SDM is a structured yet flexible process in which HCPs and patients collaboratively determine the best course of action, integrating medical evidence with the patient’s values and preferences. Recognizing that many clinical decisions involve multiple valid options, SDM ensures that the chosen path reflects what matters most to an informed patient. The process unfolds in distinct stages [1]. Information exchange serves as the foundation, with HCP presenting viable options, detailing their benefits, risks, and uncertainties. Traditionally, this stage is often supported by static Patient Decision Aids, such as those developed guided by frameworks like the Ottawa Decision Support Framework [28]. The aim is to prepare patients by increasing knowledge and helping clarify values. Deliberation follows, allowing the patient and HCP to explore these options in the context of the patient’s goals, concerns, and circumstances. This phase encourages active dialogue, where patients seek clarification and HCPs ensure comprehension. Decision-making emerges from this discussion, as both parties reach a consensus that aligns clinical expertise with patient priorities. Finally, implementation translates the decision into action, requiring commitment from both patient and HCP. Adherence depends on confidence in the decision, reinforced by clear communication, trust, and continued support through follow-up. While SDM enhances patient engagement and clinical outcomes, its integration into routine practice remains inconsistent. Effective implementation demands a cultural shift in clinical workflows, supported by training, institutional commitment, and tools that facilitate meaningful participation rather than tokenistic involvement.

Despite the established benefits of SDM, practical implementation faces substantial barriers that AI, particularly generative AI, can effectively address. The contemporary medical environment presents HCPs and patients with increasingly complex information that can impede effective communication. While traditional AI models provide structured risk stratification and evidence-based recommendations, generative AI complements these by transforming clinical data into adaptive, natural language explanations that facilitate interactive engagement.

A significant barrier is varying health literacy, with many adults struggling to comprehend complex medical information. Generative AI addresses this by converting dense medical reasoning into accessible narratives, calibrated to individual literacy levels through techniques like reading-level adaptation, while preserving clinical accuracy. This supports more meaningful engagement across diverse patient populations without sacrificing informational integrity. Furthermore, AI reasoning can synthesize information related to multiple conditions or comorbidities, presenting a holistic view tailored to the patient’s overall health status, which is often difficult with standard, single-condition PDAs.

Time constraints consistently limit comprehensive SDM implementation. Generative AI streamlines this process by autonomously producing structured, real-time summaries of clinical options and responding dynamically to patient queries. This capability allows HCPs to allocate consultation time to value-based discussions rather than manual data synthesis, enhancing clinical efficiency without compromising decision quality.

Patient heterogeneity in clinical priorities and outcome preferences necessitates personalized communication. Generative AI enables interactive dialogue that adapts to individual concerns. For example, it can restructure treatment comparisons to emphasize nonsurgical alternatives when patients express concerns about operative interventions or highlight specific risks and benefits relevant to the patient’s unique circumstances (eg, comorbidities). This responsive adaptation ensures explanations evolve according to articulated preferences, supporting truly patient-centered communication. To ensure consistency and interoperability, the output generated by AI reasoning systems could be grounded in standardized clinical terminologies, such as Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT). SNOMED CT provides a comprehensive, computer-processable vocabulary for clinical terms used in EHRs globally. Aligning AI-generated explanations with SNOMED CT could help ensure the terminology used is consistent with the patient’s record and potentially compatible with existing structured decision support tools or clinical information systems.

In clinical decision support, AI reasoning aims to deliver tailored rationales specific to a patient’s context and values, going beyond technical transparency. Conventional explainability methods, such as feature-importance plots or probability distributions, may reveal how a model arrives at its outputs, yet rarely clarify why a recommendation is meaningful for this patient. By contrast, AI reasoning situates those outputs within clinical logic and patient priorities, generating user-friendly justifications that directly facilitate SDM conversations. In this way, generative AI can transform raw model outputs into narrative explanations relevant to each patient’s unique goals, thus enabling a richer, more interactive exchange than code-level transparency can provide.

