Recent Advances in Artificial Intelligence and Natural Antioxidants for Food and Their Health Benefits in Practice: A Narrative Review

Abstract

Natural antioxidants align with consumer demand for clean-label, sustainable, and health-promoting food solutions. Artificial intelligence (AI) is enabling deeper understanding, more rapid screening, and new application modalities in food systems. Novel deep learning frameworks have been developed to predict interactions between polyphenols and proteins—crucial for understanding how antioxidants affect nutrient bioavailability, therapeutic functions, and food processing behavior. The convergence of AI and natural antioxidants is forging a transformative frontier in food science. This review aims to focus on AI-enabled methods and advances in natural antioxidants, focusing on practical impact and future directions. PubMed, Web of Science, Scopus, and the Cochrane Library databases were searched for relevant articles published up to September 2025. AI accelerates the analysis, design, and personalization of food systems, while natural antioxidants deliver health-promoting, sustainable, and clean-label functionality. Together, they offer promising avenues for safer, fresher, and more nutritious food systems. Continued innovation, multidisciplinary synergy, and thoughtful regulation are essential to unlocking their full potential. Encapsulating essential oils, polyphenols, and curcumin within nanocarriers significantly improves their stability, antimicrobial efficacy, controlled release, and bioavailability, extending their shelf life and application in diverse food formats. Advancing the use of natural antioxidants in food systems must navigate additive classifications, health claim validations, labeling transparency, and regulatory compliance across regions.

1. Introduction

Recent advancements in food science are focusing on enhancing the antioxidant properties of both natural and synthetic compounds to improve food preservation and nutritional value [1]. This includes exploring new extraction methods for natural antioxidants, developing novel delivery systems for both natural and synthetic antioxidants, and optimizing their use in various food products [2].

Researchers are developing eco-friendly methods like ultrasound-assisted, microwave-assisted, and enzyme-assisted extraction to obtain natural antioxidants from various sources [3]. Nanotechnology is being used to enhance the effectiveness of natural antioxidants by improving their stability, dispersion, and bioavailability in food systems. This includes the use of techniques such as microencapsulation, nanoemulsions, and nanoliposomes to deliver antioxidants more effectively [4].

Researchers are exploring new and underutilized sources of natural antioxidants, such as spent grape pomace, kombucha tea, and antioxidant peptides from animal by-products [5]. Astaxanthin, a potent antioxidant, is being studied for its application in various food products, with research focusing on improving its stability and bioavailability through different delivery systems [6]. Other examples include rosemary, laurel, and thyme extracts for enriching edible oils and the use of spent grape pomace for its high antioxidant content [7].

Research is also ongoing to optimize the use of synthetic antioxidants like BHA, BHT, and TBHQ in food products, aiming to maximize their effectiveness while minimizing potential health concerns [8]. Studies are investigating the potential of combining natural and synthetic antioxidants to achieve synergistic effects and enhance overall antioxidant activity. Furthermore, new analytical techniques like biosensors, nanosensors, and electrochemical methods are being developed to accurately quantify antioxidants in food matrices and monitor their effectiveness [4].

Antioxidants are crucial for preventing lipid oxidation and extending the shelf life of various food products, including meat, poultry, seafood, and edible oils [9]. Natural antioxidants are being incorporated into functional foods to improve their nutritional value and health benefits [1]. Antioxidants are being incorporated into food packaging materials to prevent oxidation and maintain food quality [9]. This review highlights the recent advances in artificial intelligence (AI) and natural antioxidants for food, in combinations along with their associated potential health benefits. Moreover, this narrative review outlines novel extraction methods and nanotechnology-based delivery systems (e.g., nano-antioxidants and antioxidant polymers) used in the food industry. Applications include active packaging, preservatives, bakery and meat products, stabilized oils, and natural dyes extracted from by-products such as apple, grape, tomato, and citrus pomace.

2. Method

A narrative review was conducted to explore the comparative performance and applications of machine learning models in nutritional biochemistry, with a focus on natural antioxidants and intervention strategies associated with their use. This review also considered the benefits of antioxidants as recommended by health professionals and industry stakeholders. The search strategy involved a comprehensive examination of the following databases:

PubMed and Web of Science searches utilized MeSH terms, specifically targeting “#Natural Antioxidants,” “#Artificial Intelligence,” “#Food,” “#Applications,” “#Nutrients,” and “#Bioactive compounds.” The aim was to uncover the relationship between natural antioxidants and their applications, with a particular focus on finding synergistic or antagonistic effects, as well as improved classification of antioxidants in food contexts. The initial search results yielded 147 articles. After removing 7 duplicates, 140 articles were retained for further analysis.

Searches in PubMed, Web of Science, Scopus, and the Cochrane Library aimed to identify articles related to “#Natural Antioxidants,” “#Artificial Intelligence,” “#Food,” “#Applications,” “#Nutrients,” “#Bioactive compounds,” “#Health benefits,” “#Diagnosis,” and “#Pharmacological efficacy.” Additional keywords such as “#Healthcare” were also included. The search criteria were designed to provide insights into interventions and the role of natural antioxidants in addressing the interplay between specific bioactive compounds and disorders. The initial search results yielded 180 articles. After excluding 6 duplicates, 174 articles remained for detailed scrutiny.

The Scopus and Cochrane Library search emphasized “#Natural Antioxidants,” “#Artificial Intelligence,” “#Food,” “#Applications,” “#Nutrients,” “#Bioactive compounds,” “#Health benefits,” “#Healthcare,” “#Public health,” “#Diagnosis,” “#Nanotechnology,” “#Food packaging,” and “#Pharmacological efficacy.” The aim was to identify articles exploring how AI-driven approaches are increasingly being applied to discover, deliver, and optimize natural antioxidants in functional foods, packaging, and personalized nutrition—highlighting health impacts as validated in practice. Initially, 29 articles were retrieved. After removing 25 duplicates, 4 articles were selected for thorough review.

