A comprehensive search was conducted across major academic databases, including:

PubMed/MedlineScopusIEEE XploreScienceDirectGoogle Scholar (as supplementary)

Search terms included combinations of:

“artificial intelligence”“machine learning”“deep learning”“healthcare diagnostics”“medical imaging AI”“predictive analytics”“clinical decision support”“diagnostic accuracy”“algorithmic bias”“data quality healthcare AI”

Boolean operators (AND, OR) were used to combine terms. Reference lists of included papers were scanned to identify additional relevant literature.

Inclusion criteria:

Articles published between 2020 and 2025Peer-reviewed journal articles or reputable conference proceedingsStudies focusing on AI applications in diagnosis, risk prediction, medical imaging, genomics, or clinical decision supportResearch that reports performance metrics, methodological details, or clinical validationStudies discussing data quality, bias, explainability, or ethical and regulatory issues in AI diagnostics

Exclusion criteria:

Non-English publicationsStudies focusing exclusively on robotics, non-diagnostic administrative systems, or wearable hardware without a diagnostic contextOpinion pieces or editorials lacking an empirical or methodological basisArticles without an available full text

The database search returned several hundred records. After removal of duplicates, titles and abstracts were screened for relevance to artificial intelligence in healthcare diagnostics. Articles that were clearly unrelated to diagnostic applications, not focused on healthcare, or lacking sufficient methodological detail were excluded at this stage.

Full-text versions of the remaining articles were then assessed against the predefined inclusion and exclusion criteria. Studies that did not address diagnostic use cases, did not report methodological or performance details, or were purely opinion-based without analytical grounding were excluded.

In total, 84 articles met the eligibility criteria and were included in the final synthesis. These studies covered a range of topics including medical imaging, predictive diagnostics, clinical decision support, explainable AI, ethical and regulatory issues, and implementation challenges in both high-income and low- and middle-income country (LMIC) settings.

For each included study, the following variables were extracted when available:

AI methodology used (for example, CNN, RNN, transformer models, NLP approaches)Dataset characteristics (size, imaging modality, demographic diversity)Ground truth definition and annotation processValidation strategy (train-test split, cross-validation, external validation)Diagnostic performance metrics (accuracy, AUC, sensitivity, specificity)Ethical or regulatory notes (privacy, consent, bias, explainability, liability)

Studies were grouped into thematic categories to support structured synthesis, including medical imaging, predictive analytics, decision support, real-time monitoring, LMIC implementation, and cross-cutting ethical and regulatory concerns.

2.5.1. Data Quality, Curation, and Preprocessing

High-quality datasets are essential for developing reliable AI diagnostic systems. Many studies emphasized challenges such as limited dataset diversity, missing or incomplete clinical values, inconsistent imaging protocols, variation in EHR structures, and the presence of artifacts or low-resolution images [8][9]. Data preprocessing often requires cleaning and normalization, rigorous anonymization to comply with privacy laws such as HIPAA and GDPR [10][11], reduction of noise and imaging artifacts, and balancing of class distributions. Poor-quality or non-representative datasets can propagate errors throughout the diagnostic pipeline and adversely affect model performance.

2.5.2. Annotation and Ground Truth Challenges

Developing labeled medical datasets is difficult and labor-intensive. It typically requires expert annotators such as radiologists and pathologists, and is subject to inter-observer variability [12] and is often underfunded in low-resource settings [13]. Multiple reviewers are frequently needed to ensure consistency. Several studies note the scarcity of well-annotated data as a major impediment to robust AI model development [14][15].

2.5.3. Security Foundations: Fault Detection and Cryptographic Integrity

The security of embedded AI diagnostics, especially within IoT-enabled medical systems, increasingly depends on integrating robust fault detection mechanisms and cryptographic hardening to safeguard against adversarial manipulation and operational faults. Recent advances in side-channel-resistant cryptographic implementations, such as optimized Ed25519 architectures for constrained processors, demonstrate how lightweight yet robust cryptography can protect medical devices from leakage-based threats [16]. Furthermore, with the advent of post-quantum cryptography, implementations like Fast SIKE Round 3 on ARM Cortex-M4 highlight ongoing efforts to maintain cryptographic security in embedded medical devices against future quantum threats [17]. Equally important, hardware-level fault detection schemes, including those using Cyclic Redundancy Check for finite-field computations, help prevent fault-injection attacks that could alter model outputs or compromise diagnostic integrity [18]. Additionally, the development of fault detection architectures specifically designed for FPGA implementations involving finite-field operations further enhances the resilience of these systems [19]. Incorporating these techniques within AI-driven diagnostic pipelines strengthens the trustworthiness of medical inference processes and ensures that clinical decisions remain secure, verifiable, and resistant to tampering in real-world healthcare environments.

