Recent advances at the intersection of artificial intelligence (AI), nanotechnology, and medicine are creating new opportunities to accelerate biomedical discovery and improve the development of nanotechnology-based diagnostic and therapeutic systems. Nanomedicine has demonstrated considerable promise in areas including targeted drug delivery, medical imaging, disease detection, biosensing, and personalized treatment. Yet translation from experimental research to routine clinical practice remains slow. Nanomaterial design continues to rely heavily on iterative experimentation; manufacturing and characterization processes can be time-intensive, and many systems lack the real-time monitoring and adaptive control capabilities necessary for reliable clinical use. AI and machine-learning approaches are increasingly being incorporated across the nanomedicine development lifecycle to address these challenges. At the same time, the adoption of AI-enabled nanomedicine introduces human factors challenges involving interpretability, trust, data quality, workflow integration, reproducibility, interdisciplinary communication, and appropriate allocation of responsibility between automated systems and human experts. These issues become particularly important as technologies move from laboratory proof-of-concept toward clinical environments where system performance must be understandable, predictable, safe, and usable by clinicians and researchers. This review synthesizes 15 studies published between 2023 and 2026 to examine how AI is being used within contemporary nanomedicine and to identify opportunities and barriers relevant to eventual clinical translation. The literature was organized around three major areas: (1) optimization of nanomaterial design, (2) real-time monitoring and control of nanocarriers and manufacturing processes, and (3) diagnostic and imaging data analysis. Particular attention was given to the maturity of AI applications, the type and quality of data supporting them, evidence of experimental or clinical validation, interpretability, and challenges that may affect safe integration into healthcare systems. Across the first theme, AI-supported nanomaterial design, machine-learning methods were consistently used to reduce the traditional trial-and-error process associated with formulation development. However, the quality and quantity of available data varied substantially. For example, Noorain et al. used Gaussian Process regression to predict drug loading and encapsulation efficiency in PLGA nanoparticles. Although 275 candidate publications were initially identified, only eight contained usable data, and much of the final dataset came from a single paper. The authors specifically identified limited data availability as a major constraint on model prediction. Similar concerns emerged in studies predicting protein corona formation, where imbalanced datasets could bias models toward common proteins while underrepresenting rare but potentially biologically important proteins. Other investigations demonstrated greater
integration between computational prediction and experimental validation. Zhang et al., for example, used AI to screen self-assembling drug combinations and subsequently validated predicted assembly mechanisms using computational simulation and wet-lab experiments. Li et al. applied Bayesian optimization to vaccine formulation using industrial formulation and assay data, illustrating how AI can support formulation decisions when predictions are grounded in experimentally derived information. The second and most represented theme involved real-time monitoring and control of nanocarriers and manufacturing processes. The maturity of systems varied widely, ranging from autonomous laboratory platforms to entirely simulated control frameworks. Mottafegh et al. developed a Bayesian-optimization-guided continuous-flow platform capable of autonomously modifying synthesis parameters while producing drug-loaded liposomes and polymeric nanoparticles, completing multistep synthesis with limited manual intervention. Glaubitz et al. addressed another practical bottleneck by using machine-learning models trained on dynamic light scattering and UV-Vis measurements to estimate nanoparticle size and shape that would otherwise require slower electron microscopy procedures. Their approach was evaluated during an active gold nanoparticle synthesis process. DetectNano similarly demonstrated the potential for automated characterization by using a YOLOv8-based model to size polymer nanostructures from microscopy images in under two seconds per image. AI was also used to predict nanocarrier behavior after administration. Chou et al. combined an AI-based quantitative structure-activity relationship model with physiologically based pharmacokinetic modeling to predict nanoparticle tumor delivery and evaluated the approach against 288 experimental datasets. Other models attempted to predict broader pharmacokinetic behavior or brain biodistribution. Importantly, some recent approaches incorporated interpretability mechanisms, addressing a limitation commonly associated with black-box prediction systems. In contrast, the least clinically mature systems relied primarily on synthetic simulation. A reinforcement-learning framework proposed for real-time adaptation of nanoparticle dosing, for example, was evaluated only with simulated pharmacokinetic and pharmacodynamic data rather than with patients, animals, or physical nanoparticles. This variation illustrates the importance of distinguishing technically sophisticated demonstrations from systems with evidence sufficient to support clinical decision-making. The third theme involved diagnostic and imaging data analysis. AI-enhanced nanoparticle sensing systems demonstrated encouraging diagnostic performance, although levels of validation again varied. Chen et al. combined a nanoparticle-based SERS substrate with a MobileNet V2 deep-learning model to classify lung cancer subtypes from exosome Raman spectra, reporting 95.23% accuracy and an area under the curve above 0.95 for each subtype. Other approaches used neural networks to predict MRI relaxation characteristics from nanoparticle properties or machine learning with computational simulation to optimize graphene-based biosensors for breast cancer detection. However, some reported diagnostic performance was derived exclusively from simulated systems rather than physically constructed and clinically tested devices.