The selection of these databases was based on their reputability, comprehensiveness, and relevance to healthcare and research (

Table 1. Details of the search process and the unique contributions of each database to the study.

Database	Keywords	MeSH Terms (PubMed)	Initial Articles	Duplicates Removed	Final Articles for Analysis	Contribution to Study	Reason for Inclusion
PubMed	#Natural Antioxidants, #Artificial Intelligence, #Food, #Applications, #Nutrients, #Bioactive compounds, #Health benefits, #Diagnosis, and #Pharmacological efficacy	#Natural Antioxidants, #Artificial Intelligence, #Food, #Applications, and #Health benefits	147	7	140	Provided a broad understanding of the interplay between applications, natural antioxidants evaluation, and health benefits; MeSH terms ensured precision in search	Widely recognized as a premier biomedical database, often used for reviews in healthcare research
Web of Science	#Natural Antioxidants, #Artificial Intelligence, #Food, #Applications, #Nutrients, #Bioactive compounds, #Health benefits, #Diagnosis, and #Pharmacological efficacy	N/A (Web of Science does not use MeSH terms)	180	6	174	Enhanced the overall coverage of literature related to natural antioxidants, applications, and AI	Provides a multidisciplinary approach, covering a wide range of scientific disciplines
Scopus	#Natural Antioxidants, #Artificial Intelligence, #Food, #Applications, #Nutrients, #Bioactive compounds, #Health benefits, #Healthcare, #Public Health, #Diagnosis,” #Nanotechnology, #Food packaging, and #Pharmacological efficacy.	#Antioxidants, #Healthcare, #Applications	22	22	0	Strengthened the evidence base by focusing on bioactive compounds related to applications for evidence-based interventions and nanotechnology in food science; MeSH terms ensured specificity	Renowned for reviews and emphasis on applications in healthcare research and nanotechnology in food science
Cochrane Library	#Natural Antioxidants, #Artificial Intelligence, #Food, #Applications, #Nutrients, #Bioactive compounds, #Health benefits, #Healthcare, #Public health, #Diagnosis, #Nanotechnology, #Food packaging, and #Pharmacological efficacy.	#Antioxidants, #Healthcare, #Applications	7	3	4	Strengthened the evidence base by focusing on applications related to healthcare and nanotechnology in food science; MeSH terms ensured specificity	Renowned for reviews and emphasis on applications in healthcare research and nanotechnology in food science

). Further exclusion of case reports, reviews, and non-research articles was made to ensure methodological rigor. Specifically, the eligibility criteria required (1) studies employing probability sampling and reporting sample size; (2) studies providing evidence of variability and validation; and (3) articles published from 2012 to September 2025. Finally, 70 studies were included [10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78], health benefits in practice: [25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78] (Figure 1).

A minimum sample size of 20 was set as an inclusion criterion, and the minimum required accuracy of the AI model was defined as 30%.

3. Results

3.1. AI, Applications, and Food

Researchers have used AI applications to determine antioxidant capacity [10,11], identify specific antioxidants [12], assess product safety [13], and discover antioxidant properties in various plants [14], seeds [79], and food groups [15]. These food groups were categorized in baked products; fruits and fruit juices; cereal grains and pasta; sweets; legumes and legume products; beverages; nuts and seeds; vegetables and vegetable products; spices and herbs; dairy and eggs; fats; and oils. Moreover, functional foods were studied and identified using AI, such as mushrooms [16]; microalgae [17] and Juglans mandshurica Maxim, an important industrial crop in China [18]; and herbs [19]. Finally, in some studies, researchers also examined combinations of foods to achieve enhanced antioxidant effects [24]. The validation and selection of the models are based on their fit and alignment with experimentally obtained results [20], with performance accuracy ranging from 33–98% (

Table 2. Comparative performance and applications of machine learning models in nutritional biochemistry.