The value of AI in SDM lies not in technical transparency but in delivering clear, relevant, and actionable explanations that support informed decision-making. Generative AI enhances this process by enabling real-time refinement of reasoning based on HCP modifications and patient queries. This dynamic responsiveness allows the system to restructure explanations according to evolving priorities, for instance, shifting focus when patients express preferences regarding quality versus length of life, or adjusting the complexity based on literacy needs. Human-level AI reasoning, augmented by generative AI’s capacity to produce adaptive, context-aware explanations, surpasses abstract explainability in clinical relevance and utility, directly supporting the fundamental objectives of SDM in contemporary health care practice.

For AI to effectively support SDM, its reasoning processes must align with the communicative and deliberative nature of HCP-patient interactions. Both AI reasoning and SDM inherently demand clarity, transparency, justification, and personalization. For instance, when an AI provides clinically aligned logic, it directly supports the information exchange step by framing recommendations in medical terms that HCPs can relay and discuss with patients. Transparent recommendations facilitate the deliberation phase by clearly presenting options alongside their respective pros and cons. Similarly, a clear justification for AI-generated outputs bolsters the decision-making step, providing concrete, evidence-based rationales. Additionally, AI adaptability to individual patient contexts, values, and literacy levels emulates the tailored communication essential for effective SDM. An AI system capable of communicating through clinical reasoning can seamlessly integrate into the SDM dialogue. In contrast, an AI that provides only raw recommendations without explanations offers limited value in collaborative clinical interactions (Table 2).

When AI reasoning is absent, HCPs and patients are left with raw scores or black-box outputs that fail to address individual preferences and concerns. Moreover, merely disclosing the technical details of a system’s predictions does not sufficiently enable patients to evaluate personal trade-offs. Similarly, it does not help them understand how a recommendation aligns with their health objectives. Consequently, lacking coherent, patient-centered logic, these AI suggestions may appear arbitrary, eroding trust and undermining SDM’s commitment to collaborative, value-sensitive decision-making. Ultimately, advice that lacks contextual reasoning, which both the HCP and patient can discuss meaningfully, turns into top-down instructions. Thus, this approach limits the opportunity for a shared dialogue.

Bridging the gap between AI capabilities and SDM needs requires a shift toward a new paradigm: AI-SDM. This model emphasizes practical integration and technical feasibility in real-world care. AI-generated explanations must be tailored to the clinical context [29,30]. Just as experienced HCPs adjust communication to different scenarios and patient profiles, AI systems should generate context-sensitive justifications. These must reflect clinical reasoning and align with patient values. For example, in chemotherapy decisions, AI reasoning should emphasize expected efficacy based on tumor type, potential side effects, and survival projections—framed according to the patient’s values, such as prioritizing quality of life over longevity. In chronic disease management, such as lifestyle interventions, explanations may instead highlight long-term risk reduction and adherence support. Tailoring AI reasoning to clinical context ensures its explanations are both relevant and usable [30].

Integrating AI-SDM into clinical practice requires alignment with existing health IT infrastructure. A feasible workflow might involve the AI-SDM system being triggered within the electronic health record (EHR) during a patient encounter. The system could leverage modern interoperability standards, such as Health Level Seven International Fast Health care Interoperability Resources application programming interfaces, to interface with the EHR [31]. These standards enable secure retrieval of up-to-date patient data, including diagnoses, medications, lab results, and problem lists coded using SNOMED CT [32]. Predictive AI components would then analyze this data to generate context-specific risk assessments, outcome probabilities, or treatment comparisons based on established models and guidelines. Subsequently, a generative AI component would synthesize these complex outputs into patient-friendly language, creating tailored explanations, summaries, and potentially visual aids. These can be presented directly within the EHR interface for the HCP and patient to review and discuss together.

