Decoding the “black-box”: explainable artificial intelligence towards trustworthy advancement in respiratory medicine

Abstract

Artificial intelligence (AI) is increasingly applied in respiratory medicine, offering potential advances in diagnostics, treatment guidance and patient monitoring. However, widespread clinical adoption remains limited due to the opaque “black-box” nature of many algorithms, which challenges clinicians’ trust and hinders integration into routine practice. Explainable AI (XAI; methods and frameworks that render AI outputs interpretable and transparent) has emerged as a promising approach. By providing insights into algorithmic reasoning alongside predictive performance, XAI can support clinician evaluation, facilitate informed decision-making, and enhance accountability in patient care. This Viewpoint discusses the potential applications of XAI across respiratory medicine, highlighting its role in improving transparency, fostering clinician engagement and supporting integration of AI into clinical workflows. Beyond technical considerations, successful adoption of XAI requires cultural and educational shifts, including training programmes, interdisciplinary collaboration, patient engagement, and adherence to ethical and regulatory standards. XAI also holds potential in supporting shared decision-making, translating complex algorithmic outputs into understandable information for patients. By bridging advanced computational tools with clinical reasoning, XAI may help respiratory medicine move towards responsible, patient-centred and transparent AI implementation. Continued research, education, and collaboration are essential to realise its potential and ensure AI serves as a reliable partner in delivering high-quality respiratory care.

Shareable abstract

Explainable AI has the potential to enhance transparency in respiratory medicine, support clinician decision-making, foster patient engagement, and promote accountable, patient-centred care by aligning algorithmic insights with clinical reasoning https://bit.ly/49rU6Av

Introduction

The integration of artificial intelligence (AI) into respiratory medicine promises substantial advances in diagnostics, treatment guidance and personalised care [1–8]. However, clinical adoption remains limited, largely due to the opaque “black-box” nature of many sophisticated algorithms [9, 10]. This lack of interpretability challenges physicians’ ability to trust and implement AI tools in daily practice. Explainable AI (XAI), defined as methods and frameworks that render AI decisions understandable and transparent to users, has therefore emerged as a crucial paradigm [11–13]. By offering interpretable insights alongside predictive accuracy, XAI bridges algorithmic sophistication with the clinical reasoning essential to pulmonology. This Viewpoint explores how XAI differs from conventional AI, why interpretability matters in respiratory medicine, and how the respiratory community can lead responsible implementation of transparent AI systems (table 1).

TABLE 1.

Educational key points on explainable artificial intelligence (XAI) in respiratory medicine

Key theme	Educational points
XAI fundamentals	XAI provides transparency through scientifically verifiable methods (SHAP, LIME, saliency maps, attribution analyses) that reveal how AI models reach conclusions, distinguishing it from black-box AI
Clinical necessity	Interpretability enables clinicians to validate algorithmic reasoning against pathophysiological principles, essential across all medical specialties and particularly critical in respiratory medicine where subtle interpretative differences substantially impact outcomes
Clinical applications	In pulmonology, XAI has diverse potential applications, including thoracic imaging (Grad-CAM heatmaps), functional assessment, risk stratification, digital health monitoring, lung cancer screening, COPD/asthma management, and interventional pulmonology (these are illustrative examples rather than an exhaustive list)
Practical XAI tools	Available XAI implementations include Grad-CAM for imaging, feature importance rankings for spirometry, decision tree visualisations for risk prediction, and counterfactual explanations for personalised interventions
Technical challenges	A fundamental tension exists between model complexity and interpretability: deep learning achieves highest accuracy but resists explanation, while simpler models are transparent but may sacrifice performance, requiring context-dependent balancing
Educational needs	Implementation requires targeted education for both early-career and established clinicians in fundamental AI concepts, XAI method strengths/limitations, and critical evaluation skills, with standardised frameworks
Patient empowerment	Patient engagement and shared decision-making are strengthened through accessible, health-literacy-informed AI explanations using visual representations and avoiding technical jargon
Cultural transformation	XAI represents a cultural shift toward collaborative technology integration, enhancing trust, accountability, interdisciplinary communication, and medicolegal clarity through auditable decision trails
Governance and ethics	Regulatory frameworks must address explanation adequacy, validation standards, and ethical considerations including algorithmic bias, equitable access, training data transparency, and human oversight maintenance
Professional responsibility	Professional societies and respiratory journals should establish best-practice guidelines, curate validated tools, promote transparent AI reporting, and foster knowledge exchange through dedicated conferences
Vision for the future	Pulmonology must embrace XAI as fundamental to responsible, patient-centred respiratory care that preserves clinical expertise and transforms AI from tool requiring blind faith to partner supporting informed judgement
Take-home message	XAI is not merely a technical feature but a fundamental requirement for trustworthy, transparent, and clinically meaningful integration of AI in respiratory medicine; pulmonologists must lead this transformation through education, collaboration, and commitment to interpretability, ensuring AI serves as true partner rather than inscrutable oracle

This table summarises the main learning objectives of this Viewpoint, outlining definitions, technical distinctions from conventional artificial intelligence (AI), specific XAI methods, applications, and the educational, technical, regulatory and ethical aspects of XAI, as well as its implications for patient engagement and shared decision-making. SHAP: SHapley Additive exPlanations; LIME: Local Interpretable Model-agnostic Explanations; Grad-CAM: Gradient-weighted Class Activation Mapping.

