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The integration of artificial intelligence in medical image classification for screening has the potential to enhance efficiency, diagnostic accuracy and accessibility. However, ethical concerns such as accountability, bias, transparency and the impact on healthcare professionals remain critical. This review synthesises qualitative evidence on the ethical considerations surrounding artificial intelligence adoption in screening programmes. A systematic search of qualitative studies, from June 2020 to September 2024, was conducted across multiple databases: MEDLINE, EMBASE, PsycInfo® (American Psychological Association, Washington, DC, USA) and Cumulative Index to Nursing and Allied Health Literature. Primary qualitative studies exploring healthcare professionals', patients' and other stakeholders' perspectives on artificial intelligence in screening were included. Thematic analysis was performed, and findings were assessed using the Grading of Recommendations Assessment, Development and Evaluation-Confidence in the Evidence from Reviews of Qualitative Research approach to evaluate confidence in the evidence. Fourteen qualitative studies were included, covering perspectives from clinicians, radiologists, artificial intelligence developers, policy-makers and patients. Key ethical concerns identified included: (1) the necessity of human oversight to ensure that artificial intelligences diagnostic recommendations are appropriate; (2) challenges in assigning liability when artificial intelligence errors occur; (3) risks of algorithmic bias due to discrepancies between training data sets and real-world populations; (4) concerns over data privacy, cybersecurity and informed consent in artificial intelligence-driven decision-making; (5) the need for transparency in artificial intelligence decision-making processes to build trust and (6) potential deskilling of healthcare professionals and shifts in professional responsibilities. While artificial intelligence was seen as a valuable tool to augment clinical decision-making, stakeholders emphasised that ethical frameworks must guide its implementation to maintain public trust and patient safety. This review highlights the critical considerations that must be addressed to ensure the responsible integration of artificial intelligence in medical screening. Policy-makers, healthcare institutions and developers should prioritise human oversight, robust regulatory frameworks and strategies to mitigate bias and ensure transparency. Future research should focus on disease-specific artificial intelligence applications and long-term ethical implications. The protocol for this study is registered on PROSPERO as CRD42024599536. This award was funded by the National Institute for Health and Care Research (NIHR) Evidence Synthesis programme (NIHR award ref: NIHR172233) and is published in full in Health Technology Assessment; Vol. 30, No. 51. See the NIHR Funding and Awards website for further award information. Research is exploring if artificial Intelligence could help doctors find cancer by looking at medical images like X-rays and scans. Artificial intelligence could spot tiny signs of cancer that people might miss. This could help detect cancer and other diseases earlier and more accurately, for example in breast cancer and diabetic eye screening. Artificial intelligence can also speed up the process, so patients get results faster. However, ethical questions arise with using artificial intelligence in this way. While there are not yet specific national or international guidelines for artificial intelligence in screening, general healthcare guidance highlights the following key issues: transparency: being clear about how artificial intelligence makes decisions fairness: ensuring artificial intelligence treats everyone equally and does not discriminate against certain groups accountability: making sure someone is responsible for artificial intelligence’s actions reducing risks: ensuring artificial Intelligence systems are safe to use and do not cause harm governance and oversight: having strong systems in place to make sure artificial intelligence is used responsibly and ethically. This study examined ethical concerns of artificial intelligence in screening by reviewing research involving the general public, clinicians and patients. Initially focusing on diabetic retinopathy and breast cancer, it expanded to other conditions due to limited evidence. The study highlighted several ethical concerns raised in the literature, such as accountability for artificial intelligence mistakes, bias, data privacy, transparency and artificial intelligence’s impact on doctors’ professional roles. In addition, people in the studies included in the literature expressed worries about related issues, particularly keeping humans in control of decisions, who is responsible when errors occur and whether artificial intelligence systems can be trusted to act fairly. Ethical challenges related to the implementation of artificial intelligence in clinical screening were also highlighted. These included healthcare inequality (with resource-limited hospitals potentially not benefiting equally), risks to patient safety from delays or errors in artificial intelligence-generated reports, the need for trust through rigorous testing and the importance of clear governance guidelines to ensure that artificial intelligence remains an assistive tool rather than replacing human judgement. This study provides useful information by identifying recurring ethical concerns that can inform the development of governance frameworks, guide safe implementation of artificial intelligence in screening and highlight priorities for future research and policy. Despite providing useful information, this study has some limitations due to incomplete research available. Future studies could focus on specific diseases and ethical issues, reassessing ethical considerations as new evidence becomes available.

