Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 6 appraisals
Balkan medical journal
Bias and Fairness Across the Healthcare AI Lifecycle: A Clinician-Oriented Review
Artificial intelligence (AI) is increasingly being investigated and, in selected clinical settings, implemented to support diagnosis, triage, and workflow optimization. Although these systems have the potential to improve access, consistency, and efficiency, they may also reproduce or amplify health inequities when bias is introduced during development, evaluation, implementation, or postdeployment use. This clinician-oriented narrative review adopts a practical lifecycle approach to explain how algorithmic unfairness becomes clinically relevant, how clinicians can recognize it, and how institutions can mitigate its impact. We first outline the ethical, clinical, and mathematical dimensions of fairness. We then examine fairness risks and sources of bias across six stages of the healthcare AI lifecycle: problem formulation, data generation, model development, evaluation, implementation, and postdeployment monitoring and governance. Key mechanisms include biased proxy outcomes, unrepresentative or error-prone data and labels, model shortcut learning, hidden stratification, distribution shift, and human-AI interaction effects (e.g., automation bias and alert fatigue), all of which can create feedback loops and contribute to fairness drift over time. For each stage, we identify clinician-facing red flags and practical mitigation strategies, including defining clinically meaningful outcomes, using representative and well-documented datasets, conducting subgroup-stratified evaluations, performing external and prospective validation, justifying decision thresholds, implementing safeguards for human-AI interactions, and maintaining continuous postdeployment monitoring, including postmarket surveillance for regulated medical devices. Fairness cannot be ensured through a single metric, publication, regulatory clearance, or one-time validation. Instead, equitable healthcare AI requires transparent design, rigorous evaluation, local governance, and ongoing monitoring across diverse populations, clinical sites, devices, workflows, and time. Fairness should therefore be regarded as a continuous clinical and institutional responsibility rather than a downstream technical consideration.
1 Aug 2026
Read appraisal →International journal for equity in health
A conceptual framework for measuring AI health equity
BACKGROUND: Artificial intelligence (AI) is increasingly embedded in health systems globally and has the potential to improve efficiency, diagnostic accuracy, and decision support. However, its benefits remain unevenly distributed, particularly in low- and middle-income countries (LMICs). Models developed using datasets from specific populations may perform poorly in other settings, reinforcing structural inequities rather than correcting them. OBJECTIVE: This viewpoint proposes a composite framework, the AI in Healthcare Equity Index (AIHEI), to support measurable assessment of equity in health AI systems. FRAMEWORK: The AIHEI is designed to assess equity across five domains: data representation, algorithmic fairness, transparency and explainability, governance and oversight, and community impact and benefit sharing. By generating a standardised score, the index could enable comparisons across technologies, incentivise improvement, and support regulation, procurement, publication, and funding decisions. IMPLICATIONS: Pilots across diverse health domains and geographic settings are needed to assess feasibility, refine domain weighting, and evaluate reliability, reproducibility, and validity. Important challenges include contextual definitions of fairness, data sovereignty, post-deployment monitoring, and the risk of metric gaming. CONCLUSIONS: Quantifying equity in health AI is essential to ensure that AI does not create, widen, or exacerbate existing disparities by neglecting underserved populations. A common, objective measure of AI-related health equity can help move the field from ethical aspiration toward measurable accountability, monitoring, and enforcement.
25 July 2026
Read appraisal →Journal of medical ethics
AI interventions in cancer screening: balancing equity and cost-effectiveness
This paper examines the integration of artificial intelligence (AI) into cancer screening programmes, focusing on the associated equity challenges and resource allocation implications. While AI technologies promise significant benefits-such as improved diagnostic accuracy, shorter waiting times, reduced reliance on radiographers, and overall productivity gains and cost-effectiveness-current interventions disproportionately favour those already engaged in screening. This neglect of non-attenders, who face the worst cancer outcomes, exacerbates existing health disparities and undermines the core objectives of screening programmes.Using breast cancer screening as a case study, we argue that AI interventions must not only improve health outcomes and demonstrate cost-effectiveness but also address inequities by prioritising non-attenders. To this end, we advocate for the design and implementation of cost-saving AI interventions. Such interventions could enable reinvestment into strategies specifically aimed at increasing engagement among non-attenders, thereby reducing disparities in cancer outcomes. Decision modelling is presented as a practical method to identify and evaluate these cost-saving interventions. Furthermore, the paper calls for greater transparency in decision-making, urging policymakers to explicitly account for the equity implications and opportunity costs associated with AI investments. Only then will they be able to balance the promise of technological innovation with the ethical imperative to improve health outcomes for all, particularly underserved populations. Methods such as distributional cost-effectiveness analysis are recommended to quantify and address disparities, ensuring more equitable healthcare delivery.