Beyond hardware-level fault detection, AI diagnostic pipelines must also address broader security threats documented in prior research. Early studies on systematic poisoning attacks in healthcare [20] demonstrate how corrupted training data can compromise model reliability, highlighting the need for robust data provenance safeguards. Likewise, advances in secure hardware implementations for EdDSA signatures (Ed25519/Ed448), FPGA-optimized SIKE architectures, and reliable hash functions such as ECHO and Fugue provide practical cryptographic pathways for strengthening integrity, authentication, and tamper-resistance in embedded medical AI systems.

2.5.4. Dataset Diversity and Generalizability

A major theme across the literature is that AI models trained on homogeneous datasets perform poorly on diverse populations. Contributing factors include data silos across institutions, geographic and ethnic underrepresentation, heterogeneous imaging protocols, and lack of national or global reference datasets [21]. Performance degradation of 15 - 20 percent across hospitals has been reported when models are evaluated on unseen data distributions [22][23].

2.5.5. Ethical and Privacy Considerations in Data Handling

Ethical issues related to data handling include strict consent requirements, institutional review board approvals, the need to protect sensitive patient information through robust de-identification, restricted data access due to regulatory barriers, and the risk of systemic bias arising from skewed training datasets [8][24]. Deep learning’s black-box nature raises accountability concerns and underscores the need for transparent documentation and validation of model development processes.

2.5.6. Bias, Equity, and Fairness Challenges

AI tools often underperform in underrepresented demographic groups, leading to diagnostic disparities, unfair clinical outcomes, and amplification of existing health inequities [7][25]. Suggested mitigation strategies include more diverse sampling, fairness-aware algorithms, subgroup performance reporting, and careful evaluation of heterogeneity in datasets [9][26].

2.5.7. Data Access Barriers and Infrastructure Limitations

Many studies highlight difficulties accessing high-quality medical data, lack of standardized infrastructure, privacy restrictions between institutions, insufficient computational capacity in LMICs, and dependence on third-party annotation companies [27][28]. These barriers limit the scalability and external validity of AI diagnostic models.

2.5.8. Importance of Ethical Oversight and Regulatory Compliance

Ethical oversight mechanisms, including written consent, review by ethics committees, rigorous data inspections, and adherence to local and international regulations, are critical for ensuring that AI diagnostic research respects patient rights and institutional responsibilities [12][29][30].

A total of 84 studies were included. Although many publications addressed multiple aspects of AI in healthcare, their primary focus areas could be grouped into several broad categories. Approximately 12 studies (14.3 percent) concentrated mainly on medical imaging and computational pathology, including applications in radiology, oncology, and dermatology. Around 18 studies (21.4 percent) focused on diagnostic and predictive modeling, such as disease risk prediction, outcome forecasting, and AI-enabled personalized medicine.

A smaller subset of about 7 studies (8.3 percent) examined clinical decision support systems and AI-based diagnostic copilots, with an emphasis on workload reduction, triage, and integration into clinical workflows. Roughly 19 studies (22.6 percent) dealt primarily with ethical, regulatory, fairness, and explainability questions surrounding diagnostic AI, including algorithmic bias, transparency, and liability. Approximately 8 studies (9.5 percent) explored implementation, infrastructure, and context-specific challenges, particularly in LMICs and in real-world deployment settings.

The remaining 20 studies (23.8 percent) were broad narrative or scoping reviews that spanned multiple domains, simultaneously discussing diagnostic performance, clinical integration, ethical implications, and health system impacts.

3.2.1. AI in Medical Imaging and Advanced Pattern Recognition

Recent years have witnessed a profound acceleration in the development and application of artificial intelligence across engineering, computing, and particularly medical domains [31]. In healthcare, AI-driven technologies are reshaping diagnostic and therapeutic processes by enabling complex data analysis, enhancing clinical accuracy, and supporting personalized decision-making [2][32]. This section synthesizes major thematic areas emerging from the literature review.