The Indian healthcare sector is responsible for an urgent need for quality healthcare and socio-economic development. This study investigates the effects of Green e-Human Resource Management (Green e-HRM) on sustainability performance within hospitals. Green e-HRM includes e-HRM management with environmentally friendly considerations for reduced resource usage, efficiency and enhanced social accountability. This research is premised on the resource-based view (RBV), technology acceptance model (TAM) and triple bottom line (TBL) methodologies. This study used a mixed-method approach, quantitative data collection through surveys of 380 public and private hospitals, with results from healthcare professionals' responses analysed through partial least squares structural equation modelling. It was determined that Green e-HRM significantly and positively impacts sustainability performance in hospital environmental, economic and social performance, with the most considerable impact being social sustainability. Thus, implementing digitalisation practices of Green HR can reduce environmental accountability while promoting personnel welfare and organisational social accountability. Results found that Green e-HRM is a strategic sustainability facilitator for knowledge-based resource management. Ultimately, this study attempts to fill a literature gap surrounding Green e-HRM in a developing country's healthcare system while contributing to value for practitioners seeking digital HR systems with significant sustainable developments in the healthcare SDG realm.

Statistical review is essential for research quality and integrity, yet traditional manual review is inefficient. Large language models (LLMs) offer potential support but are unreliable when used without guidance for precise calculations and raise concerns about accountability. This study evaluated whether a structured, rule-based prompt can reliably constrain an LLM to perform statistical review of comparative categorical data, and characterized both its feasibility and its inherent risks from an accountability perspective. This study employed a two-stage design based on the DeepSeekV3.2. In the first stage, a structured prompt was developed through dozens of "test-fail-iterate" cycles using 20 published medical articles. The prompt assigned the LLM the role of a "statistics expert" and provided a closed set of computational rules and a "recognize data-select calculation formula-calculate" workflow for analyzing categorical data, including Pearson's Chi-square test, continuity correction, and McNemar's tests. In the second stage, the performance of the final prompt was evaluated on a test set of 20 independent manuscripts. The model's output was compared against the results calculated by a senior statistician (the gold standard). The primary outcome measures were the performance in statistical method selection and numerical computation, including accuracy, sensitivity (recall), specificity, positive predictive value, negative predictive value, F1 score, and Cohen's Kappa. Secondary measures included reproducibility and efficiency. The test set consisted of 15 manuscripts with independent samples and 5 with paired samples. In the assessment of the appropriateness of statistical method selection for 148 analysis items, the model achieved an accuracy of 99.3% (147/148), a sensitivity of 96.2% (25/26) (F1=98.0%, κ=0.976). For the test of computational consistency in 97 independent sample tests, the accuracy for χ2 value consistency was 94.8% (92/97) (F1=89.3%, κ=0.859), and for P-value consistency, it was 96.9% (94/97) (F1=90.9%, κ=0.891). In the paired-sample analysis, the model's methods and results were in perfect agreement with the manual review, and prompt optimization eliminated discrepancies in degrees-of-freedom calculation rules. Efficiency analysis showed no statistically significant difference in time consumption between the model (407 s) and manual review (374 s) (P=0.601). In reproducibility tests, the intraclass correlation coefficients for both χ2 values and P-values exceeded 0.91. However, qualitative analysis revealed 3 typical failure modes in the task workflow: (1) Instability: The model's failure to produce identical outputs across repeated runs, manifesting as inconsistent data extraction or the failure to process all designated tasks (scope neglect). (2) Performance degradation/"lazy" behavior: A decline in execution quality on long or complex tasks, often characterized by the model abandoning its reasoning process to copy author-provided values without verification. (3) Anchoring effect: The model's tendency to over-rely on author-provided statistical values (the "anchor"), causing its verification process to be unduly influenced. A structured, rule-based prompt can guide the DeepSeek to achieve high accuracy in standardized statistical review tasks, but its reliability is contingent on operational stability. Inherent failure modes, including performance instability and a strong anchoring effect on author-provided data, persist and can lead to significant errors, particularly when source data are flawed. These findings suggest that the the DeepSeek is not suitable for autonomous auditing. Their most appropriate application is as assistive tools within a human-in-the-loop framework, where rigorous human supervision is essential for risk mitigation and to maintain ultimate accountability.