Successful adoption hinges not only on technical integration but also on stakeholder readiness. Key hurdles include ensuring robust IT infrastructure and establishing privacy-compliant data governance protocols. Providing adequate training for HCPs is also key. This helps them effectively use and critically appraise AI outputs within the SDM context. Strong leadership and organizational commitment are essential to address these challenges, supporting integration and promoting AI as a collaborative tool. This tool enhances, rather than replaces, clinical judgment and patient partnership. Such a standards-based foundation is a prerequisite for reliable data retrieval, consistent interpretation, and effective AI-SDM deployment across diverse clinical settings and platforms.

Dual-process theory of clinical cognition proposes that clinicians alternate between fast, intuitive pattern recognition (system 1) and slower, analytical reasoning (system 2) when diagnosing and selecting treatments [33]. AI-SDM mirrors this architecture by pairing predictive and recommendation models, which emulate System 2’s probabilistic deliberation, with a generative reasoning layer that approximates System 1’s narrative synthesis. This pairing enables the framework to deliver quantitative risk estimates while simultaneously providing context-sensitive justifications that fuel real-time dialogue. The model is further anchored in the Ottawa Decision Support Framework, which conceptualizes SDM as a sequence of need identification, values clarification, and decision support [34]. By embedding adaptive values-clarification prompts within the generative layer, AI-SDM operationalizes these stages and ensures that explanations evolve in response to patient priorities. Principles of patient-centered communication likewise inform system design: explanations are calibrated to individual literacy, emotional state, and cultural context to preserve relational autonomy and encourage bidirectional questioning [35]. Empirical evidence shows that decision aids incorporating tailored narratives and explicit values clarification improve decisional quality and patient trust, particularly when powered by AI-driven reasoning engines that maintain transparency and contestability [36,37]. Synthesizing these theoretical strands positions AI-SDM not merely as a technological overlay but as a cognitive and communicative scaffold that aligns algorithmic inference with the epistemic norms of evidence-based, patient-centered care.

AI-SDM is a comprehensive, multimodel conceptual framework developed to incorporate AI-driven reasoning into clinical decision-making. It explicitly ensures HCP oversight and preserves patient autonomy. Unlike conventional AI-based decision-support tools that often focus solely on algorithmic outputs or technical explainability, AI-SDM introduces a collaborative reasoning approach. It enables real-time interaction and deliberation among HCPs, patients, and AI-generated insights. AI-SDM is built upon a multilayered AI system where different AI models contribute distinct functionalities: predictive AI performs risk stratification and outcome modeling; recommendation AI retrieves evidence-based guidelines and treatment options; natural language processing (NLP) AI extracts relevant data from clinical records; and generative AI functions as the crucial reasoning facilitator, transforming complex, structured AI outputs into interactive, patient-specific explanations. Through this synergistic integration, AI-SDM ensures that AI remains an adaptive and justifiable tool. It allows HCPs and patients to engage in structured deliberation while preserving the core principles of SDM.

AI-SDM builds on advances from sophisticated clinical decision support systems and incorporates Human-Computer Interaction principles for usability. It distinguishes itself fundamentally by its primary goal. That is to generate adaptive, narrative clinical reasoning specifically designed to facilitate the triadic deliberation (HCP-patient-AI) inherent in the SDM process. It shifts the focus from mere prediction or transparency toward context-rich, personalized justifications that clinicians can explore, modify, and communicate in natural language. While Table 1 compared technical forms of AI interpretation, Table 3 expands the comparison to full clinical decision frameworks, contrasting how SDM, XAI, and AI-SDM function at the bedside.

AI-SDM operates through 4 integrated phases. Each phase leverages specialized AI models while preserving HCP oversight and patient autonomy (Figure 1).

The decision process begins with structured data acquisition. This involves gathering information from 3 essential sources: HCP-provided medical history and diagnostic considerations; patient-articulated values, goals, and risk preferences; and AI-derived evidence from clinical guidelines and research findings. During this phase, 3 specific AI functions are used. Predictive AI performs personalized risk assessment and outcomes analysis. Recommendation AI determines evidence-based treatment paths. Additionally, NLP with LLMs extracts unstructured data from health records and literature.