Authors	Key Prediction	Description	Variability and Validation	Reference
Ayres et al., 2023	The type of interaction (synergistic, additive, and antagonistic) of antioxidant combinations.	The node was set to 32 cores (ncpus), and the allocated memory was set to 372 GB. As a graphical processing unit (GPU), a NVIDIA Tesla V100 was used to train the foundational chemistry model as well as to fine-tune the generated model into the regressors. Process: (1) foundation chemical model (training dataset), (2) database-developed regressors, (3) best database regressors, and (4) index value prediction (linked with TBARS database)	61%	[12]
Idowu et al., 2021 and 2009	Biorelevant antioxidant capacity of polyphenols, thus facilitating the identification or design of antioxidant molecules.	Vector machines, artificial neural networks, and Bayesian probabilistic learning are key algorithms that were used.	89%	[10,11]
Thomaz et al., 2022	Classification of common preservatives in food and drug industry by artificial intelligence.	Differentiating natural and synthetic monoaromatic antioxidants since their outputs from multivariate analysis, data mining, and machine learning algorithms generated a reliable and accurate model for prompt classification of natural and synthetic monoaromatic antioxidants.	80%	[13]
Liu et al., 2021	Artificial intelligence-assisted ultrasonic extraction of total flavonoids from Rosa sterilis.	Artificial intelligence tools (ANN-GA), ANN-particle swarm optimization (ANN-PSO) algorithm, random forest (RF), and radial basis function (RBF) were combined with response surface methodology (RSM) to optimize the extraction efficiency of flavonoids in Rosa sterilis. ANN-GA and ANN-PSO are modeled and optimized using MATLAB 2016a.	74%	[14]
Yordi et al., 2015	The total antioxidant capacity of food assessed by the oxygen radical absorbance capacity (ORAC) method.	The k-nearest neighbors (KNN) and supervised unidirectional networks MultiLayer Perceptron (MLP) technics used to predict the antioxidant capacity in the studied food groups. (a) amount of flavonoid (mean), (b) class of flavonoid, (c) Trolox equivalent antioxidant capacity (TEAC) value of each flavonoid, (d) probability and classification of clastogenicity assessed by quantitative structure-activity relationship (QSAR) method, and (e) total polyphenol (TP) value.	33%	[15]
Shen et al., 2020	Screening candidate bioactive compounds from Ganoderma species for certain diseases and identifying its authority.	An ensemble learning algorithm based on decision trees for classification and regression tasks. Secondly, random forests (RF), support vector machine (SVM), and k-nearest neighbors (KNN) models were built while using full spectra (including 1487 NIR variables and 1214 FT-MIR variables, respectively).	97%	[16]
Chong et al., 2023	AI was used for the recovery and quantification of fucoxanthin from microalgae.	AI models such as support vector machine (SVM), convolutional neural networks (CNNs), and ANN have been leveraged for the quantification of fucoxanthin, either computer vision based on color space of images or regression analysis based on statistical data.	66–99%	[17]
Chu et al., 2022	To improve the extraction efficiency of flavonoids from Juglans mandshurica Maxim, suitable ultrasonic-assisted extraction was proposed after optimization using a hybrid response surface methodology–artificial neural network–genetic algorithm approach (RSM–ANN–GA).	Extracts obtained using ultrasonic-assisted extraction and traditional solvent extraction were compared by Fourier-transform infrared spectroscopy analysis, the results of which revealed that the functional group of bioactive compounds in the extract was unaffected by the ultrasonication process.	80%	[18]
Mantiniotou et al., 2025	To evaluate if AI could replicate experimental models and ultimately supplant the laborious experimental process of rosemary, yielding the same results more rapidly and adaptably.	One statistical, derived from experimental data (pressurized liquid extraction), and the other based on AI. To further enhance data interpretation and predictive capabilities, six machine learning models were implemented on the original dataset.	92–77%	[19]
Shanker et al., 2025	The optimization of process parameters of the cold plasma technique, ensuring greater extractability or retention of total phenols and antioxidant potential.	AI techniques such as ANN and a genetic algorithm (GA) optimization in cold plasma can also widen the prospects of application by disregarding the complexity of the selection of parameters, thereby contributing to the efficacy of functional improvement in agricultural production.	98%	[20]
Muraro et al., 2020	This allowed a successful training of the machine learning algorithm and the prediction of the antioxidant capacity of a large set of compounds obtained with satisfactory accuracy and with a minimal computational effort.	A machine learning algorithm based on a classification method to categorize the hydrogen atoms into two groups: reactive (i.e., those with ΔG0 Hydrogen atom transfer < 0) and non-reactive (i.e., those with ΔG0 Hydrogen atom transfer > 0). A database of Gibbs free energies of reactions for a large set of HAT processes was obtained through density functional theory (DFT) calculations in an automatized manner.	90%	[21]
Metekia et al., 2022	Artificial intelligence (AI) based models, namely the Adaptive-Neuro Fuzzy Inference System (ANFIS) and MultiLayer perceptron (MLP) models, and stepwise linear regression (SWLR) were used to predict total phenolic compounds (TPC) of the spirulina algae.	Spirulina productivity, extraction yield, total flavonoids, percent of flavonoid, and percent of phenols are considered as input variables with the corresponding TPC as an output variable.	99%	[23]
Mladenović et al., 2023	In vitro antioxidant and in vivo antigenotoxic features of a series of 61 essential oils and quantitative composition–activity relationships modeled through machine learning (ML) algorithms.	The ML-based models explained either the positive or negative contribution of the most important chemical components: limonene, linalool, carvacrol, eucalyptol, α-pinene, thymol, caryophyllene, p-cymene, eugenol, and chrysanthone.	85%	[22]
Hrebień-Filisińska, 2021	It presents the application of natural antioxidants in the extension of fish oil shelf life and the effectiveness of natural antioxidants in protecting long-chain omega-3 fatty acids.	One statistical model, derived from experimental data, and another one based on AI.	90%	[24]

ANN: artificial neural network, ORAC: oxygen radical absorbance capacity, GA: genetic algorithm, RSM: response surface methodology, ANFIS: Adaptive-Neuro Fuzzy Inference System, MLP: MultiLayer Perceptron, SWLR: stepwise linear regression, TPC: total phenolic compounds, ML: machine learning.

) [10,11,12,13,14,15,16,17,18,19,20,21,22,23,24]. The use of machine learning (ML) allows us to tremendously reduce the computational cost of assessing the scavenging properties of potential antioxidants, with minimal impact on the quality of the results [21].

Multiple machine learning and deep learning architectures were evaluated to identify the most effective predictive model for antioxidant activity. The tested models included: random forest (RF) and XGBoost (for descriptor-based learning), graph neural networks (GNNs) (for structure-based learning using molecular graphs), feedforward neural networks (FNNs), and transformer models (for SMILES-based representations). For the GNN, molecular nodes represented atoms, while edges represented bonds. Node features included atomic number, valence, and hybridization. The model employed multiple graph convolution layers, ReLU activation functions, dropout regularization (rate = 0.2), and a fully connected prediction head. The dataset was randomly divided into training (70%), validation (33–99%), and test (15%) subsets using stratified sampling. A subset of top-ranked compounds predicted to have high antioxidant potential was selected for in vitro validation using DPPH, ABTS, and FRAP assays. Experimental results were compared with AI predictions to assess predictive accuracy and to refine the computational model. All computational analyses were conducted using publicly accessible data, ensuring compliance with open data ethics. No human or animal testing was performed during the computational phase of this study [10,11,12,13,14,15,16,17,18,19,20,21,22,23,24].