Recent changes in both federal and state-level legislation have made accessing abortion care challenging, if not impossible, for some patients. As a result, patients' reliance on comprehensive, accurate pregnancy options counseling from trusted health providers has become critical to patient access and health equity. The current generation of medical students will play an increasingly important role in counseling patients on pregnancy options. To analyze the utility of a novel pregnancy options counseling curriculum by examining preclinical medical student perspectives on professional identity development as elicited by the curriculum. This qualitative study investigated second-year medical students' perspectives on pregnancy options counseling after attending a virtual "Pregnancy Options Counseling Panel." The panel consisted of an interactive, case-based discussion of a patient diagnosed with an unintended pregnancy. After the panel, students submitted a prompt-driven reflection on professional identity development as it applies to pregnancy options counseling. The responses were coded using the AAMC Physician Competencies for Professionalism to identify themes and insights; competencies included "Physician Accountability," "Compassion, Integrity, Respect," "Patient Needs," "Patient Autonomy," "Sensitivity to Diverse Populations," and "Commitment to Ethical Principles." Coding was performed using the software Dedoose. 348 student responses were included in this analysis. The most common codes applied were "Physician Accountability" (n=416), "Compassion, Integrity, Respect" (n=201), and "Patient Needs" (n=178). Inter-reviewer reliability tests showed significant agreement for all codes (k>0.63). The majority of student responses demonstrated accountability to patients while emphasizing the importance of compassionate, nonjudgmental care. Education on pregnancy options counseling is useful in professional identity development for medical students. The curriculum challenged students to reconcile their personal beliefs with their professional obligations, and most student responses were thoughtful and empathic. Pregnancy options counseling serves as an ideal vehicle for introducing value conflict as a measure of professionalism, while simultaneously improving students' medical knowledge on pregnancy termination and family planning.

Large language models (LLMs) are proposed as scalable solutions for health workforce gaps in low- and middle-income countries, yet deployment risks deepening inequities. LLM evaluation remains disproportionately centered on English-language resources, and current reporting standards do not operationalize linguistic parity, data sovereignty, or accountability for specific language communities, care pathways, and regulatory arrangements. We propose three minimum governance commitments: parity before scale, requiring pre-specified performance thresholds or non-inferiority margins by language and care setting before deployment; local ownership before extraction; and accountability before integration. Systems failing these thresholds should be restricted, redesigned, or subject to additional oversight before scale-up.

