Artificial intelligence (AI) is rapidly transforming health care, yet neuroanesthesiology stands at a unique crossroads. While numerous FDA-approved AI/ML tools are already in clinical use, current models underperform in several spheres.1 This disconnect reveals a critical truth: the most sophisticated analytics are meaningless without clinical context. As health care data grows exponentially and demand for precision care expands, neuroanesthesiologists must move beyond passive adoption to an active partnership in developing AI systems that genuinely enhance patient care. The question is not whether AI will transform our field, but whether we will shape that transformation. True clinical relevance in neuroanesthesia requires reconciling 3 often divergent perspectives: patient-centered outcomes, clinical workflow efficiencies, and health system value. These perspectives exist in current clinical care environments, but developing algorithms that formalize them requires clarity and alignment. The challenge and opportunity lie in developing AI systems that satisfy these perspectives. This will require active participation in defining success metrics that extend beyond traditional anesthetic endpoints to encompass functional neurological outcomes not typically captured in perioperative datasets. The discussion surrounding AI adoption in health care has become focused on the “black box” problem, the concern that complex algorithms obscure their decision-making processes from human understanding. Yet this framing misses a more fundamental question: who possesses the expertise to meaningfully interrogate these systems in the first place? The challenge is not merely achieving algorithmic transparency, but ensuring that those developing, validating, and implementing AI in clinical settings possess the domain knowledge necessary to critically evaluate its outputs.2 As neuroanesthesiologists confront the proliferation of AI tools promising to revolutionize perioperative neuroscience, we must recognize that successful adoption requires deep clinical expertise guiding technical implementation, not the reverse.3 Evidence across medical specialties demonstrates a clear pattern: clinician-led AI development consistently outperforms technology-first approaches. When domain experts actively participate, the resulting systems better capture clinical nuance and achieve meaningful improvements in patient care.4 The current landscape reveals an expertise gap, with algorithms sometimes trained on inadequate datasets, validated against inappropriate benchmarks, or deployed without sufficient consideration for clinical workflow integration.5 Recent advances in federated learning offer promise by enabling collaborative development that keeps patient data at originating institutions while allowing model refinement across diverse environments, a framework that naturally emphasizes clinician involvement throughout the development lifecycle.6 Clinical expertise becomes particularly critical in perioperative neuroscience, where the complexity of brain-anesthesia interactions defies simple algorithmic solutions. The effects of anesthetic agents on consciousness, cerebral metabolism, and functional connectivity involve nonlinear dynamics that even experienced practitioners find challenging.7 The multimodal data streams integral to neuroanesthesia care, electroencephalography (EEG), intracranial pressure monitoring, cerebral oximetry, transcranial doppler, and evoked potentials, each require specialized interpretation that depends heavily on clinical context.5 An algorithm may detect patterns in EEG data, but clinicians understand how those patterns change meaning in the context of surgical manipulation, anesthetic depth, patient temperature, and underlying pathology. The patient safety implications warrant careful attention. Generic AI models, even when technically sophisticated, may struggle in neurocritical scenarios where subtle changes in monitored parameters could herald impending complications or simply reflect benign physiological variation. Neuroanesthesiologists distinguish between these possibilities by integrating information across multiple domains simultaneously, a reasoning process that current AI systems cannot replicate without explicit guidance from experts who understand the clinical significance of these integrations. Validation frameworks must prioritize clinical relevance over purely technical metrics, demanding demonstration of utility in real clinical environments and evaluation against outcomes that matter to patients.8 Examining successful AI implementations across medical specialties reveals consistent themes: domain expertise guides not only initial development but also ongoing validation and implementation. The most impactful clinical AI systems emerge from iterative collaboration between clinicians who understand problems deeply and technologists who can translate that understanding into computational solutions.4 This collaborative model requires mutual learning, clinicians developing AI literacy to engage meaningfully with technical decisions, while technologists acquire sufficient clinical understanding to appreciate subtleties that determine whether algorithms truly serve patient care.9 The path forward for neuroanesthesia AI requires positioning ourselves as co-developers, not merely end-users (Fig. 1). Realizing potential requires active engagement from our specialty throughout the development process.1 We should establish training pathways that equip neuroanesthesiologists with quantitative skills and computational literacy necessary to contribute substantively to AI projects, while encouraging developers working in our domain to invest time understanding perioperative neuroscience at levels enabling productive collaboration. Similar to other clinical domains, the roadmap for successfully integrating AI into clinical practice requires clear articulation of clinical problems, alignment of AI methodologies with those problems, and rigorous evaluation of clinical impact.10 Without clinical impact, the most novel algorithms fall short.FIGURE 1: Collaborative co-development model for AI in neuroanesthesia: Centering Clinical Relevance and Technical Innovation. This schematic illustrates the collaborative co-development framework for artificial intelligence tools in neuroanesthesiology, emphasizing the cyclical and iterative nature of effective AI development. The model depicts a continuous cycle of 5 interconnected stages arranged clockwise: (1) Clinician-Developer Collaboration, representing the equal partnership between domain experts and technologists; (2) Problem Definition, where clinical questions and needs are identified; (3) Feature Engineering, involving selection and optimization of relevant data streams and variables; (4) Validation Framework development, establishing appropriate metrics and benchmarks for clinical relevance; and (5) Implementation and Refinement, addressing real-world deployment and ongoing optimization.Neuroanesthesiologists will need essential competencies to be able to recognize the difference between AI models, not to build them, but to understand their respective strengths and failure modes. Models that perform brilliantly on training data may generalize poorly to your institution’s patient population. A model developed exclusively on academic medical center data may fail in community hospitals with different case mixes and resource constraints. Does high overall accuracy mask poor performance in specific subgroups, perhaps elderly patients or those with rare conditions underrepresented in training data? Understanding metrics such as the area under the receiver operating characteristic curve (AUC), positive predictive value, and calibration enables a critical evaluation of whether a promising algorithm is ready for clinical deployment. Anesthesiologists possess unique expertise in physiology, clinical uncertainty, and the consequences of perioperative decisions. This expertise should inform AI development from inception through implementation. Our specialty benefits most when we lead rather than follow AI development in our domain. True partnership with technology developers requires our active participation in building these systems.2,3 We have the clinical knowledge necessary to guide AI toward genuinely useful applications while helping avoid potential pitfalls. The black box problem will be addressed not merely by making algorithms more transparent, but by ensuring that experts with the knowledge to interpret them are meaningfully involved in their creation. The question before us is whether we will exercise that expertise proactively to shape the future of perioperative neuroscience care.
Meredith C B Adams (Tue,) studied this question.