Neuromorphic computing is emerging as a promising paradigm for sustainable edge intelligence by enabling event‐driven, low‐latency and energy‐aware computation close to sensors. However, the field remains fragmented across device technologies, mixed‐signal and digital hardware platforms, spiking neural network models, software frameworks, event‐stream processing tools, interoperability standards and benchmarking practices. This review provides a cross‐layer synthesis of contemporary neuromorphic computing platforms with particular emphasis on their relevance to scalable and sustainable edge deployment. The article organizes the neuromorphic ecosystem into interconnected layers spanning materials and devices, hardware architectures, software and interoperability tools, benchmark resources and application domains. Mixed‐signal platforms are analysed in terms of analog efficiency, accelerated neural dynamics, biological plausibility, calibration requirements, variability and reproducibility challenges. Digital neuromorphic processors are examined with respect to programmability, deterministic execution, routing fabrics, memory organization, software integration and deployment readiness. The review further discusses software frameworks, simulators, event‐data libraries, hardware‐mapping tools and intermediate representations that support model development, portability and cross‐platform evaluation. A central finding is that neuromorphic systems cannot be compared meaningfully using isolated metrics such as neuron count, chip power, latency, or throughput alone; fair evaluation requires explicit reporting of workload, event rate, model topology, mapping strategy, software stack, measurement boundary and deployment context. Accordingly, the article proposes a benchmarking and reporting perspective for neuromorphic edge intelligence that links accuracy, latency, energy efficiency, robustness and reproducibility. Thus, this review clarifies current progress, unresolved challenges and future directions for sustainable edge sensing, robotics, healthcare monitoring, smart infrastructure, industrial automation and distributed intelligent systems.
Artificial intelligence (AI) is increasingly reshaping ophthalmology because the specialty depends heavily on structured imaging, quantitative measurements, and repeatable diagnostic workflows. This review provides a clinically grounded and translationally oriented synthesis of AI in ophthalmology, covering methodological foundations, ophthalmic imaging modalities, public datasets, disease‐specific applications, evaluation metrics, deployment barriers, and future directions. Unlike reviews that mainly summarize algorithmic performance by disease category or model type, this article organizes ophthalmic AI through an integrated framework that emphasizes clinical use cases, evidence maturity, translational readiness, and real‐world implementation requirements. The review examines applications across population screening, referral triage, disease grading, progression monitoring, prognosis, treatment guidance, workflow support, and automated reporting. Major disease domains include diabetic retinopathy, glaucoma, age‐related macular degeneration, cataract, infectious keratitis, and keratoconus. Particular attention is given to the distinction between retrospective proof‐of‐concept studies, external validation, multicenter evaluation, prospective trials, and real‐world deployment. The review also interprets evaluation metrics from a clinical perspective, highlighting the importance of threshold selection, sensitivity, specificity, false referral burden, missed disease, calibration, uncertainty, segmentation adequacy, robustness, and generalization. Key translational challenges include dataset bias, domain shift, interpretability, privacy, regulatory oversight, infrastructure constraints, workflow integration, and post‐deployment monitoring. Emerging paradigms such as multimodal AI, foundation models, generative AI, and edge‐based point‐of‐care systems are discussed cautiously, with emphasis on hallucination risk, clinical grounding, accountability, and the gap between benchmark performance and deployment readiness. Overall, the review argues that the next phase of ophthalmic AI should move beyond high accuracy values toward prospective validation, external generalization, clinician‐centered design, calibrated uncertainty, and accountable integration into real‐world eye‐care pathways.
P. Ray· Eye & ENT Research· 0 citations
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