The Health Equity Report Card (HERC) was developed to establish best practice recommendations and promote accountability among health systems in addressing care inequities. Recognizing the growing importance of health equity, particularly in cancer care, this study aims to evaluate the feasibility of implementing the HERC within academic cancer centers and to gather insights on its benefits and challenges during implementation to improve its usability. This quality improvement study used a mixed-methods approach, collecting both quantitative and qualitative data over an 18-month period from April 2022 to October 2024 from 5 NCCN Member Institutions. Participants completed self- and third-party scores, as well as survey evaluations providing feedback on the process of using the HERC. Feedback from stakeholders was systematically collected to assess usability and identify areas for improvement. All participating sites successfully achieved the feasibility objectives by completing the self- and third-party scoring processes using the HERC, with unanimous agreement among the sites regarding the feasibility of the HERC for implementation. Site feedback indicated areas for enhancement, particularly in improving question clarity and simplifying the scoring process. Continuous feedback loops facilitated iterative improvements to the HERC, ultimately enhancing user experience and the scoring process. The HERC demonstrates strong potential as a viable framework for prioritizing and assessing equity in care delivery within academic cancer centers. The successful implementation across multiple sites, along with positive stakeholder feedback, underscores its utility in enhancing accountability and promoting best practices in addressing health inequities. Future studies should explore the long-term impacts of HERC implementation on patient outcomes and equity in care delivery. A study testing the HERC for applicability in community oncology settings is ongoing.

Access to real-world healthcare data is increasingly hindered by privacy concerns and stringent legal frameworks, including the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA). Consequently, synthetic healthcare data generators, notably Synthea™, have emerged as essential tools to generate high-quality datasets with minimal privacy and legal concerns. However, many simulation engines including, Synthea™, predominantly rely on command-line interfaces (CLI), imposing significant technical barriers for clinical researchers. Furthermore, raw output formats such as HL7 Fast Healthcare Interoperability Resources (FHIR) JSON create a substantial "interpretability gap" for end-users. To address these challenges, we present SyntheaWeb, a web-based platform that simplifies the generation and interactive inspection of synthetic patient cohorts. It provides a user interface, visual cohort dashboard, structured longitudinal patient records, semantic terminology linking (e.g., SNOMED CT, LOINC), and the capability for selective subset export.

Medical imaging data are an essential resource for research and teaching; however, regulations such as the Health Insurance Portability and Accountability Act require careful consideration of protected health information (PHI) contained within images and their metadata. De-identification, the removal of PHI to prevent subject re-identification, is often essential for regulatory compliance. In pathology, whole-slide imaging (WSI) files-high-resolution scans of entire glass slides-are essential for research and machine learning model training. Currently, many scanner vendors use proprietary WSI formats; some vendors offer de-identification tools, which are often manual and tedious to use. Alternatively, physical slides may be re-scanned with obfuscated labels, though this is resource intensive. Here, we describe an institutional-scale automated WSI de-identification pipeline, which utilizes an informatics-based approach to convert clinical WSI from 11 proprietary formats into de-identified WSI in multiple open formats. This approach minimizes manual processes and additional slide-scanning hardware, storage, and personnel. De-identification requests are submitted through a zero-footprint web portal following the Fast Healthcare Interoperability Resources ServiceRequest data model. De-identification is verified via a human-in-the-loop review, and archives are distributed using cloud-based storage. Since deployment in November 2024, our pipeline has de-identified 819 cases comprising 4322 WSI files generated by 7 scanner models in 4 formats. We demonstrate that the conversion process is predictable, linearly scalable, and reliable across de-identification request sizes (238× variation), image sizes (120× variation), and capture format. This pipeline eliminates the need for manual de-identification or re-scanning while achieving high throughput and reliability at an institutional scale.