3.2. Health Benefits in Practice (Health Applications and Pharmacological Aspects)

Today’s emerging tools can monitor food antioxidants in real time before or after meals, use AI to detect diseases based on metabolic response [80], and analyze synergies, antagonisms and additivities between compounds to recommend supportive foods (Figure 2) [81].

The accuracy, sensitivity, and specificity of “convolutional neural network” (CNN) for deep learning in the early detection of skin cancer are the major advantages of AI-based health applications. The benefits of these algorithms on improving public health for skin cancer [41] include being image-based, cost-effective, sustainable, and capable of rapid detection with high accuracy. Nanotherapeutic applications and health benefits of natural antioxidants are summarized in

Table 3. Nanotherapeutic uses of natural antioxidants and possible health benefits.

Authors	Natural Antioxidants	Nano Therapeutic Uses	Possible Health Benefit	Reference
Chen and Wang, 2015	Ferulic acid	Effect of ferulic acid on cholesterol efflux in macrophage foam cell formation and potential mechanism.	It may show the anti-atherosclerosis effect by increasing the expression of surface ABCA1 and ABCG1 proteins of macrophage foam cells and by promoting cholesterol efflux.	[25]
Chmielowski et al., 2017	Ferulic acid	Athero-inflammatory nanotherapeutics used ferulic acid-based poly(anhydride-ester) nanoparticles attenuate foam cell formation by regulating macrophage lipogenesis and reactive oxygen species generation.	It may offer an integrative strategy for the localized passivation of the early stages of the athero-inflammatory cascade in cardiovascular disease.	[26]
Zhao et al., 2020	Ferulic acid	Antioxidant nanoparticles for concerted inhibition of α-synuclein fibrillization and attenuation of microglial intracellular aggregation and activation. Ferulic acid diacid with an adipic acid linker and tannic acid were used as shell and core molecules to form NPs via flash nanoprecipitation.	Antioxidant-based nanotherapeutic candidate to target pathological protein aggregation and neuroinflammation in neurodegenerative diseases.	[27]
Agarwal et al., 2012	Ferulic acid	Ferulic acid and metformin can cause synergistic interaction when they affect proteins/enzymes on parallel pathways, which ultimately cause increased uptake of glucose.	Anti-diabetic effect.	[28]
Safwat et al., 2025	Caffeic acid	Caffeic acid, quercetin, and 5-fluorocytidine-functionalized Au-Fe3O4 nanoheterodimers for X-ray-Triggered Drug Delivery in breast tumor spheroids.	Au-Fe3O4 and caffeic acid exhibit encouraging potential for application as nanotherapeutics in combined radiotherapy and chemotherapy, as they allow for radiation damage to hypoxic cells due to their bimodal action.	[29]
Klein et al., 2021	Caffeic acid	Bioinspired caffeic acid-laden milk protein-based nanoparticles targeting folate receptors for breast cancer treatment.	This study paves the road to a “back to nature” approach in designing biocompatible-bioinspired conjugated nanocarriers for the diagnosis and treatment of various diseases.	[30]
Tata et al., 2025	Caffeic acid	Caffeic acid-based complexes with biogenic amines, specifically spermine and histidine, are potential iron chelation therapy candidates. Initial in vitro assays on HEK-293 cells under iron dextran-induced toxicity have demonstrated their protective effects, with CA-Sp exhibiting superior efficacy.	A safer and more effective strategy for managing iron overload and its associated complications.	[31]
Babić Radić et al., 2024	Quercetin and caffeic acid	Gelatin-/alginate-based hydrogel scaffolds reinforced with TiO2 nanoparticles for simultaneous release of allantoin, caffeic acid, and quercetin as multi-target wound therapy platform.	Multi-target therapy has significant promise for improved wound healing in a beneficial and non-invasive manner.	[32]
Arulmozhi et al., 2013	Ellagic acid	Ellagic acid encapsulated chitosan nanoparticles were successfully synthesized by ionic gelation method. Physicochemical characterization confirms the synthesis of nanoparticles. In vitro drug release profile showed sustained release of ellagic acid. Nanoformulated ellagic acid exhibited enhanced cytotoxicity in human oral cancer cell line cells.	Ellagic acid encapsulated chitosan nanoparticles for drug delivery system in human oral cancer cell line.	[33]
Mady and Shaker, 2017	Ellagic acid	In vivo testing revealed that the oral administration of nanoformulated ellagic acid produced a 3.6-fold increase in the area under the curve compared to that of EA.	Enhanced anticancer activity and oral bioavailability of ellagic acid through encapsulation in biodegradable polymeric nanoparticles.	[34]
Kaczmarek-Szczepańska et al., 2024	Ellagic acid	Hyaluronic acid/ellagic acid as materials for potential medical application exhibited prominent antibacterial properties, particularly against Staphylococcus aureus.	It could be a promising technique for future applications in regenerative dermatology.	[35]
Hoang et al., 2020	Vitamin C	High-dose vitamin C has been shown to be a safe and effective therapy for severe cases of respiratory viral infection. Other widely available nutraceuticals can improve the redox balance and reduce tissue damage in viral pneumonia and Severe Acute Respiratory Syndrome Coronavirus 2.	Possible application of high-dose vitamin C in the prevention and therapy of coronavirus infection.	[36]