Following data integration, AI-SDM synthesizes statistical models, clinical best practices, and individual patient characteristics into a structured decision model. This model then generates 2 distinct outputs. First, HCPs receive a comprehensive, evidence-based report detailing risk-adjusted treatment pathways, complete with probability estimates and confidence intervals. Second, patients receive an interactive explanation, which may include visual aids, tailored to their understanding. Generative AI plays a crucial role by transforming these structured outputs into context-specific explanations. These explanations are adaptable to user engagement, thereby surpassing the limitations of static AI summaries.

An important conceptual consideration in this multimodel integration is the potential for conflicting or inconsistent outputs. Such conflicts can arise between the predictive, recommendation, and NLP components. For example, a high predicted risk from the predictive model might conflict with a standard guideline recommendation from the recommendation module. To address these conflicts, the AI-SDM framework incorporates a dedicated reconciliation layer. This layer automatically applies a clinically prioritized weighting mechanism. If a conflict occurs, the system assigns greater weight to validated risk factors while flagging any unresolved discrepancies for HCP review. This process ensures full transparency regarding potential ambiguities within the underlying data. Moreover, it maintains a robust foundation that supports subsequent generative AI reasoning. This ensures both transparency and audibility of any data ambiguities.

A central innovation in the AI-SDM workflow involves converting structured algorithmic output into adaptive, human-centered reasoning. Instead of static recommendations, generative AI produces dynamic, context-sensitive explanations that evolve based on HCP and patient interaction. These explanations are explicitly grounded in the underlying evidence and are safeguarded against potential biases or hallucinations (details of this implementation are beyond the scope of this paper). The AI component is designed for adaptability in both content and timing. It can, for instance, provide concise, rapid summaries for acute scenarios or more detailed rationales for planned consultations. The AI delivers reasoning, rather than merely outcomes, through dual channels tailored specifically to HCP and patient needs. This transforms the AI from a data synthesizer into a deliberation partner, supporting more justifiable clinical decisions.

The AI-SDM model facilitates real-time modification of AI-generated reasoning through continuous HCP evaluation and patient engagement. HCPs can adjust recommendations based on their expertise and contextual factors that extend beyond algorithmic reach. Simultaneously, patients can interrogate specific risks and refine their preferences. In response to these inputs, generative AI dynamically updates explanations. This iterative adaptation process ensures continuous alignment with both clinical judgment and evolving patient priorities.

The culmination of this process is a human-controlled, AI-assisted decision that aligns clinical evidence with patient values. AI-enhanced documentation captures the deliberative process, preserving transparency and accountability in medical records. The system can then generate personalized educational materials to support treatment adherence and follow-up strategies, ensuring continuity of care beyond the initial decision point.

A fundamental requirement for AI-SDM is that it must safeguard patient autonomy and uphold the ethos of SDM at every step. To this end, the model is designed such that the AI’s outputs are always transparent, open to question, and subordinate to human input. Both the HCP and the patient should be empowered to challenge or adjust the AI’s suggestions freely. For example, if the AI’s analysis seems to favor a particular treatment strongly, the patient can ask for clarification or express discomfort, and the HCP can probe the AI’s reasoning for validity—in both cases, the AI must accommodate these challenges by explaining its rationale or recalibrating its advice. This contestability is deliberate: the AI is not a black box oracle handing down decisions, but a tool that invites scrutiny. Transparency is crucial here; the AI-SDM system should clearly communicate why it is highlighting certain options (eg, “Option A is supported by X study for patients with your profile”) so that the human participants can critically evaluate the reasoning. By avoiding opaque or one-sided recommendations, the AI prevents any undue influence or bias that could pressure the patient. In practice, this means AI-SDM will present multiple options with evidence rather than a singular “do this” directive, and it will explicitly incorporate the patient’s own goals into its analysis. The HCP retains ultimate responsibility to interpret and, if necessary, correct the AI’s output before any action. In sum, AI-SDM is constructed as a facilitator, not a decision maker: it expands the information and reasoning available to the patient and HCP, but it never replaces their agency. The patient’s values and the HCP’s professional judgment remain at the center of every decision, thereby preserving the autonomy and individualized nature of care.

