The predictive capability crisis in drug discovery and development remains a significant factor hindering medical progress. Among emerging solutions, organoids offer the ability to reflect the biological fidelity of tissues, whereas organs-on-chips (OoCs) demonstrate strengths in simulating dynamic tissue microenvironments and enabling multi-organ interactions. Herein, we present a unifying artificial intelligence (AI)-orchestrated framework that incorporates both platforms into verifiable and regulatable “model-as-evidence” (MAE) pathways capable of self-optimization with researcher oversight. AI is employed as an indispensable computational integration layer addressing three bottlenecks that render conventional approaches infeasible: (i) combinatorial explosion in high-dimensional parameter spaces, (ii) multi-scale coupling between molecular interactions and tissue-scale level morphogenesis, and (iii) requirement for real-time adaptive control of dynamic microenvironments exceeding human cognitive bandwidth. At the organoid level, this integration enables multi-objective optimization to improve reproducibility, reduce resource use, and achieve predictive modeling to capture extracellular matrix-cells interactions. For OoCs, active learning algorithms condense protracted design cycles into efficient, goal-directed experimentation, and deep reinforcement learning sustains physiological steady states through adaptive, real-time control. The convergence of these systems within an integrated platform enables standardized, cross-comparable phenotyping pipelines that transform heterogeneous experimental data into a universal quantitative language, and harmonized evaluation benchmarks align experimental outputs across laboratories. Together, this framework generates curated evidence packages that directly address regulatory concerns related to efficacy and safety. By systematizing experimental design, parameter control, and interpretive analytics, this AI-orchestrated approach elevates organoid and OoC technologies from specialized crafts to a robust engineering discipline. It establishes a virtuous cycle wherein AI accelerates iterative validation and optimization, freeing researchers to focus on higher-order hypothesis generation and translational strategy. Thus, through AI-orchestrated quantification of biological fidelity and engineering rigor, these platforms may mature into credible, scalable, and transformative toolboxes for next-generation biomedical translation.

Recent advancements in artificial intelligence(AI) have significantly enhanced the perception and cognition of language and the decision-making process. However, the increase in computational power and energy efficiency has also led to the emergence of computational and energy needs. Traditional Von Neumann architectures, with the separation of memory and processing, are costly due to numerous data transfers between hierarchical memory systems. Therefore, in large-scale AI workloads, energy consumption and data transfer latency are major challenges to scalability and efficiency. Using physical phenomena to compute and combining devices, circuits, and algorithms by co-design, these issues are resolved by physics-driven computing. In-memory operations, nonlinearity, and parallelism are also enabled by resistive switching, spin dynamics, phase transitions, and optical interference at the device level, thereby significantly reducing data transmission and energy consumption. Unless the entire hardware stack, including devices, circuits, architectures, and learning algorithms, is coordinated, one achieves limited system-level benefits. This review presents a unified physics-based AI hardware design by jointly optimizing device, circuit, and algorithm. It also discusses new device platforms, including memristive, spintronic, phase-change, photonic, and neuromorphic complementary metal-oxide-semiconductor (CMOS), and how the physics underlying these principles can be used to implement computational primitives. It is discussed further at the circuit level, e.g., in-memory computing arrays, event cameras, neuromorphic processors, and algorithms such as hardware-conscious training, spike-based neural computation, phase-based training, and stochastic inference are also discussed in these physical resources. Together, these innovations enable the development of scalable, adaptable, and energy-efficient intelligent hardware through integrated co-design of materials, electronics, and machine learning.

Large-scale consumer reviews provide a valuable but weakly structured source of market insight for early-stage automotive design. However, existing generative design workflows often rely on direct prompting or manual interpretation, making it difficult to translate heterogeneous consumer preferences into inspectable visual design conditions. This paper presents AutoGen-Insight, a probabilistic semantic-to-visual parameter mapping framework for consumer-driven electric vehicle concept generation. Building on a retained corpus of 14,153 high-confidence electric vehicle reviews, the proposed framework reorganizes consumer feedback into four representative persona-level semantic preference profiles. These profiles are further transformed into probabilistic design vectors that encode both central tendencies and uncertainty across visually observable exterior concept descriptors, including vehicle proportion, styling, color, lighting, wheel design, and surface-finish parameters. A conflict-aware reasoning module integrates knowledge graph traversal with parameter-level trade-off aggregation to handle competing perceptual requirements, such as sportiness, comfort, compactness, and spatial utility. The resulting probabilistic design vectors are used to construct parameterized prompts for diffusion-based multi-view concept image generation, while the intermediate reasoning chains and parameter vectors remain available for inspection. The framework is evaluated through quantitative metrics, focused ablation analysis, qualitative conflict-resolution analysis, and expert/user assessment. Compared with direct prompting, keyword-based prompting, and large language model-generated prompting baselines, AutoGen-Insight achieves higher semantic alignment, plausibility of visual proportions, and cross-view consistency. The results suggest that probabilistic intermediate representations can improve the controllability and interpretability of early-stage image-based design exploration. This study contributes a structured and extensible approach for transforming large-scale consumer feedback into inspectable visual design knowledge for early-stage electric vehicle concept development.