What XAI is, and how it differs from conventional AI

Traditional AI systems, particularly deep learning models, function as “black boxes”: they produce predictions without revealing the reasoning process underlying their outputs. While these models may achieve impressive accuracy, clinicians cannot verify whether predictions stem from genuine pathophysiological patterns or spurious correlations in training data. This opacity fundamentally limits clinical trust and adoption.

XAI represents a distinct approach. XAI encompasses scientifically verifiable methods designed to illuminate how models reach their conclusions. Key XAI techniques include attribution analyses, which identify which input features most strongly influence predictions; saliency maps, which visually highlight relevant regions in medical images; SHAP (SHapley Additive exPlanations), which quantifies each feature's contribution to individual predictions; and LIME (Local Interpretable Model-agnostic Explanations), which approximates complex model behaviour with simpler, interpretable representations [14–16]. These methods transform AI from inscrutable oracle to transparent partner in clinical reasoning (figure 1).

FIGURE 1.

The pathway from black-box to explainable artificial intelligence (XAI): enhancing trust and patient care in respiratory medicine.

The distinction matters profoundly in respiratory medicine. Consider an AI system predicting acute exacerbation risk in COPD. A black-box model might generate a risk score without explanation. An XAI system would additionally reveal whether predictions rely on clinically meaningful variables (such as recent functional decline, inflammatory biomarkers, or medication adherence patterns) or on potentially misleading correlations. This transparency enables clinicians to critically evaluate algorithmic logic before incorporating recommendations into patient care.

Why clinicians need insight into the model's reasoning process

The complexity and heterogeneity of respiratory diseases demand AI systems that are both accurate and interpretable. From early disease detection to chronic disease trajectory assessment, respiratory medicine relies on nuanced, context-dependent clinical judgement. XAI enhances clinicians’ ability to interrogate, validate, and appropriately integrate algorithmic outputs, ensuring that AI augments rather than replaces professional expertise.

Interstitial lung disease exemplifies this challenge. AI may accurately classify disease subtypes based on imaging or clinical data, yet without understanding which features drive classification, clinicians cannot determine whether predictions reflect genuine pathological patterns. XAI addresses this limitation by revealing the reasoning process, allowing verification that algorithmic logic aligns with established pathophysiological principles.

This requirement for transparency extends across medical disciplines but is particularly critical in pulmonology, where small interpretative differences may significantly influence outcomes. XAI transforms AI from a tool requiring blind faith into a system supporting informed clinical judgement.

Beyond immediate decision-making, interpretability supports broader objectives. Transparent AI facilitates medical education by reinforcing pathophysiological reasoning, supports quality improvement by enabling error identification, and enhances interdisciplinary communication by providing a shared language for clinicians, data scientists, and informaticians.

Applications in respiratory medicine and support for patient empowerment

XAI applications in respiratory medicine are expanding rapidly. In thoracic imaging, explainable models highlight features underlying diagnostic predictions, enabling clinicians to cross-check outputs against their expertise [17]. In functional assessment, XAI clarifies how physiological parameters contribute to diagnostic interpretations, supporting transparent evaluation of complex pulmonary function patterns [18]. In digital health and remote monitoring, explainable systems applied to respiratory sounds, wearable sensors, and telemedicine platforms foster trust and facilitate adoption by clinicians and patients [19].

Emerging applications include exacerbation risk stratification in COPD, where XAI identifies clinical, functional, and biochemical predictors of deterioration [20]. In asthma management, explainable algorithms integrate environmental exposures, adherence patterns, and symptom data to support personalised therapeutic adjustments [21]. In lung cancer screening, XAI clarifies how nodule characteristics, smoking history, and demographic factors contribute to malignancy risk, enabling nuanced discussions around surveillance and intervention [22, 23]. In interventional pulmonology, XAI supports procedural planning and outcome prediction by integrating imaging features with patient-specific variables [24, 25].

Practical XAI examples in respiratory medicine

Several XAI implementations demonstrate tangible clinical utility. Gradient-weighted Class Activation Mapping (Grad-CAM) generates heatmaps identifying lung regions most influential in radiological classification [26]. Feature importance rankings in spirometry models reveal which parameters (such as forced expiratory volumes, diffusing capacity, or flow–volume loop morphology) drive diagnostic outputs. Decision tree visualisations for asthma exacerbation prediction display clear logical pathways from patient characteristics to risk estimates. Counterfactual explanations illustrate which variable changes would alter predictions, supporting personalised intervention planning.