A dominant theme concerns the integration of AI into medical imaging. Deep learning and convolutional neural networks (CNNs) have demonstrated superior performance in detecting patterns and anomalies in X-rays, CT scans, MRIs, ultrasounds, and other imaging modalities [1][33]. CNNs excel at segmentation, classification, and anomaly detection due to their capacity to learn hierarchical visual features with high precision [34].

3.2.2. Machine Learning for Clinical Data, Prediction, and Personalized Medicine

AI models have achieved diagnostic performance comparable to, and in some domains exceeding, expert clinicians. Examples include dermatology, where deep learning systems match specialist-level accuracy, and breast cancer screening, where AI reduces false positives and false negatives in mammography [6][35]. AI-assisted imaging has been applied to earlier detection of cancers, cardiovascular irregularities, neurological lesions, and genetic disorders [36]. These systems support more accurate, timely, and cost-effective diagnostic pathways and can reduce unnecessary interventions and radiation exposure [37].

Beyond imaging, AI methods such as machine learning algorithms, ensemble models, and deep neural networks analyze heterogeneous clinical datasets, including laboratory results, vital signs, and genomic information, to identify disease trajectories and predict patient outcomes [2][38]. These models process clinical histories, biochemical tests, genomic variants, and large-scale EHRs, uncovering non-linear patterns indicative of disease progression or risk [39][40].

In precision medicine, AI systems integrate patient-specific data to personalize therapeutic strategies, optimize drug choices, minimize adverse events, and support individualized care pathways [41][42]. By leveraging big data analytics, AI shifts healthcare from reactive, symptom-based practice toward a more proactive and preventative paradigm [43].

3.2.3. AI in Clinical Decision Support Systems and Workflow Optimization

AI-powered decision support tools play a central role in reducing diagnostic errors and enhancing clinician performance. These systems synthesize complex patient data, generate differential diagnoses, reference medical literature and guidelines, and highlight risk factors and disease correlations [3][6][44]. They can reduce cognitive burden, mitigate human variability, and improve treatment pathways across specialties.

AI decision support systems also shorten diagnostic timelines by automating routine image analyses, filtering irrelevant information, and prioritizing critical findings [45][46]. This is particularly valuable in high-pressure environments such as emergency medicine, oncology, and intensive care.

3.2.4. Real-Time Monitoring and Remote Diagnostic Capabilities

AI’s utility extends to real-time health monitoring through data from wearable devices, mobile health applications, and remote sensors. These systems continuously track heart rate, glucose levels, oxygen saturation, movement patterns, and chronic disease markers [47]. Continuous, machine-generated physiological data allow AI to detect anomalies early and trigger rapid medical responses [48]. Such capabilities are critical for remote or underserved regions, supporting proactive interventions and reducing hospitalizations.

3.2.5. Demonstrated Performance Advantages Over Human Diagnosis

Several studies show that AI surpasses human diagnostic accuracy in specific domains, including early tumor detection, retinal disease identification, dermatological lesion classification, and radiographic interpretation for breast cancer and lung disease [49]-[51]. These systems identify patterns that may be imperceptible due to clinician fatigue, high workloads, or subtle visual variations. Enhanced precision contributes directly to improved patient outcomes and reduced healthcare costs [52]. AI also supports differential diagnosis, automates labor-intensive tasks, and improves inter-observer consistency, thereby strengthening diagnostic reliability [44].

3.2.6. Cross-Specialty Applications Across Medicine

AI diagnostics now span a wide range of specialties, including radiology, cardiology, neurology, oncology, genetics, dermatology, pathology, and emergency medicine [34][53][54]. In each domain, AI contributes through image recognition, predictive analytics, automated classification, and treatment optimization. Its cross-disciplinary impact underscores AI’s role as an integrative technology reshaping clinical diagnostics at a systemic level.

3.2.7. Challenges: Data Quality, Bias, and Generalizability

Despite its promise, the literature consistently identifies critical barriers to AI adoption in clinical diagnostics:

Dataset quality and heterogeneity: AI performs best when processing objective, machine-generated information and may struggle with inconsistent or subjective inputs such as self-reported symptoms [55]. Challenges include missing values, inconsistent imaging parameters, heterogeneous EHR systems, unbalanced datasets, and poor annotation quality [8][9].Bias and lack of demographic diversity: Models trained on narrow or homogeneous datasets risk underperforming in diverse populations, exacerbating diagnostic inequities [7][14]. This problem is especially pronounced when datasets originate from high-income, majority populations.Limited access and infrastructure barriers: Data silos, privacy restrictions, limited infrastructure, and inadequate clinical computing capacity hinder model generalizability and scalability [28][56][57].Lack of standardization: Variability in imaging protocols and workflow processes may reduce diagnostic consistency by 15 - 20 percent across institutions [22][23].Annotation and labor constraints: The creation of high-quality labeled datasets remains expensive, labor-intensive, and logistically difficult, especially in low-resource settings [13][58].