Adoption hinges on transparent, interpretable AI systems that avoid “black box” functionality. In AI-SDM, generative AI transforms complex algorithmic outputs into verifiable explanations with clear references to clinical guidelines and explicit confidence levels. Implementation requires structured reasoning pathways that allow HCPs to interrogate AI-derived conclusions and understand their evidentiary basis, particularly when recommendations diverge from conventional practice.

Navigating the evolving regulatory frameworks for AI-assisted clinical decision-making is crucial. AI-SDM systems, particularly those providing diagnostic or therapeutic recommendations, would likely be considered software as a medical device and need to align with guidelines from regulatory bodies like the US Food and Drug Administration or equivalent authorities globally. Key considerations include rigorous validation, demonstrating safety and effectiveness, ensuring transparency (allowing HCPs to independently review the basis for recommendations), and implementing robust quality management systems, including postmarket surveillance. While AI-SDM preserves human oversight by positioning AI as decision support rather than the ultimate decision maker, clear governance policies are needed to delineate responsibility among developers, HCPs, and health care institutions, especially concerning liability if AI suggestions deviate from the standard of care.

AI-SDM must be implemented ethically, safeguarding patient rights and promoting equity. This includes strict adherence to data privacy regulations pertinent to health information, such as the principles outlined in the Health Insurance Portability and Accountability Act in the United States or the General Data Protection Regulation in Europe, as well as relevant national or local regulations (eg, in Saudi Arabia). Systems must be designed to accommodate diverse health literacy levels, cultural contexts, and cognitive abilities, and generative AI interfaces should dynamically adjust explanation complexity based on individual needs while preserving clinical accuracy. Furthermore, proactive measures are essential to address potential algorithmic biases, which could arise from training data used in the predictive or recommendation models. To prevent AI hallucinations and preserve clinical integrity, each generative output is anchored to explicit citations from validated guidelines or peer-reviewed studies. An automated audit protocol continuously monitors real-time outputs for discrepancies, flagging any deviations from established evidence standards so that HCPs can rapidly override or adjust the AI’s recommendations. This includes rigorous auditing of the underlying predictive and recommendation models for fairness across demographic groups and designing the generative AI reasoning layer to explicitly surface significant uncertainties or conflicting evidence that might stem from data limitations or potential biases.

Building on these safeguards, future deployments will institute a 4-layer governance loop for continuous bias mitigation. First, training pipelines will use fairness-aware algorithms—such as reweighting and equalized-odds postprocessing—to correct calibration disparities before clinical deployment, an approach recommended by Rajkomar et al [47] for advancing health equity in machine-learning systems. Second, the production environment will stream model outputs into a real-time dashboard that audits performance by age, sex, ethnicity, and socioeconomic status; similar bias-auditing infrastructures have been shown to reveal hidden performance gaps in widely used clinical algorithms [48]. Third, quarterly ethical-compliance reviews will examine data provenance, feature attribution, and workflow impact to maintain regulatory alignment, and finally, all bias metrics and remediation actions will be logged in a version-controlled registry to support external audit and public transparency. Together, these stages create an auditable feedback loop that limits drift, documents remediation, and embeds fairness governance directly into routine system maintenance.

The sociotechnical impact of AI-SDM also depends on how clinicians and patients adopt, negotiate, and contest its recommendations. Rogers’ Diffusion of Innovations theory explains variability in uptake by highlighting perceived complexity, relative advantage, and trialability, whereas technological-determinist perspectives warn that overly authoritative AI may erode clinician agency, and social-constructivist analyses emphasize that users actively reshape technology through practice [49]. To preserve balanced doctor-patient dynamics, AI-SDM therefore labels the scope and limitations of every recommendation, requires explicit clinician confirmation before any automated action, and provides a “why-question” interface so both parties can interrogate underlying evidence or override suggestions. Empirical work on person-centered AI indicates that transparent, assistive designs strengthen trust when clinicians retain control, while unmoderated reliance can attenuate empathy and SDM [50]. Embedding these sociological insights into interface rules and governance policies anchors AI-SDM in relational autonomy and guards against power imbalances.