An often-overlooked advantage of XAI is its potential to enhance patient engagement. Respiratory diseases frequently require long-term management with multiple therapeutic options. XAI translates complex predictions into understandable insights, enabling patients to better comprehend their condition, prognosis, and treatment choices [27]. This transparency supports shared decision-making, improving adherence and self-management in chronic conditions such as asthma, COPD, and interstitial lung diseases.

Patient-facing XAI interfaces should prioritise health literacy, using clear visualisations and avoiding technical jargon while maintaining accuracy. Across applications, interpretability enables AI to evolve from passive assistant to trusted partner in care.

Technical challenges and limitations of XAI

Widespread adoption of XAI faces significant challenges [28]. A central tension exists between model complexity and interpretability. Highly accurate deep learning architectures often resist intuitive explanation, whereas simpler, more interpretable models may inadequately capture non-linear biological relationships.

Balancing these trade-offs requires careful consideration of clinical context. In high-stakes decisions, such as transplant candidacy or mortality prediction, prioritising interpretability may be justified even at modest accuracy costs. In lower-risk scenarios, performance optimisation may reasonably take precedence. No universal solution exists; case-by-case evaluation remains essential.

Implementation barriers also include limited clinician familiarity with XAI principles, workflow integration challenges, time constraints, and scepticism regarding explanation robustness. Current XAI methods are not infallible; explanations must be clinically meaningful rather than merely technical. Addressing these issues requires collaboration among clinicians, developers, and regulators, alongside targeted education and infrastructure development.

Educational priorities

Advancing XAI in respiratory medicine requires focused educational initiatives [29]. Pulmonologists must develop competencies to critically evaluate XAI outputs, including understanding fundamental AI concepts, recognising the strengths and limitations of explanation methods, and assessing alignment with clinical evidence.

Training should emphasise practical application through case-based learning, simulations, and interdisciplinary workshops. Importantly, education must extend beyond early-career clinicians. Continuing medical education programmes should incorporate XAI modules tailored to respiratory medicine, enabling established practitioners to develop digital literacy and critical evaluation skills [19].

Clinicians must also learn to identify misleading explanations, recognise data bias, and evaluate model generalisability. These competencies are essential for responsible AI integration into clinical practice.

Regulatory and ethical considerations

Implementing XAI in respiratory medicine involves important regulatory and ethical considerations. While interpretability is increasingly recognised as a component of AI safety, standards remain evolving. Clear guidance is needed on what constitutes adequate explanation, how explanation quality should be validated, and where responsibility lies when AI informs clinical decisions.

XAI supports a broader cultural shift from accuracy-focused AI to transparent, accountable systems [28]. In respiratory care, where long-term management and patient engagement are central, this transparency enhances trust, communication, and confidence. XAI also addresses medicolegal considerations by providing auditable reasoning trails to support accountability and quality assurance.

Ethical priorities include ensuring equitable access, preventing bias, and maintaining human oversight. Transparency regarding training data provenance and limitations is essential, particularly when models are developed using demographically narrow datasets. The respiratory community must proactively shape ethical and regulatory frameworks that balance innovation with patient safety.

Future directions and recommendations

Several priorities emerge for advancing XAI in pulmonology [30]. Standardised metrics are needed to assess XAI tools’ reliability and clinical utility in respiratory medicine. Education and training programmes should equip pulmonologists with skills to critically evaluate XAI outputs. Interdisciplinary collaborations must be strengthened to ensure technical innovations remain clinically relevant and patient-centred. Policy frameworks and funding strategies should explicitly support transparent AI tools’ integration, preventing opaque, proprietary solutions from dominating the field.

Research agendas should prioritise developing explanation methods tailored to the specific needs of respiratory medicine. This includes creating visualisations and narratives that resonate with clinical workflows, validating explanations against expert interpretation, and investigating how different stakeholder groups, including patients, interpret and respond to AI explanations. Implementation-science approaches can identify barriers and facilitators to XAI adoption in diverse healthcare settings, informing strategies to maximise real-world impact.

Professional societies have vital roles in establishing best-practice guidelines for XAI implementation, curating validated tools, and fostering knowledge exchange through dedicated conferences and publications. Respiratory journals should encourage transparent reporting of AI methodologies, including explanation approaches, in published research. By leading these initiatives, the respiratory community can ensure AI serves as a true partner in clinical excellence.

Conclusion

XAI plays a pivotal role in shaping the future of respiratory medicine. Its promise lies not only in potentially improving diagnostic accuracy or efficiency, but in transforming how clinicians engage with algorithms, moving from passive recipients of opaque outputs to active interpreters of transparent insights. By aligning technological power with human judgement, XAI enhances trust, fosters accountability and advances the quality of care delivered to patients with respiratory disease.

The challenge now is for respiratory medicine to lead this transformation, embracing explainability not as optional enhancement but as a fundamental pillar of responsible, transparent, and patient-centred care. Through education, collaboration, and commitment to interpretability, the respiratory community can ensure AI serves as a true partner in clinical excellence, supporting rather than supplanting the expertise and compassion that define outstanding respiratory care. The journey toward trustworthy AI in pulmonology has begun: XAI provides the roadmap for navigating it successfully.