3.2.8. AI in Low- and Middle-Income Countries (LMICs)

Studies focusing on LMICs highlight the transformative potential of AI in compensating for specialist shortages and improving diagnostic access [59][52]. AI-enabled tools can assist clinicians in remote or underserved regions by providing automated interpretations and decision support. However, LMICs face greater obstacles, including limited high-quality training data, infrastructure constraints, regulatory uncertainty, and a lack of interoperable systems [13][60]. Addressing these barriers is essential to ensure that the benefits of AI diagnostics are equitably distributed.

3.2.9. Summary

Overall, the literature highlights significant advances in AI-enabled diagnostics across medical disciplines. AI systems demonstrate high accuracy, efficiency, and the ability to process multimodal data. However, persistent challenges, including dataset quality, fairness, interpretability, and infrastructure limitations, must be addressed to ensure responsible clinical adoption.

3.3.1. Improvements in Imaging Diagnostics

The majority of imaging-focused studies reported that AI systems achieved high sensitivity and specificity in detecting abnormalities, with performance equal to or surpassing that of expert radiologists in tasks such as mammography, chest imaging, and dermatological lesion classification [6][35][49]. Enhanced detection of early-stage tumors, subtle lesions, and rare abnormalities was consistently documented.

3.3.2. Predictive Analytics Using Clinical Data

Studies leveraging clinical histories, laboratory results, genomic sequences, and biochemical indicators reported strong performance in predicting disease risk, progression, and clinical outcomes [2][14][38]. These models successfully identified subtle correlations within heterogeneous datasets, often outperforming traditional statistical methods.

3.3.3. Personalized and Precision Diagnostics

Several studies highlighted AI’s capacity to generate individualized diagnostic insights by integrating multimodal data, leading to more precise stratification and tailored treatment recommendations [4][41][42]. These systems contributed to the early identification of high-risk patients and selection of optimized therapies.

3.4.1. Enhanced Clinical Workflows

AI-supported systems improved diagnostic workflows by automating routine image analyses, prioritizing urgent cases, extracting actionable insights from EHRs, and shortening diagnostic turnaround times [45][46]. Several studies reported measurable reductions in clinician workload and improved throughput in radiology and pathology departments.

3.4.2. Decision Support Performance

AI tools used as clinical decision support systems demonstrated improved diagnostic consistency, enhanced synthesis of data across modalities, and increased clinician confidence in complex cases [3][6][44]. Many systems provided accurate evidence-based recommendations and risk profiles that complemented human judgment.

Studies employing wearable sensors and digital health platforms found that AI algorithms detected anomalies in real time, monitored chronic disease indicators, and produced reliable alerts for early intervention [47][48]. Continuous, machine-generated data streams supported early warning capabilities and remote diagnosis, particularly where in-person access to specialists was limited.

Findings from LMIC-focused studies indicate strong potential for AI to compensate for specialist shortages, assist clinicians in rural and resource-limited contexts, and improve diagnostic speed and reliability through automated systems [13][52][59]. However, these studies also documented substantial challenges related to data scarcity, infrastructure gaps, and regulatory uncertainty.

3.7.1. Data Quality and Structure

Most studies emphasized that AI diagnostic performance depends heavily on structured, machine-generated input, high-quality annotated datasets, and consistent imaging parameters [8][55]. AI exhibited lower accuracy when processing subjective or unstructured data compared to objective physiological or imaging data.

3.7.2. Dataset Bias and Representativeness

Many studies highlighted underrepresentation of minority groups, limited geographic diversity, and skewed datasets from single institutions [7][25]. These factors restricted the generalizability of diagnostic models and raised concerns about equitable performance.

3.7.3. Infrastructure and Data Access Gaps

Commonly reported challenges included restricted data sharing, heterogeneous EHR systems, lack of interoperable infrastructure, and insufficient computational resources in many regions [28][56][57].