Victim-blaming and entrenched gender norms are a persistent barrier to justice and support pathways for victim-survivors of sexual and domestic violence in Fiji. While these issues have been considered in relation to physical forms of abuse, there remains limited understanding of how these attitudes may impact victim-survivors of technology-facilitated sexual violence, particularly image-based sexual abuse-the non-consensual creation, capture, distribution, or threatened distribution of nude or sexual imagery. This article examined these dynamics drawing on a reflexive thematic analysis of focus groups run with 30 young people aged 18-24 years across the Fijian cities of Suva, Labasa, and Lautoka. The focus groups explored young people's attitudes toward image-based sexual abuse victimization, including using the illustrative case study of former Fijian Minister for Women, Children and Social Protection, Lynda Tabuya, who had a private sexual video of her disseminated online without her consent and was subsequently dismissed from her Ministerial position. Our paper explores how cultural, religious, and moral frameworks shape perceptions of victimhood, purity, and blame among young Fijians. Our findings reveal that patriarchal and moralistic narratives contributed to a hostile environment for image-based sexual abuse victim-survivors, where they are often silenced and held responsible for their abuse, while perpetrators evade accountability. This research contributes to the growing literature on gender-based violence in Fiji and underscores the need for culturally grounded education and awareness initiatives that challenge victim-blaming and patriarchal narratives to support justice for victim-survivors, such as targeted youth workshops on consent and digital harms, run in partnership with faith-based organizations and community leaders.

To examine UK general practitioners' (GPs) adoption of ambient artificial intelligence (AI) scribes and to assess user-reported error rates, workflow impact and consent practices in primary care. We conducted a nationwide online mixed-methods survey of GPs recruited via Doctors.net.uk. Detailed analyses of use, errors and workflow impact focused on current users of ambient AI scribes. In August 2025, of 1003 respondents, 14% (n=141) reported current use of ambient AI scribes, 39% (n=396) intended to adopt them soon and 46% (n=466) had no plans to use them. Among users (n=141), Heidi Health predominated (86%). Most reported efficiency gains: 80% (n=112) reported reduced time spent on documentation and 70% (n=99) reduced cognitive load. Documentation quality was judged positively, with 55% (n=78) rating outputs as better than standard notes. Errors were common but usually minor: 32% (n=45) reported errors often/always, including 14% (n=20) with significant-to-critical implications. Errors were most frequent in multiparty consultations (38%), complex histories (35%) and non-English encounters (31%). Consent practices varied: 63% (n=89) routinely sought consent, with ≤10% of patients declining. Free-text responses (21% of users) highlighted benefits for workflow, alongside concerns about accuracy, ethics and system integration. Findings suggest that ambient AI scribes deliver meaningful efficiency gains and improved perceived documentation quality, but introduce non-trivial risks related to accuracy, equity and medicolegal accountability. The uneven performance in complex and multilingual consultations raises particular concerns about potential exacerbation of healthcare disparities. Ambient AI scribes are already in use across UK primary care. Proactive regulation, consistent consent practices and independent evaluation, including patient perspectives, are urgently needed to ensure safe, equitable and sustainable implementation.

This research critically examines how adequately currently available coach education programs supports sports coaching and sports development in Oman. It also investigates how the perceptions towards coach education effectiveness, barriers, and privatization are influenced by variables such as job title and experience. An exploratory cross-sectional case study design and quantitative measures have been used to investigate coach education programs in Oman. A total of 159 stakeholders provided responses through a nation-wide online survey that was administered from September 10 to November 15, 2024. Data collected in this study were analyzed using a number of techniques, including the Kruskal-Wallis H test and the Mann-Whitney U-test. The finding indicates that the current coach education program in Oman is underdeveloped and ineffective. More specifically 75.7% of sports stakeholders support reform in coach education. The stakeholders exhibited modest differences towards coach education privatization. While privatization provides opportunities for flexibility and revenue-generation, it is not a sufficient answer on its own due to trade-offs inherent to equity, accountability, and sustainability. The way forward would involve creating a multitude of funding sources with strong regulation so as to ensure compliance across the system. Further, the findings suggest that a hybrid system (i.e., combining public oversight with private and digital innovations) may represent a promising approach to addressing existing challenges in coach education. Finally, improving coach education in Oman necessitates systemic reform through the better integration of governance, pedagogy, and contextual relevance.

