oxygenation (PbtO2)-guided therapy can improve the neurological outcome of traumatic brain injury (TBI) patients. With several Phase-III ongoing studies, most of the existing evidence is based on before-after cohort studies and a phase-II randomized trial. The aim of this study was to assess the effectiveness of PbtO2-guided therapy in a single-center cohort. We performed a retrospective analysis of consecutive severe TBI patients admitted to our center who received either intracranial pressure (ICP) guided therapy (from January 2012 to February 2016) or ICP/PbtO2-guided therapy (February 2017 to December 2019). A genetic matching was performed based on covariates including demographics, comorbidities, and severity scores on admission. Intracranial hypertension (IH) was defined as ICP > 20 mmHg for at least 5 min. Brain hypoxia (BH) was defined as PbtO2 < 20 mmHg for at least 10 min. IH and BH were targeted by specific interventions. Mann–Whitney U and Fisher’s exact tests were used to assess differences between groups. A total of 35 patients were matched in both groups: significant differences in the occurrence of IH (ICP 85.7% vs. ICP/PbtO2 45.7%, p < 0.01), ICU length of stay [6 (3–13) vs. 16 (9–25) days, p < 0.01] and Glasgow Coma Scale at ICU discharge [10 (5–14) vs. 13 (11–15), p = 0.036] were found. No significant differences in ICU mortality and Glasgow Outcome Scales at 3 months were observed. This study suggests that the role of ICP/PbtO2-guided therapy should await further confirmation in well-conducted large phase III studies.

Endoscopic spine surgery (ESS) has evolved considerably, yet there remains a lack of consensus on patient selection, technical specifications, and outcome reporting. This heterogeneity hinders collaborative efforts to establish comprehensive evidence-based practices. To develop core outcome sets (COS) and reporting guidelines for ESS for disc disease through an international consensus. We established a multidisciplinary, international expert panel through literature review and peer-to-peer recruitment. Between January and April 2025, we employed a three-round modified Delphi technique. Items spanned three domains: patient characteristics, practices and outcomes. Propositions endorsed by ≥ 75 % of participants were classified as 'strong' and included in the COS. Those with 50-75 % were reconsidered in the subsequent round, requiring ≥66 % for inclusion as 'moderate'. The panel consisted of 77 spine surgeons from Europe (61 %), Asia (14 %), and North America (13 %), with 58 completing all three rounds. Of 183 items (165 initially provided, 18 participant add-ons), agreement was reached on 99 items (82 strongly and 17 moderately). Key consensus elements included demographics, comorbidities, clinical complaints, imaging findings, surgical indications, technical parameters, and outcome measures such as pain scores, functional outcomes, and complications. We established the Neurocore-SENSED framework, comprising COS and reporting guidelines for ESS for disc disease. This consensus addresses fundamental heterogeneity in ESS research, complementing existing nomenclature and indication-specific guidelines. By enhancing consistency, comparability, and quality of ESS research, this framework aims to accelerate the development of evidence-based practices that optimize patient outcomes in this rapidly advancing field.

Background: Occupational stress among healthcare workers negatively impacts job satisfaction and patient care quality, jeopardizing healthcare system sustainability. Traditional employer-driven approaches often fail to address these challenges comprehensively, leading to persistent gaps in work condition transparency and well-being. Aims: To elucidate the working conditions of health workers and introduce a worker-centred, technology-based strategy moving beyond traditional practices and entrenched medical culture. Methods: A nationwide survey of Belgian medical residents evaluated occupational conditions and perceptions of management practices. Additionally, the alpha version of a decentralized mobile application was tested to gather user satisfaction and feedback on its usability. The data were surveyed using Pearson’s chi-squared and Kruskal–Wallis rank sum tests to assess associations between categorical and ordinal variables, respectively. Alpha-testing results were evaluated using descriptive statistics. Results: The nationwide survey, involving 257 participants, revealed significant associations between medical specialty, work choices and compensation. Notably, 91% of participants expressed strong interest in our proposed open, decentralized solution. In the alpha testing phase, 12 testers reported high satisfaction regarding time-tracking accuracy and payroll verification, though challenges related to administrative burden were also identified. Conclusions: The findings underscore the need for innovative, worker-centred occupational management solutions. The proposed solution shows promise in improving autonomy and transparency, potentially enhancing healthcare delivery and sustainability.