3.7.4. Lack of Standardized Evaluation Frameworks

Substantial performance variability, sometimes up to a 20 percent reduction across sites, was reported due to inconsistent imaging protocols, annotation practices, and validation methods [22][23]. Few studies used standardized reporting guidelines.

3.7.5. Annotation Constraints

Several studies described limited availability of expert annotators, inconsistencies in ground truth labels, and high costs of generating large labeled datasets as major bottlenecks [13][58].

Across studies, several recurring trends emerged:

1) AI demonstrates consistently high diagnostic performance in imaging and predictive tasks.

2) Real-time monitoring applications are expanding rapidly, especially for chronic conditions.

3) AI improves workflow efficiency and supports clinical decision-making.

4) Generalizability remains a major challenge due to biased and limited datasets.

5) AI systems perform best when trained on large, objective, machine-generated data.

6) LMICs benefit significantly from AI tools but face heightened structural and regulatory limitations.

Overall, the reviewed studies demonstrate strong potential for AI to enhance diagnostic accuracy, accelerate clinical workflows, and provide personalized and real-time insights. At the same time, limitations related to dataset quality, bias, infrastructure, and methodological inconsistencies present ongoing barriers that must be addressed.

AI systems show strong performance across imaging, predictive analytics, and decision support. Their ability to integrate diverse data modalities allows for the detection of subtle patterns that may elude human clinicians, enabling earlier diagnoses, more precise risk stratification, and improved clinical outcomes [1][2]. AI tools reduce diagnostic variability, enhance consistency among practitioners, and support more personalized medicine through tailored risk profiles and treatment recommendations.

Workflow optimization is particularly notable. By automating repetitive analyses, triaging high-risk cases, and summarizing complex information, AI reduces cognitive load and frees clinicians to focus on nuanced decision-making and patient communication [45][46]. Real-time monitoring adds another layer of benefit, especially for chronic disease and remote care, where continuous data streams support early detection of deterioration and timely intervention [47][48].

A central challenge emerging from the review is the limited generalizability of AI diagnostic models. Many systems perform impressively in controlled, single-center research settings but lose accuracy when deployed across different populations, institutions, imaging devices, or geographic regions [23]. Reported performance degradation of 15 - 20 percent across sites underscores how strongly AI tools depend on the structure, quality, and origin of their training data [22].

Models trained on homogeneous, institution-specific datasets are susceptible to overfitting and biased diagnostics, particularly when applied to underrepresented demographic groups. Without diverse, standardized, and well-annotated datasets, AI risks producing inequitable outcomes and reinforcing existing disparities [7][25].

Bias is one of the most pressing concerns in AI diagnostics. Skewed or non-randomized datasets can create trends and assumptions that disadvantage underrepresented groups. When models learn primarily from dominant patterns, they may misclassify rare conditions or minority population variants, leading to unequal diagnostic accuracy, higher false negatives among vulnerable populations, and reinforcement of systemic inequities [8][9].

Mitigating algorithmic bias requires both technical and structural interventions. Proposed strategies include balanced sampling, subgroup performance reporting, fairness-aware optimization objectives, and robust external validation on diverse cohorts [26][30]. However, these techniques remain unevenly applied, and fairness considerations are often secondary to aggregate performance metrics

Another major theme is the interpretability of AI models. Many high-performing models, particularly deep learning systems, function as black boxes, producing outputs without clear explanations of how conclusions were reached [47][61]. This lack of transparency undermines clinician trust, complicates accountability, and poses challenges for regulatory approval.

Explainable AI has emerged as a critical area of research, with methods such as saliency maps, attention mechanisms, counterfactual explanations, and interpretable surrogate models proposed as solutions [62]-[64]. However, the literature indicates that current XAI techniques are not yet mature enough to provide consistent, clinically validated explanations. Without robust interpretability frameworks, clinicians may hesitate to rely on AI-generated diagnoses in high-stakes contexts.

Current XAI approaches also face the fidelity-interpretability trade-off, where models that provide more comprehensible explanations often sacrifice accuracy, and no clinically validated metrics exist to assess whether an explanation is truly faithful to the diagnostic reasoning process.

The ethical implications of AI integration are multifaceted. Recurrent issues include patient privacy and data governance, unclear accountability for AI-driven clinical errors, lack of consent transparency, insufficient regulatory oversight, and risks of misuse or unintended harm [12][24][29]. The review highlights the absence of a globally unified ethical or regulatory framework for AI in diagnostics [65]. Instead, fragmented national and institutional policies lead to inconsistent standards and variable safeguards.