Deaths in custody represent a critical intersection between health care, law enforcement and human rights. By depriving an individual of liberty, the State assumes an enhanced duty to protect life and ensure access to adequate medical care. Any death occurring in custodial settings must therefore be regarded as potentially suspicious until clarified through a prompt, independent and technically robust investigation. This systematic review synthesizes and critically examines scientific evidence on deaths in police and prison custody, with particular emphasis on physical restraint, pathophysiological mechanisms, and medico-legal investigation practices. A systematic search of PubMed and Scopus (2010-2025) was conducted in accordance with PRISMA 2009 guidelines. The evidence indicates that deaths in custody are predominantly multifactorial, resulting from the interaction between physical restraint, intense physiological stress, intoxication with psychoactive substances and individual vulnerabilities such as obesity or underlying cardiac disease. The concept of "excited delirium" remains highly controversial, lacking validated diagnostic criteria or specific biomarkers and is frequently used as a diagnosis of exclusion in medico-legal contexts. Physical restraint, particularly prolonged prone positioning with thoracic or cervical compression, may precipitate metabolic acidosis and restraint-associated cardiac arrest, even in the absence of significant hypoxemia. Experimental studies in healthy volunteers fail to reproduce real-world custodial conditions and should not be used to negate associated risks. Rigorous and independent medico-legal investigation, including complete autopsy, toxicological analysis and contextual reconstruction, is essential for accurate causal attribution and institutional accountability. These findings have direct implications for forensic practice, death certification and the prevention of avoidable deaths in custodial settings.

Antibiotic use surged during COVID-19 despite it being a viral illness, heightening global concerns about antimicrobial resistance (AMR). In Türkiye - where antibiotic consumption and AMR were already high - this paradox exposed longstanding tensions between policy and practice. Türkiye has adopted World Health Organization guidelines promoting the "rational" (akılcı) use of antibiotics, yet everyday healthcare encounters reveal how these reforms are translated, negotiated and reinterpreted. Drawing on fieldwork in Istanbul, this article shows how local expectations of antibiotics as symbols of care and professional competence shape prescription and use. Rather than aligning practice with policy, stewardship reforms are absorbed into existing moral and relational norms, reinforcing rather than transforming existing dynamics of care. Global stewardship attempts, as they filter through local systems, understandings and situated priorities, are vernacularized in ways that diverge from their original intentions - redistributing accountability without addressing the structural conditions that shape antibiotic use. COVID‐19 viral bir hastalık olmasına rağmen pandemi sürecinde antibiyotik kullanımı ciddi ölçüde arttı; bu durum antimikrobiyal direnç (AMD) konusundaki küresel kaygıları çoğalttı. Antibiyotik tüketiminin ve AMD oranlarının zaten yüksek olduğu Türkiye'de ise bu çelişki, reformlar ile günlük pratikler arasındaki köklü gerilimleri açığa çıkarıyor. Türkiye, Dünya Sağlık Örgütü'nün antibiyotiklerin “akılcı” kullanımını teşvik eden kılavuzlarını benimsemiş olsa da bu reformlar günlük hayatta tekrar yorumlanıp dönüştürülüyor. İstanbul'da yürütülen saha araştırmasına dayanan bu makale, antibiyotiklerin sağlık hizmetinin ve mesleki yetkinliğin bir simgesi olarak görüldüğünü ve bu algının reçete yazma ile kullanım alışkanlıklarını doğrudan şekillendirdiğini gösteriyor. Antibiyotik reformları yerleşik normlara uyarlanıyor; küresel girişimler yerel koşullarla buluştuğunda asıl amacından uzaklaşarak yapısal sorunları çözmek yerine sorumluluğu bireylere yüklüyor.

