Norhana Arsad

Biological, molecular, and functional states evolve across multiple timescales, limiting the utility of conventional diagnostics based on isolated measurements. This Perspective introduces on‐chip biomimetic intelligence as an integrated approach combining biomimetic molecular recognition and signal transduction with dynamic sensing, adaptive local computation, and longitudinal interpretation of molecular changes. It focuses on multiplexed molecular‐affinity measurements of selected disease‐associated protein panels, coupled with event‐driven signal processing to reduce redundant data generation. Where appropriate, sensing outputs may inform patient‐specific digital twin models and dynamically update PBPK or PK–PD models. Rather than treating intelligent biosensors, neuromorphic biosensing, edge AI, and digital twins as isolated technologies, it integrates their functions and describes architectural and functional requirements for chip‐level biomimetic intelligence. Implementation requires regenerable molecular–recognition interfaces, reliable molecular‐to‐signal transduction, and computational methods capable of estimating protein states and temporal trajectories. Causal validation and constrained closed‐loop control may support clinically relevant decision‐making when sufficient evidence is available. However, complete architecture has not yet been demonstrated as a single clinically validated system. Its realization requires advances in sensing stability, biocompatibility, antifouling performance, calibration, drift control, and clinical validation. Thus, biosensing shifts from static biomarker detection to longitudinal, adaptive, and clinically interpretable monitoring of changes in biological state.

Self-adaptive optical metasurfaces with IoNT, edge AI, and Living Digital Twins enable predictive healthcare through adaptive sensing, AI recalibration, multimodal biomarker tracking, and personalized non-invasive therapy.

Treatment effectiveness is hindered by the phenotypic plasticity of cancer and the genetic complexity of tumors. However, CRISPR-Cas-based medicines face challenges with specificity, off-target effects, and tumor heterogeneity adaptability. This work investigates the possible combination of quantum biological processes, artificial intelligence, and nanomaterials to improve CRISPR gene editing and modulate or reverse selected malignant phenotypes. Quantum machine learning (QML) can be used to simulate quantum processes like electron tunneling in DNA repair and spin-dependent enzyme activity. To enable exact tumor phenotypic reversal, these models will be combined with optimization approaches powered by AI to direct CRISPR editing in oncogenic signaling networks. Graphene, gold nanoparticles, and lipid-based vectors are some of the nanomaterials that will be used as carriers to effectively and deliver CRISPR systems in a biocompatible manner to the cancer microenvironment. We hypothesize that selected homeostatic gene-expression states may be partially restored in experimental cancer models through the integration of quantum-informed AI, CRISPR gene alteration, and nanomaterial delivery. This integrated strategy could support future cancer therapies that move beyond tumor suppression toward controlled modulation of malignant cell states, although substantial preclinical and clinical validation remains necessary.

Background: Accurate subtype classification of lung histopathology is clinically valuable, but many high-performing deep models are opaque and compute-intensive. We target a transparent, CPU-feasible pipeline that maintains competitive accuracy on a standard benchmark. Method: Red, Green, Blue (RGB) patches are resized to 384 × 384, converted to Hue, Saturation, Value (HSV), and a 2D Fast Fourier Transform (FFT) is applied per channel. From the conjugate-symmetric magnitude, we extract 41 descriptors per channel (statistics, band-powers/ratios, entropy/energy, percentiles, edge statistics, centroid, Gini, radial means), yielding 123 Color Frequency Spatial Descriptor (CFSD) features. Mutual Information ranking with a data-driven cutoff reduces this to 54 features. Eight classifiers (XGBoost, LightGBM, CatBoost, Random Forest (RF), Extra Trees (ET), Gradient Boosting (GB), Bagging, Decision Tree (DT)) are trained on an 80/20 split of the lung subset of LC25000 using 5 non-overlapping folds. Metrics include Accuracy, Precision, Recall, F1-score, Jaccard, MCC, Cohen’s κ, Specificity, and area under the curve (AUC). Results: With 123 features, eXtreme Gradient Boosting (XGBoost) achieves 97.85 ± 0.25% accuracy; using the 54-feature subset yields 97.26 ± 0.25% with <1% F1-score decrease. Mean feature-extraction time drops from ~72 ± 4 ms to ~51 ± 3 ms per image (≈28% faster), with end-to-end per-image latency reductions of ~21 ms across models. Confusion exists between adenocarcinoma and squamous cell carcinoma; normal tissue is most separable. Findings are showing robust results against state-of-the-art methods.

Current therapeutic techniques for cancer often lack specificity. They also cause systemic toxicity and lack genetic control. Thus, cancer ranks among the most complex and crucial global health issues. The novel concept of smart nanocapsules is discussed in this Perspective. These oral medications modify genes using CRISPR technology and integrate biosensing and laser-guided activation to enable more personalized cancer therapies. The creation of these versatile nanocapsules is driven by three objectives. First, they aim to enable controlled gene editing in the gastrointestinal tract. Second, they deliver treatments to specific target areas. Third, they detect tumors in real time. Nanocapsules equipped with biosensing components provide microenvironmental input. An external laser can trigger the release of light-absorbing agents. Moreover, these features reduce off-target effects and allow spatiotemporal precision, thhe enteric-coated architecture ensures oral stability. Surface functionalization enhances selective tumor accumulation. AI-guided control algorithms can manage diagnostic interpretation and activation. The CRISPR-based cancer medicines offer the potential for improved safety, specificity, and translational use in the future. Combining advanced nanotechnology, gene editing, and AI-guided control could create innovative solutions.