Accountability is particularly problematic. Responsibility is shared among developers, who design and train models, healthcare institutions, which procure and deploy them, and clinicians, who interpret their outputs. Current liability frameworks do not clearly define responsibility when AI-related diagnostic errors occur, creating uncertainty and potential risk for practitioners [8][27].

While AI shows promising results in LMICs and can help address specialist shortages, these settings face heightened challenges. Limited access to high-quality training data, inadequate computational infrastructure, fragmented EHR systems, insufficient funding for annotation and validation, and restrictive regulations on data sharing are frequently cited [13][59][60]. These barriers reduce the reliability and scalability of AI tools intended for global use and risk entrenching a digital divide in diagnostic capabilities.

Several methodological limitations characterize the current evidence base:

Inconsistent validation strategies and limited external validationReliance on small or institution-specific datasetsLimited reporting of demographic distributions and subgroup performanceAbsence of standardized outcome measures and reporting frameworksRare inclusion of long-term, real-world evaluations of model impact on patient or system outcomes

These weaknesses limit the interpretability of performance claims and complicate cross-study comparisons.

Despite the breadth of evidence reviewed, the current landscape of AI diagnostics remains constrained by recurring methodological and structural shortcomings. Many studies rely on small, institution-specific datasets that limit external validity, while others use inconsistent validation strategies that make cross-study comparisons difficult. Additionally, gaps in demographic reporting, limited long-term evaluations, and the absence of standardized outcome measures weaken the strength of existing conclusions. These limitations underscore the need for more rigorous, transparent, and harmonized research practices to support reliable clinical adoption.

For research, the findings underscore the need for robust, diverse, multi-institutional datasets; stronger emphasis on explainable and transparent AI; and longitudinal, real-world clinical studies that assess not only accuracy but also safety, equity, and system-level impact [66][67].

For clinical practice, AI should be implemented as an augmentation to, not a replacement for, clinician judgment. Successful integration requires clinician training, workflow adaptation, and continuous monitoring of model performance, including post-deployment drift and bias.

For policy-making, comprehensive regulatory frameworks are needed to address validation, accountability, fairness, and data governance. Investment in interoperable infrastructure and standard-setting for evaluation and reporting will be crucial.

Although the discussion above outlines the major themes identified in the literature, it is also important to acknowledge the inherent limitations of the reviewed evidence. Many studies relied on small, institution-specific datasets and lacked standardized validation methods, which restricts the generalizability of their findings. A large portion of the literature reported only aggregate performance metrics without subgroup analysis, making it difficult to assess equity across demographic groups. Furthermore, few studies examined the long-term clinical impact of AI deployment in real settings, and most evaluations occurred in controlled environments rather than routine clinical workflows. These shortcomings collectively indicate that the current evidence base remains uneven and fragmented, and that stronger methodological standards will be necessary to support robust clinical adoption. These concerns are consistent with broader findings that emphasize the persistent gap between AI success in controlled clinical trials and its limited translation into real-world healthcare settings, underscoring the need for stronger implementation pathways [68].

AI-driven diagnostic systems also face security-specific risks that arise from adversarial interference. Prior work has demonstrated how systematic poisoning attacks in healthcare datasets can degrade or redirect model outputs, highlighting the need for robust data provenance and monitoring [20]. In parallel, advances in hardware-secure EdDSA signatures (Ed25519 and Ed448), post-quantum schemes such as SIKE implemented on FPGA, and fault-tolerant hash functions like ECHO and Fugue provide concrete pathways for strengthening the integrity, authenticity, and tamper-resilience of AI diagnostic pipelines. These developments underscore the importance of integrating cryptographic and hardware-level safeguards into the clinical deployment lifecycle.

Emerging technologies such as multimodal foundation models and generative AI may significantly reshape diagnostic pathways by integrating imaging, clinical notes, genomics, and sensor data into unified reasoning systems. Early demonstrations, including foundation-model-based chest X-ray triage tools and large-vision-language architectures adapted for LMIC radiology units, suggest substantial potential for scalable deployment even in resource-constrained environments. However, these systems also introduce new risks related to hallucination, data provenance, and opaque reasoning chains, requiring more rigorous validation pipelines before clinical integration.

Recent analyses of generalist multimodal models further demonstrate significant limitations in diagnostic reliability and error interpretability, reinforcing the need for cautious, rigorously monitored deployment of such systems in clinical environments [69].