Chanphai and Tajmir-Riahi, 2019	Vitamin C	Conjugation of vitamin C with serum proteins. Serum proteins are capable of transporting vitamin C in vitro.	A potential application for vitamin delivery.	[37]
Rodrigo et al., 2013	Vitamin C and E	Cardioprotection against ischemia/reperfusion by vitamins C and E plus n−3 fatty acids.	Using these antioxidant vitamins plus n−3 PUFAs for cardioprotection in clinical settings, such as postoperative atrial fibrillation, percutaneous coronary intervention following acute myocardial infarction, and other events associated with ischemia/reperfusion.	[38]
Hajikhani et al., 2023	Vitamin E	Nanoarchitectonics of doxycycline-loaded vitamin E–D-α-tocopheryl polyethylene glycol 1000 succinate micelles for ovarian cancer stem cell treatment.	Doxycycline in hemo/biocompatible nanomicelles holds potential for ovarian cancer stem cell therapy.	[39]
Bakhshi et al., 2022	Curcumin	Comparative efficacy of 1% curcumin nanomicelle gel and 2% curcumin gel for treatment of recurrent aphthous stomatis.	The reduction in pain score and lesion size was significantly greater in the curcumin nanomicelle gel group.	[40]
Abedanzadeh et al., 2020	Curcumin	Application of synthesized polymeric micelles for loading of poorly-soluble phytochemical “curcumin.”	Cytotoxic effect of curcumin-loaded polymeric micelles on various cancerous cell lines.	[82]
Spanou et al., 2023	Genistein	Development and characterization of gel-like matrix containing genistein for skin application	A promising application as a supplement for sun protection and skin diseases associated with solar UV radiation.	[41]
Fabiani et al., 2012	Hydroxytyrosol	Pro-apoptotic effects were observed in HL60 cells incubated with hydroxytyrosol, even in conditions not supporting H2O2 accumulation (between 23.8 and 38.0% depending on the media), suggesting that other mechanisms, in addition to the H2O2-releasing activity, could be involved in the pro-apoptotic activity.	Hydroxytyrosol and potential uses in cancer.	[42]
Pereira-Caro et al., 2013	Hydroxytyrosol	Hydroxytyrosyl ethyl ether exhibits stronger intestinal anticarcinogenic potency and effects on transcript profiles compared to hydroxytyrosol.	Hydroxytyrosol and potential uses in intestinal cancer.	[43]
Elamin et al., 2013	Hydroxytyrosol	Hysroxytyrosol exhibits specific cytotoxicity against SK-BR-3 and T-47D breast cancer cells. Furthermore, hydroxytyrosol triggered apoptosis that showed a dose-dependent increase in both cell lines. Moreover, hydroxytyrosol inhibited cell proliferation by delaying the cell cycle at G2/M phase.	Hydroxytyrosol and potential uses in breast cancer.	[44]
Castaner et al., 2012	Hydroxytyrosol	Hydroxytyrosol and two derivatives at 10−5 M almost completely inhibited conjugated diene formation induced by 5 μM copper sulfate as oxidant in LDL incubation samples.	Hydroxytyrosol and potential uses in cardiovascular diseases.	[45]
Alcami et al., 2014	Hydroxytyrosol	Antiviral activity of 5-hydroxytyrosol, a microbicidal candidate against HIV-1 transmission.	Hydroxytyrosol and potential uses in AIDS.	[46]
Saba et al., 2017	Hydroxytyrosol	5-hydroxytyrosol inhibits HIV-1 replication in primary cells of the lower and upper female reproductive tract. 5-hydroxytorosol also decreased the levels of CD4+ and CD8+ T lymphocytes infiltrating the CTE and coexpressing CD38.	Hydroxytyrosol and its potential uses in AIDS.	[47]
Ahmed et al., 2022	Luteoline	Luteolin loaded on zinc oxide nanoparticles ameliorates non-alcoholic fatty liver disease associated with insulin resistance in diabetic rats via regulation of PI3K/AKT/FoxO1 pathway.	Anti-diabetic effect.	[48]
Naso et al., 2016	Luteoline	Luteolin modulate apoptosis, autophagy, angiogenesis, cell cycle progression, metastasis, and epithelial–mesenchymal transition in cancer cells.	In mono-acyl derivatives of luteolin, the benzylation of the hydroxy groups at 7-, 3′-, and 4′- positions can enhance the oral bioavailability of this compound.	[49]
Erdoğan et al., 2022	Luteoline	Quercetin and luteolin improve the anticancer effects of 5-fluorouracil in human colorectal adenocarcinoma.	Quercetin and luteolin synergistically enhanced the anticancer effect of 5-fluorouracil in HT 29 cells and may therefore minimize the toxic effects of 5-fluorouracil in the clinical treatment of colorectal cancer.	[50]
Xu et al., 2014	Luteolin	Luteolin provides neuroprotection in models of traumatic brain injury via the Nrf2–ARE pathway.	Luteolin lowered the number of damaged cells and intercellular ROS after scratch in vitro.	[51]
Zhang et al., 2019	Resveratrol	Nano-gold loaded with resveratrol enhances the anti-hepatoma effect of resveratrol in vitro and in vivo.	Significantly better anticancer effect than resveratrol alone in vitro and in vivo, which may be helpful for the clinical therapy of liver cancer.	[52]
Soo et al., 2016	Resveratrol	Enhancing delivery and cytotoxicity of resveratrol through a dual nanoencapsulation approach.	Co-encapsulation of pristine resveratrol along with its cyclodextrin complex in liposomal formulations is a plausible option for the enhanced delivery of the hydrophobic chemotherapeutic agent.	[53]
Qiao et al., 2018	Vitamin A	Vitamin A-decorated biocompatible micelles for chemo-gene therapy of liver fibrosis.	A promising tool for targeted delivery of chemo-genes to activated hepatic stellate cells in the treatment of liver fibrosis.	[54]

[25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54].