The clinical utility of AI-SDM depends on seamless integration with existing EHR systems and clinical workflows. Implementation requires user-friendly interfaces that generate concise, contextually relevant insights without increasing cognitive burden or documentation requirements for HCPs. However, seamlessly embedding this potentially complex, multistep interaction, particularly the deliberative refinement phase, into time-constrained and varied clinical workflows represents a significant practical and design hurdle. Achieving this without disrupting clinical practice or unduly lengthening consultations will be critical for successful adoption. As discussed earlier (in section “Bridging AI Reasoning and SDM: Toward AI-SDM”), leveraging interoperability standards like Fast Health care Interoperability Resources and terminologies like SNOMED CT is vital. Ultimately, AI-SDM must demonstrate measurable improvements in decision quality, patient experience, or efficiency to justify the technological investment and workflow adjustments required for widespread adoption.

Addressing these multifaceted challenges necessitates an iterative implementation approach, combining continuous HCP and patient feedback with rigorous validation. Validating the efficacy and safety of the AI-SDM framework itself would require a phased approach, progressing from algorithmic validation of individual AI components and rigorous usability testing of the interface and explanation formats, through simulation studies assessing decision quality, to eventual pilot clinical trials evaluating real-world impacts on patient engagement, decision concordance, and outcomes. Successful deployment will ultimately depend on collaborative governance structures that balance technological innovation with clinical pragmatism, ethical principles, patient safety, and regulatory compliance.

Realizing the potential of AI-SDM necessitates substantial future research and development. Key priorities include the rigorous development and refinement of the generative reasoning component, incorporating robust mechanisms for clinical validity, grounding, and bias mitigation, alongside effective strategies for reconciling outputs from disparate AI models. While this paper introduces AI-SDM as a conceptual framework, future work could explore empirical validation, such as usability studies, workflow simulations, or clinical implementation pilots, to assess its impact on decision quality, patient engagement, and workflow integration. Further research grounded in HCI principles may also inform how the model could integrate seamlessly into clinical environments without increasing HCP burden. Finally, ongoing investigation into dynamic fairness auditing, evolving regulatory pathways for AI-driven SDM tools, and establishing clear governance structures will be crucial for responsible and equitable deployment.

Building on the dual-process and patient-centered theories outlined above, future iterations of AI-SDM will deepen its affective intelligence by coupling multimodal emotion‐recognition pipelines with the existing generative explanation layer. Recent work demonstrates that equipping decision-support systems with emotional capabilities can reduce affective bias and improve user trust when complex trade-offs are discussed [51]. To operationalize this insight, we plan to integrate a multimodal deep learning model that fuses facial microexpressions, vocal prosody, and lexical sentiment—an approach shown to outperform unimodal affect detectors in health care contexts and to strengthen access trust between patients and clinicians [52]. Continuous emotion streams will inform dynamic values-clarification prompts generated by the narrative engine, ensuring that explanations adapt when signs of confusion, anxiety, or decisional conflict emerge. A recent systematic review of emotion-recognition AI identifies transparent feature attribution and dataset diversity as prerequisites for reliable affective computing in clinical environments; these requirements will guide our data-governance and model-validation strategy [52]. Finally, evidence from a randomized trial of an AI-enabled decision aid shows that personalized, empathetic narratives significantly improve decisional quality and shared-decision metrics compared with static educational material. By embedding such adaptive affective feedback into AI-SDM, we not only enhance the emotional-computing module but also further align the framework with the Ottawa Decision Support and patient-centered communication theories that underpin its interdisciplinary foundation.