From Algorithm to Bedside: A Clinician's Framework for AI in Practice

The Five Questions Every Surgeon Should Ask About Artificial Intelligence.

Artificial intelligence (AI) is being integrated into clinical practice at a pace that has outstripped clinicians' ability to critically evaluate it. While 2 in 3 physicians now report using AI in some capacity in their practice, most trainees receive no formal education in AI. The result is not a deficit in intelligence or curiosity, but, at least in part, a lack of shared vocabulary that limits clinicians' ability to interpret AI claims and participate in governance decisions. This paper presents a practical clinical framework for interrogating AI claims, organized around five core questions: What is it? Does it work? Will it work here? What does it do? Is it real? These domains provide a structured approach applicable across clinical encounters, including literature appraisal, vendor evaluation, and point-of-care use. To support this framework, we define 6 vocabulary clusters commonly encountered in clinical AI discourse and introduce a translation layer that maps AI terminology to familiar traditional clinical and biological concepts. Finally, we illustrate the framework through representative real-world scenarios. This approach enables clinicians to critically evaluate AI tools, engage meaningfully in institutional decision-making, and apply consistent standards of evidence to emerging technologies.

Building Clinical Artificial Intelligence at the PICU Bedside: Recognizing the Clinician-Builder

Several of the highest-value problems in critical care—synthesizing fragmented bedside data, surfacing the tacit heuristics of experienced clinicians, generating accurate family-facing explanations, and decision support—are too specialized, too local, and too embedded in uncodified practice to specify from outside the unit; the market they represent is often too small to repay commercial development. This commentary argues for recognizing a missing translational figure: the clinician-builder—a frontline clinician who identifies workflow failures and prototypes tools to address them, much as the clinician-scientist did for molecular medicine. Because a prototype is not a product, the clinician-builder co-develops intended use, evidence, and governance with institutional artificial intelligence (AI) centers. Pediatric intensive care should not be the last setting to receive clinical AI, but among the first where it is judged—and a blueprint for other specialties.

Navigating AI in clinical practice: A practical guide for physicians.

Artificial intelligence is rapidly entering clinical practice, yet many physicians-especially those in solo or small-group settings-lack the guidance and evaluation resources needed to use these tools safely. Because state medical boards regulate physicians rather than AI developers, clinicians remain fully accountable when AI‑assisted care contributes to patient harm. This article outlines the risks posed by opaque algorithms, hallucinated outputs, omissions, and inequitable model performance, while emphasizing that AI should augment-not replace-clinical judgment. Drawing on recent research and the Federation of State Medical Boards' 2024 guidance, the article clarifies professional responsibilities related to competence, documentation, informed consent, privacy, and bias mitigation. A practical toolkit offers actionable steps for evaluating AI tools, establishing verification protocols, monitoring performance, and ensuring transparency with patients. The article concludes that responsible physician engagement is essential to realizing AI's benefits while preserving patient safety, professional accountability, and equitable care.

Can you trust the algorithm? A clinical exploration of AI models in perioperative care

A narrative conceptual review of four commonly encountered AI model types: random forests, support vector machines, convolutional neural networks and large language models is presented, exploring their clinical applications, interpretability, limitations and relevance to perioperative and surgical settings.

Beyond the Algorithm: Artificial Intelligence, Clinical Decision-Making, and the Moral Ecology of Care in Africa

Artificial intelligence (AI) is increasingly promoted as a tool to enhance clinical decision-making and thus improve the quality of healthcare. While much of the emerging scholarship on AI and healthcare in Africa has focused broadly on opportunities and systemic challenges, what remains underexplored is the specific application of AI to clinical decision-making. This paper contributes to addressing this gap by offering a conceptual and critical analysis of AI-based clinical decision support systems (AI-CDSS) in African contexts. Drawing on philosophical accounts of medical reasoning and relational moral frameworks such as Ubuntu, the paper draws on the moral ecology of care and shows that algorithmic systems can reconfigure epistemic authority, redistribute responsibility, and risk marginalising context-sensitive and relational dimensions of care. The paper further argues that AI systems are better understood as socio-technical mirrors that reflect and amplify existing human values, institutional arrangements, and power asymmetries. Moving beyond the algorithm, it proposes a shift toward relational and context-sensitive AI governance, including the development of relational impact assessments, the redistribution of responsibility across the AI lifecycle, and the co-production of knowledge with local stakeholders. While focusing on African clinical contexts, the analysis offers broader insights for global debates on AI ethics and clinical decision-making.

Ask-Audit-Apply: A Framework for Clinical Reasoning with Artificial Intelligence.