Antioxidants are used to enhance both the efficacy and the shelf life of skincare products. AI tools support their evaluation and increase their ability to improve skin protection against UV-induced damage [83]. Another benefit includes the use of antioxidants such as syn-7-hydroxy-7-anisylnorbornene, geijerene, piperiton, pregeijerene, nerolidol (or peruviol), and thymol methyl ether for cardiovascular infection prevention and mitigation of oxidative damage [81]. Moreover, AI and machine learning methods are increasingly applied to evaluate cardiotoxicity using the adverse outcome pathway (AOP) frameworks [84]. Hydroxytyrosol has been shown to enhance endothelial function and reduce artery inflammation [45]. Nano-ellagic acid exhibits stronger pro-apoptotic effects in cancer cells with lower systemic toxicity [34]. Nano-curcumin has shown increased efficacy in preventing beta-amyloid plaque formation in Alzheimer’s disease models [85]. Nano-luteolin improves insulin sensitivity and reduces oxidative stress in pancreatic beta cells due to the improved bioavailability that enhances blood sugar regulation [86]. In general, natural antioxidants delivered via nanocarriers help suppress chronic inflammation—a root cause of aging, degenerative diseases, and inflammation-related comorbidities [27]. Nano-antioxidants also reduce oxidative DNA damage, thereby slowing cellular aging [87].

Findings of Madani et al. (2025) suggest that dietary antioxidants are inversely associated with body mass index (BMI), waist circumference, fasting blood glucose, triglyceride, total cholesterol, and low-density lipoproteins (LDL) in both overweight/obese and normal weight individuals. It seems that following a diet rich in ORAC may help counteract obesity and its associated comorbidities [88]. However, another study found that the ORAC index did not have a significant effect on the development of the metabolic syndrome or its components and was only associated only with the intake of various food groups [89]. According to a large-scale study involving 10,000 participants, a diet high in polyphenols has been demonstrated to contribute to improved liver health outcomes [90]. Other studies published in 2024 [91] and 2023 [92] have shown that dietary antioxidant intake was linked to a lower risk of myocardial infarction [91] and to reduced disease severity, along with decreased levels of inflammatory and oxidative stress biomarkers in patients with knee osteoarthritis [92]. The literature has shown that the Mediterranean diet, rich in a variety of wholesome foods, is associated with greater resilience. If consumers could use AI to improve the quality of their diet, they may also enhance their resilience and mental health [93]. Taking into account all the aforementioned studies, the therapeutically applications of natural antioxidants as nanocomposites are shown in Figure 3 [25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54].

Nanoencapsulated antioxidants such as curcumin nanoparticles and resveratrol-loaded liposomes can cross the blood–brain barrier. These formulations reduce oxidative stress and inflammation in models of Alzheimer’s, Parkinson’s, and other neurodegenerative diseases [85]. Curcumin also enhances cytotoxicity toward cancer cells while reducing side effects compared to free compounds [82]. Nanocarriers improve the oral delivery of poorly soluble antioxidants (like curcumin), leading to improved glycemic control and reduced oxidative damage to pancreatic β-cells. Quercetin or resveratrol-based nanoparticles improve endothelial function, reduce LDL oxidation, and lower inflammation, thereby helping prevent atherosclerosis and ischemic injury [91]. Topical antioxidant nanoemulsions (containing vitamin E, green tea extract, or aloe vera) accelerate wound healing and protect the skin from UV-induced oxidative stress. Furthermore, nanoformulations enable sustained antioxidant release at inflammation sites, modulating cytokine levels and reducing tissue damage (

Table 4. Practical examples of the specific release and targeting effects of nanocarriers [94].

Nanocarrier	Drug	Targeting Mechanism	Release Mechanism	Example Outcome
Liposome Doxil	Doxorubicin	Enhanced permeability and retention effect (passive)	Slow degradation/pH-sensitive	Reduced cardiotoxicity
Abraxane	Paclitaxel	Gp60/secreted protein acidic and rich in cysteine receptor-mediated	Nanoparticle disassembly	Solvent-free, improved efficacy
pH-Sensitive Micelles	Cisplatin	Enhanced permeability and retention effect	PLGA degradation	Selective tumor cytotoxicity
Folate-Sensitive Polymeric Micelles for Cisplatin Delivery	Methotrexate	Folate receptor-mediated	Heat-triggered release	Spatial control
Thermo-liposomes	Doxorubicin	Passive and thermal localization	pH/redox triggered	High tumor localization
Magnetic Nanoparticles	Doxorubicin	Magnetic field-guided

) [87,94].

3.3. Nanotechnology and Increasing Bioacceptability of Antioxidants

Nanotechnology plays a significant role in enhancing the bioavailability and bioacceptability of antioxidants [95]. Many natural antioxidants (e.g., curcumin, quercetin, resveratrol, etc.) suffer from poor water solubility, low stability, and limited absorption in the gastrointestinal tract [96], which significantly limits their therapeutic effectiveness [97]. Many potent antioxidants are hydrophobic (i.e., poorly water-soluble), which limits their absorption in the body. Nanocarriers such as nanoemulsions, solid lipid nanoparticles (SLNs), polymeric nanoparticles, and lipid-based nanocarriers (e.g., liposomes) can encapsulate these antioxidants, improving their solubility, stability, and absorption [98]. For instance, curcumin-loaded nanoparticles have shown a several-fold increase in bioavailability compared to free curcumin [99]. Nanoparticles can more effectively penetrate biological barriers such as the intestinal lining and the blood–brain barrier, due to their small size (typically <200 nm). This improves antioxidant delivery to target tissues and cellular uptake and retention. Moreover, quercetin-loaded nanoparticles exhibited improved intracellular antioxidant activity due to enhanced cellular uptake. Nanocarriers can also be engineered to release antioxidants in a controlled manner, either over time or in response to specific triggers (e.g., pH, enzymes, etc.) in order to improve therapeutic efficacy and minimize side effects. Nanoencapsulation protects antioxidants from premature degradation, oxidation during storage or digestion, and damage from UV or heat exposure [100]. Furthermore, surface modification of nanoparticles with ligands, peptides, or antibodies allows targeting specific cells or tissues, crossing biological barriers and reducing off-target effects. Overall, nanotechnology offers powerful tools to increase the bioacceptability of antioxidants, making them more effective for both therapeutic and preventive health applications [101]. By addressing major limitations such as poor solubility, low stability, limited targeting, and insufficient absorption, nanotechnology enables antioxidants to reach their full potential in combating oxidative stress-related diseases [102]. This is particularly valuable since many antioxidants are unstable under physiological conditions, including exposure to oxygen, heat, or extreme pH levels [103].