25 July 2026
Read appraisal →Journal of medical Internet research
Evaluation Frameworks for Clinical AI Incorporating Validation Strategies, Real-World Applicability, and Ethical Principles: Scoping Review
BACKGROUND: AI shows substantial potential in health care; however, the absence of standardized evaluation frameworks limits its safe and effective clinical implementation because of inconsistent validation requirements and fragmented ethical principles. Existing guidelines vary in structure, methodological rigor, and ethical integration, creating uncertainty. OBJECTIVE: This study aimed to systematically map, characterize, and critically analyze existing evaluation frameworks for clinical AI, focusing on three core dimensions: methodological rigor, validation strategies (internal validation, including reporting of technical and clinical performance; external validation, including real-world applicability), and alignment with the United Nations Educational, Scientific and Cultural Organization (UNESCO) AI ethical considerations. METHODS: A scoping review was conducted following PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. Six databases (PubMed, Embase, BVS, EBSCOhost, ProQuest, and Sage) and the Enhancing the Quality and Transparency of Health Research Network were searched without language or date restrictions up to February 2026. Eligible documents included peer-reviewed papers, gray literature, and organizational guidelines describing evaluation or reporting frameworks for clinical AI. Editorials, commentaries, and conference abstracts lacking a clearly defined evaluative framework or clinical applicability were excluded. Two reviewers independently screened records and extracted data. Data were extracted across three domains: (1) general characteristics, (2) methodological rigor and validation parameters, and (3) ethical integration and were synthesized using a dot plot-based gap map. Ethical adherence was assessed using a 10-domain UNESCO-based scoring matrix. No formal risk-of-bias assessment was conducted, consistent with scoping review methodology. RESULTS: From 3363 records, 46 frameworks met the inclusion criteria. Mapping revealed a rapidly expanding but fragmented landscape. Most frameworks targeted investigational use (88%), with limited focus on clinical applicability. Frameworks varied in structure, methodology, and scope, with a predominance of reporting guidelines and few validated tools. Most (63%) were developed through multi-institutional collaborations, and 32.6% incorporated transdisciplinary participation. Only 31.8% reported technical metrics (commonly area under the curve, sensitivity, and specificity), and 15.9% provided clinical indicators (eg, predictive values or calibration). Only 11.4% achieved methodological rigor, incorporating validation aligned with intended use, while most relied on partial validation strategies, highlighting a gap between model development and clinical evaluation. Ethical integration was heterogeneous: only 5 frameworks achieved high compliance (≥80%), whereas 4 scored <10%. The most frequently addressed UNESCO principles were awareness and education (71.1%) and transparency and explainability (70%), while human oversight (24.4%) and adaptive governance (33.3%) were least represented. Findings indicate a misalignment between framework design, validation requirements, and clinical implementation. CONCLUSIONS: Evaluation frameworks for clinical AI remain heterogeneous and oriented toward investigational contexts. Critical gaps persist in methodological rigor, validation aligned with intended use, and fragmented ethical coverage. These findings highlight the need for standardized, robust, and ethically grounded frameworks to enable safe, reliable, and scalable integration of AI into clinical practice.
24 July 2026
Read appraisal →International journal of qualitative studies on health and well-being
Between duty and constraint: a qualitative systematic review of healthcare providers' ethical challenges and moral stressors in caring for undocumented migrants
PURPOSE: Healthcare providers play a critical role in delivering care to undocumented migrants, who face systemic barriers to healthcare access. While research has documented undocumented migrants' legal and policy barriers, less is known about the ethical challenges and moral stressors experienced by providers. This systematic review synthesizes qualitative studies on providers' experiences when delivering care to undocumented migrants. METHODS: The review followed PRISMA guidelines. PubMed, Embase, CINAHL, and the Cochrane Library were searched for relevant qualitative studies. Studies were screened by title, abstract, and full text using predefined inclusion and exclusion criteria. Quality assessment was conducted using the CASP checklist. Data were synthesized using the Qualitative Analysis Guide of Leuven (QUAGOL), integrating Graneheim and Lundman's approach to qualitative content analysis. RESULTS: The systematic search identified 37 qualitative studies. Analysis revealed 58 themes and subthemes, organized under five key concepts: experiences, perceptions, attitudes, practices and coping mechanisms, and ethical challenges. Providers reported moral distress, emotional exhaustion, and professional dilemmas arising from legal constraints, resource limitations, and conflicting obligations. Ethical tensions centered on beneficence vs. non-maleficence, professional duty vs. legal compliance, and the moral dilemma of deservingness. Across these findings, the synthesis identifies ethical burden-shifting as a central analytical contribution: restrictive systems transfer the moral and practical consequences of exclusionary arrangements onto providers and undocumented migrants at the point of care. CONCLUSION: This review shows that providers' ethical challenges are not only individual clinical dilemmas but structurally generated moral stressors. Addressing these challenges requires structural interventions, including policy reforms that reconcile professional ethics with legal constraints and institutional support to mitigate moral distress.
10 July 2026
Read appraisal →Current psychiatry reports
Is Technology Remaking Therapy: The Screen and the Self: Telepsychiatry, AI Therapy, and the Defense Against Intimacy
PURPOSE OF REVIEW: This paper examines several methods of technology that have challenged traditional expectations of the meaning of psychotherapy, from the widespread adoption of telepsychiatry to the subsequent emergence of AI-driven therapeutic agents (Therabots). RECENT FINDINGS: Widespread usage of new technology that impacts the therapeutic process has outpaced an analysis of how that technology might affect the meaning and effectiveness of that process. Lawsuits assume such technology causes harm, while limited data and the literature has been more mixed. From Frankenstein to CRISPR, new technology always has its cheerleaders and its detractors. The more the technology seems to impact a topic especially connected to our humanity, the deeper the convictions will be on both sides. Certainly, when it comes to psychotherapy, the introduction of new technologies such as telepsychiatry to Therabots has provoked discussion. We argue that while new technologies offer practical advantages, they risk functioning as structural defenses against the vulnerability and authentic intimacy essential to transformative psychotherapy. Through analysis of the therapeutic alliance, relational dynamics, and the psychology of vulnerability, this paper contends that the structural form of telepsychiatry does not alter the inherent nature of the therapeutic experience, whereas AI-mediated therapy may collude with maladaptive defenses, fundamentally altering the nature of the therapeutic encounter.
1 July 2026
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