To explore the data risk perception structure and connotation in the entire process of generative AI-enabled nursing research and to identify healthcare management and training needs as applied to digital health developments. Nursing research highly relies on contextualized and unstructured data. General generative AI still faces shortcomings in professional adaptation, data governance, and responsibility definition, which may lead to risks such as privacy leaks, amplified bias, academic misconduct and accountability vacuums. The study focusses on the perceptions of Future nursing professionals. Purposeful maximum variance sampling was used to recruit 20 participants from 3 universities, and semi-structured one-on-one interviews were conducted. The report followed the COREQ Protocol checklist. Five data risk awareness themes were identified: data adaptation risk, data security risk, data quality risk, data ethics risk, and response risk, presenting risk concerns throughout the entire process of "use-generation-sharing-responsibility". The data risk perception of nursing master's students regarding generative AI-enabled nursing research presents a clear five-dimensional structure, unfolding along the chain of "input-processing-output-diffusion-attribution." This structure supports the development of a framework for defining boundaries of AI use, data governance, ethical compliance, and capacity building in nursing research settings and digital health developments.

Artificial intelligence (AI) is increasingly being integrated into healthcare, particularly in data-intensive chronic diseases that rely on longitudinal monitoring and shared decision-making. Multiple sclerosis is a prototypical example of such care, but real-world benefit will depend on whether people accept AI support in different clinical roles. We conducted a cross-sectional, web-based survey among 241 people with MS (pwMS) to assess comfort with AI across eight clinical domains and to identify predictors of acceptance. We derived an artificial-intelligence attitudes composite with high internal consistency (Cronbach alpha&#x2009;=&#x2009;0.90). Overall acceptance was moderate (mean 3.39&#x2009;&#xb1;&#x2009;0.78). Acceptance differed across domains, demonstrating a responsibility gradient: comfort was highest for supportive applications such as chronic management (54.4%) and symptom screening (50.2%), but lower for treatment selection (38.6%) and diagnosis (35.3%; P&#x2009;<&#x2009;0.001). In multivariable models, frequent general AI use (at least weekly; 30.7%) was the strongest independent predictor of acceptance (P&#x2009;<&#x2009;0.001). Acceptance also differed by region (Eastern vs Western Germany, P&#x2009;=&#x2009;0.025), whereas clinical disability was not significantly associated. Older age was associated with lower acceptance of AI-supported management. Most participants viewed AI as a logistical support tool but, assuming equal diagnostic accuracy, 78.8% preferred joint artificial-intelligence-clinician decision-making with clinician final responsibility. These findings indicate that acceptance may be context-dependent and more strongly associated with prior familiarity than with disease severity. Implementation should move beyond technical validation to transparent, clinician-led 'human-in-the-loop' workflows with explicit accountability and staged adoption beginning with low-risk use cases.

Learning processes, cognitive architectures, available resources, and methods for sampling the environment and generating intelligent responses in complex sensory domains can differ significantly between natural and artificial systems. In this work, we present theoretical and modeling-based analysis of early-stage learning under resource constraints, comparing biological intelligence with a class of freely evolving, weakly constrained artificial systems (FEW), focusing on essential resource constraints such as computational capacity, memory, and energy. We develop quantitative models of sensory exploration and learning under strong and weak resource constraints, formalizing how limitations in energy, memory, and computational capacity shape sampling strategies and learning dynamics. For biologically constrained systems, we show that steep anisotropy in the cognitive cost gradient induces prioritized, depth-oriented exploration within limited sensory regions, leading to robust and resource-efficient learning. In contrast, we demonstrate that FEW systems, despite access to abundant resources, face a paradox of unconstrained learning: in the absence of intrinsic prioritization and evaluative feedback, uniform or random sampling leads to inefficient exploration of the sensory domain. To examine this challenge, we introduce a comparative framework for evaluating sensory traversal strategies and show that no single strategy dominates across prioritization accuracy, robustness, and resource efficiency. Instead, our analysis suggests a meta-strategy approach, in which adaptive selection among exploration strategies optimizes empirical success while preserving empirical accountability required for adaptive optimization. These results clarify the functional role of constraints in biological learning and provide principled guidance for the design of next-generation artificial learning systems operating in complex sensory environments.