3.4. Food Packaging, Natural Antioxidants, and Public Health Benefits

The integration of natural antioxidants into food packaging is a rapidly growing area in food science, nanotechnology, and public health [104]. This approach not only extends the shelf life of food products but also enhances consumer health by reducing exposure to synthetic preservatives and preventing foodborne diseases [105]. Natural antioxidants—derived from plant sources like green tea, rosemary, grape seed, turmeric, oregano, and essential oils—can inhibit lipid oxidation (preventing rancidity) and reduce microbial growth in foods, replace synthetic food additives (e.g., BHA, BHT, and TBHQ), enhance safety and shelf life of foods (

Table 5. Comparative antioxidant activity and applications of natural antioxidants in food science.

Authors	Natural Antioxidants	Food Science	Reference
Huang et al., 2022	Ferulic acid	Applications of ferulic acid-loaded fibrous films for fruit preservation. To develop a novel ultrathin fibrous membrane with a core–sheath structure as an antioxidant food packaging membrane.	[55]
Vilela et al., 2017	Ellagic acid	Bioactive chitosan/ellagic acid films with UV-light protection for active food packaging.	[56]
Ouazzani, 2021	Hydroxytyrosol and oleuropein	Use of olive leaf extract to inhibit the growth of Campylobacter spp. in an active packaging for fresh chicken preservation.	[57]
Wei et al., 2024	Theobromine and catechins	Cassia seed gum films incorporated with partridge tea extract as an edible antioxidant food packaging film for preservation of chicken jerky.	[58]
Roy and Rhim, 2020	Curcumin	Preparation of antimicrobial and antioxidant gelatin/curcumin composite films for active food packaging application.	[59]
Alehosseini et al., 2019	Curcumin	Electrospun curcumin-loaded protein nanofiber mats as active/bioactive coatings for food packaging applications.	[60]
Mohan and Paneerselvam, 2022	Curcumin	Development of polylactic acid-based functional films reinforced with ginger essential oil and curcumin for food packaging applications.	[61]
Baysal and Doğan, 2020	Curcumin	Biodegradable starch-based nanofilms for potential use of curcumin and garlic in food packaging applications	[62]
Xie et al., 2021	Genistein	Genistein magnetic molecularly imprinted polymers as dispersive solid-phase extraction adsorbents combined with HPLC were used to selectively separate genistein in soy sauce samples, and the recoveries ranged from 85.7 to 88.5%.	[63]
Zhang et al., 2025	Resveratrol	An antibacterial and antioxidant food packaging film based on amphiphilic polypeptides-resveratrol-chitosan.	[64]
Huang et al., 2021	Resveratrol	Fish gelatin–low methoxylated pectin–resveratrol films were prepared, and the mixing enhanced the film mechanical properties. The prepared resveratrol films helped to prevent fat oxidation in beef tallow.	[65]
Yang et al., 2025	Resveratrol	Microcrystalline cellulose/corn starch-based active packaging enhanced by resveratrol/β-cyclodextrin complex. The complex endowed the film with antioxidant and antibacterial activities.	[66]
Guo et al., 2024	Resveratrol	Acrolein/resveratrol-grafted chitosan-sodium alginate bilayer films exhibited excellent antioxidant and antibacterial activity and packaging of fresh-cut apple with this film retarded its browning.	[67]
Yuan et al., 2025	Resveratrol	An antioxidant composite film based on loquat seed starch incorporating resveratrol-loaded core-shell nanoparticles. This film helped to prevent fat oxidation in soybean oil during storage.	[68]
Wang et al., 2025	Resveratrol	Sodium alginate composite films incorporating self-assembled cyclodextrin succinic acid/chitosan nanoparticles encapsulating resveratrol for blueberry preservation.	[69]
Liying et al., 2025	Resveratrol	Chitosan-gelatin sustained-release film incorporated with resveratrol-loaded emulsion can prolong the shelf life of prepared steak.	[70]
Zhang et al., 2019	Anthocyanin	Multifunctional food packaging films based on chitosan, TiO2 nanoparticles, and anthocyanin-rich black plum peel extract have antioxidant, ethylene scavenging, and antimicrobial abilities.	[71]
Khuntia et al., 2022	Vitamin C	Vitamin-loaded stearic acid-free liposomes could effectively replace sterol-based liposome preparation for food packaging applications.	[72]
Aresta et al., 2013	Vitamin E and C	Vitamin(s)-loaded chitosan nanoparticles for potential food packaging applications for a better storage of hydrophilic and/or lipophilic food.	[73]
Stoleru et al., 2016	Vitamin E	Multifunctional surface properties by electrospraying chitosan/vitamin E formulation destined to biomedical and food packaging applications	[74]
Mirzaei-Mohkam et al., 2020	Vitamin E	Nanoencapsulated vitamin E-loaded carboxymethyl cellulose films could be proposed for sheltering food items containing lipids or fats stored at the ambient temperature.	[75]
Vera et al., 2016	Selenium	Nano selenium as an antioxidant agent in a multilayer food packaging material.	[76]
Ndwandwe et al., 2022	Selenium	Selenium nanoparticles–enhanced potato starch film for active food packaging application.	[77]
Lu et al., 2020	Selenium	Polylactic acid films with selenium microparticles and their application for food packaging.	[78]

), and contribute to the health-promoting qualities of foods [106]. In contrast, synthetic antioxidants are increasingly under scrutiny due to their potential toxicological risks [107]. Natural antioxidants offer a safer, biobased alternative, aligning with clean-label trends and consumer demand for sustainable and health-conscious food solutions [104].