From a human factors perspective, the findings suggest that the principal challenge facing AI- enabled nanomedicine is not simply whether algorithms can generate accurate predictions, but whether the larger sociotechnical system can support trustworthy, interpretable, and reproducible use. The reviewed literature repeatedly identified limitations involving inconsistent datasets, lack of standardization, variable transparency of methods, limited clinical validation, and black-box model behavior. These characteristics affect how researchers and clinicians assess model reliability and determine when algorithmic recommendations should be accepted, questioned, or overridden. The findings also emphasize the importance of appropriate human–automation collaboration. AI appears most useful when functioning as an augmentation tool that reduces the number of possibilities humans must evaluate rather than independently replacing experimental judgment. In nanomaterial design, machine learning can predict toxicity, formulation characteristics, or promising design candidates, allowing researchers to concentrate experimental resources on a smaller and more promising set of alternatives. In manufacturing, automated systems can potentially connect synthesis, monitoring, and quality control. In diagnostics, AI can rapidly analyze complex biosensor and imaging signals that may be difficult or time-consuming for humans to interpret manually. Across these domains, however, humans remain responsible for determining whether predictions are biologically plausible, clinically meaningful, and sufficiently validated. The reviewed literature therefore supports a model of human-AI teaming, rather than complete automation, for the foreseeable future. This perspective also raises important design requirements for future systems. Clinical-facing AI tools should communicate uncertainty, provide interpretable evidence where feasible, and make the origin and limitations of their training data visible to users. Interfaces and workflows should support verification rather than encourage uncritical reliance on automated recommendations.
In conclusion, AI is increasingly shaping nanomedicine by accelerating material design, enabling automated monitoring and manufacturing, improving nanocarrier modeling, and extracting diagnostic information from complex nanoparticle-enabled sensing and imaging systems. Across the reviewed literature, however, the dominant pattern is augmentation rather than replacement: AI reduces experimental search spaces and increases analytical efficiency, while real-world nanomedicine still requires experimental validation and human expertise. The major barriers to broader clinical adoption are therefore not exclusively technical. Inconsistent data standards, limited transparency, inadequate real-world validation, and variable interpretability create substantial challenges for trust and implementation. Progress will require collaboration among nanoscientists, clinicians, AI researchers, HFE professionals, and regulatory stakeholders to develop shared data standards, transparent evaluation practices, usable decision- support systems, and validation frameworks appropriate for clinical environments. Incorporating human factors principles early in technology development may help ensure that AI-enabled
nanomedicine systems are designed not merely for computational performance, but for safe, understandable, and effective integration into the complex sociotechnical environments of healthcare.
Background
Brain tumors present significant diagnostic challenges because early and accurate identification is essential for treatment planning and patient outcomes, while conventional diagnostic processes rely heavily on expert interpretation of complex medical images. Magnetic resonance imaging (MRI) remains central to brain tumor diagnosis, but manual evaluation can be time- consuming, cognitively demanding, and subject to inter-observer variability. Recent advances in artificial intelligence (AI), particularly machine learning, deep learning, computer vision, and explainable AI (XAI), have created opportunities to automate tumor detection, segmentation, classification, and characterization. However, translation of high-performing AI models from controlled datasets into clinical practice requires consideration of more than algorithmic accuracy. From a human factors perspective, effective AI must support clinicians’ cognitive work, provide understandable information, reduce rather than redistribute workload, integrate appropriately into clinical workflows, and enable clinicians to maintain meaningful oversight of diagnostic decisions.
Objective
This study synthesizes recent literature on AI applications for brain tumor diagnosis to characterize both the technological capabilities of current approaches and the human factors issues affecting their potential clinical adoption. Particular attention was given to how AI may influence diagnostic workload, decision-making, clinical confidence, interpretability, and human- AI collaboration, as well as the barriers that remain between high algorithmic performance and effective clinical implementation.
Methods
A structured synthesis was conducted using 15 publications examining AI applications related to brain tumor diagnosis and management, primarily published between 2022 and 2026. The reviewed literature included empirical model-development studies and broader reviews addressing AI-enabled brain tumor detection, classification, segmentation, imaging analysis, radiomics and radiogenomics, biomarker identification, multimodal diagnosis, and emerging non-invasive diagnostic technologies. For each publication, information was systematically extracted regarding the healthcare problem addressed, clinical application, AI technique, data source, reported findings and benefits, limitations, future research recommendations, and implications for healthcare delivery. The synthesis emphasized recurring themes relevant to human factors and ergonomics, including diagnostic accuracy, speed of decision-making, clinician workload, interpretability and explainability, trust, clinical decision support, human error, workflow integration, data quality, generalizability, computational requirements, privacy, and human acceptance. Because studies used heterogeneous datasets, tasks, architectures, and performance measures, reported accuracy values were interpreted descriptively rather than directly compared or meta-analyzed.
Results
The literature demonstrates substantial technical progress in AI-supported brain tumor diagnosis. Convolutional neural networks (CNNs), transfer-learning architectures, EfficientNet variants, Vision Transformers, ensemble models, YOLO-based detection, segmentation networks, and hybrid architectures were commonly applied to MRI data. Several studies classified MRI images into glioma, meningioma, pituitary tumor, and no-tumor categories, while others focused on binary tumor detection, automated segmentation, localization, or multimodal diagnosis. Reported performance was generally high, with several models achieving classification accuracy above 95% under experimental conditions. Hybrid approaches combining complementary architectures frequently outperformed individual models by capturing both local tissue characteristics and broader spatial relationships. AI applications also extended beyond conventional image classification. Radiomics and radiogenomics approaches demonstrated the potential to infer tumor characteristics and clinically meaningful molecular information non-invasively. AI-driven biomarker discovery integrated imaging, genomic, transcriptomic, proteomic, and clinical information to support earlier diagnosis, prognosis, treatment-response prediction, and personalized oncology. Another emerging approach combined machine learning with a highly sensitive terahertz metasurface biosensor to explore liquid-biopsy-based tumor detection, illustrating the potential for rapid and less invasive diagnostic pathways. Three-dimensional reconstruction and segmentation-based approaches further demonstrated how AI could support spatial localization, treatment planning, and preoperative assessment. Across these technological approaches, a central human factors benefit was the potential reduction of clinician workload. Automated segmentation, measurement, detection, and classification can reduce repetitive image-review tasks and provide radiologists and neuro- oncology teams with a rapid second opinion. The reviewed literature frequently described AI as a clinical decision-support mechanism rather than a replacement for clinicians. By identifying subtle abnormalities, prioritizing suspicious regions, distinguishing tumor categories, and generating consistent quantitative assessments, AI may reduce diagnostic delays, false-positive and false-negative findings, and variability associated with manual interpretation. However, improved algorithmic performance did not necessarily correspond to clinical readiness. Explainability emerged as one of the most prominent challenges. Many high- performing deep learning systems operate as “black boxes,” creating difficulty for clinicians who must evaluate why a particular diagnosis was generated. Several studies addressed this problem using Grad-CAM, LIME, segmentation maps, or other visualization mechanisms that highlight image regions contributing to a prediction. These approaches can transform AI output from a simple classification into visually inspectable evidence, allowing clinicians to compare model reasoning with their own interpretation. Thus, explainability appears to function not only as a technical feature but also as a human factors mechanism supporting situation awareness, appropriate trust, verification, and shared decision-making between clinicians and AI. Generalizability was another consistent limitation. Many systems were developed using public or single-source MRI datasets, including commonly used benchmark or Kaggle datasets. Differences in scanner characteristics, imaging protocols, patient populations, tumor prevalence, image quality, and institutional practices may substantially influence performance when algorithms are transferred between healthcare settings. Consequently, several studies recommended validation using larger, diverse, multi-center clinical datasets. Data imbalance and limited representation of rare tumor types further raise concerns about whether high average accuracy masks clinically important failure modes for specific patients or diagnoses. Computational and workflow constraints also emerged. Hybrid CNN-transformer architectures, ensemble approaches, 3D reconstruction, and real-time explainability can impose significant memory and processing demands, potentially limiting implementation in resource-constrained settings. In contrast, lightweight models demonstrated that somewhat lower benchmark performance may be accompanied by extremely rapid execution and easier deployment. This creates an important human factors and systems-engineering tradeoff: the most accurate algorithm in an experimental environment may not necessarily be the most useful system within a clinical workflow. Finally, privacy, security, algorithmic bias, regulation, and human acceptance were repeatedly identified as implementation concerns. These challenges become more prominent when systems integrate imaging with clinical records, multi-omic information, cloud infrastructure, or distributed data sources. Successful implementation therefore requires both technical validation and attention to how information is presented, how clinicians interact with AI recommendations, when human review is required, how uncertainty is communicated, and how accountability is distributed between clinicians and automated systems.
Discussion and Conclusion
Collectively, the literature suggests that AI has substantial potential to improve brain tumor diagnosis by increasing diagnostic accuracy, accelerating image analysis, supporting earlier detection, reducing repetitive workload, enabling non-invasive characterization, and assisting personalized treatment planning. Nevertheless, the major barrier to clinical translation is increasingly not simply whether AI can classify an MRI image accurately. Rather, the challenge is designing a sociotechnical system in which clinicians can understand, evaluate, appropriately trust, and effectively use AI-generated information. Future research should therefore move beyond performance-centered model development toward human-centered clinical evaluation. Multi-institutional validation should be accompanied by usability testing, workflow analysis, assessment of clinician cognitive workload, examination of appropriate reliance and trust, and evaluation of how explainability interfaces influence diagnostic decisions. Studies should also investigate AI performance under realistic conditions, including ambiguous cases, rare tumors, imperfect imaging, competing clinical demands, and disagreement between clinicians and algorithms. Human factors methods can play a critical role in determining where AI should enter the diagnostic workflow, what information should be displayed, how uncertainty should be communicated, and how human oversight should be maintained. The next generation of AI-enabled brain tumor diagnosis should therefore be evaluated not only by “How accurate is the model?” but also by “How effectively does the human-AI system support safe, efficient, interpretable, and clinically meaningful decision-making?” This shift from algorithm-centered evaluation toward human-centered system design represents an important opportunity for human factors and ergonomics to guide the responsible translation of AI into neuro-oncology.