4. Discussion

Oxidative stress occurs when unstable molecules called free radicals build up and start damaging cells (a process somewhat analogous to rust forming on metal). This can happen from excessive sun exposure, pollution, a poor diet, or even just normal metabolism [108]. Antioxidants are compounds that neutralize these free radicals, and research has shown that pairing vitamins C and E improves immune-function measures, suggesting that the combination helps bolster the aging immune system under stress [109]. Many other antioxidants are often paired together in supplements and recommended jointly in diets; after all, nature frequently packages them together in many wholesome foods [110]. However, most of the antioxidants present in foods—whether their efficacy in preventing oxidation in foods or in dealing with oxidative stress in the body—depend on different factors [111]. This is where the application of modeling, prediction, and control tools comes into play [20].

For most studies included in this review (Figure 1, Table 1) [10,11,12,13,14,15,16,17,18,19,20,21,22,23,24], the process is the following: (a) data collection, (b) modeling of antioxidant activity (e.g., DPPH and FRAP assays) based on input variables using artificial neural networks (ANNs), (c) optimization process via algorithm, and (d) reporting of outcomes [13,14,15,112]. According to Ayres et al., the prediction capability of a model for mixtures of phenolic antioxidants, based on the thiobarbituric acid reactive substances (TBARS) method, was analyzed over 140 tests to assess variability, and the accuracy rate of the model analysis was 90% [12]. The use of such applications could offer more rational predictions related to the behavior of antioxidant mixtures and help fulfill the principles of the 3Rs (replacement, reduction, and refinement) in using animals in biomedical research [11]. Moreover, they minimize inconsistencies and inaccuracies of results from sensitive gas chromatography–mass spectrometry (GC/MS) method [113]. Some biomarkers used in these studies were F2-isoprostanes, malondialdehyde, exhaled volatile alkanes (e.g., ethane and pentane), D2 and E2 isoprostanes, and lipid hydroperoxides [11,12]. AI-driven approaches for predicting antioxidants in natural products usually combine bio-informative and machine learning approaches [114]. Convolutional neural network (CNN)-based deep learning (DL) models extract multilayered features directly from raw spectral inputs through end-to-end learning, significantly outperforming traditional manual feature extraction methods and enhancing classification accuracy [115]. In addition, the error rate was found to be approximately 10%, corresponding to approximately 90% accuracy (Table 2) [114]. The relationship between antioxidant substances and antioxidant activity demonstrated that AI can be used effectively to determine the antioxidant properties of foods [114]. For example, AI applications confirmed that preventing oxidation of unstable fish lipids is still achieved by using tocopherols (especially in mixtures with ascorbyl palmitate, lecithin, or rosemary extract) and by using antioxidant preparations from rosemary [24]. AI-supported integration of metabolomics, transcriptomics, and bioinformatics is assisting in identifying algal bioactives (like astaxanthin and fucoxanthin), modelling metabolic pathways, predicting bioavailability, and building intelligent delivery systems. It also supports functional food engineering (e.g., optical sensing freshness, targeted release, and sustainable applications) and significantly accelerates development, reducing timelines by over 60% compared to traditional methods [116].

On the other hand, the ORAC method is a well-known laboratory test that measures the overall antioxidant capacity of a food [15]. Specifically, in this test, the food being tested is placed in a system that generates free radicals and consists of molecules vulnerable to oxidation. By measuring the oxidation that occurs in these molecules after a certain period of time, one can draw a conclusion about the antioxidant capacity of the food, that is, how well it protects the vulnerable molecules from free radicals [88]. The less damage that has occurred, the higher the ORAC score for that food. Measurements of the oxidative capacity of hundreds of foods, drinks, and spices have been compiled into a publicly accessible database that can be found on the Internet [15]. A simple browse through the database can help consumers discover which foods are rich in antioxidants and generate ideas on how to increase antioxidants, and the consumer or patient can easily measure whether they are getting enough antioxidant intake daily. However, users must be cautious: because the score for each food is based on 100 g of product, one must factor in the portion size actually consumed before concluding that oregano in a salad is not a major source of antioxidants since the oregano’s score per 100 g may be high, but the consumed portion is minimal [112]. The ORAC database has often been cited as a reliable measure of antioxidant capacity in foods and supplements. However, it has significant limitations as studies have shown a poor correlation between ORAC values and actual in vivo effects in humans. Due to these concerns and the potential for misleading marketing claims, the USDA officially withdrew the ORAC database in 2012. This underscores the importance of interpreting antioxidant data cautiously and considering biological relevance rather than relying solely on in vitro assays. Furthermore, a claim that a food or supplement has a high ORAC value (or “antioxidant capacity”) does not mean that it is permitted to make a health claim in the EU. For researchers or manufacturers preparing claims related to antioxidant/oxidative damage or health effects guidance from the EFSA emphasizes physiological relevance, human intervention data, well-defined biomarkers, appropriate study durations, etc. [15,88].

The score of each food as estimated by the previous applications can therefore be used as an indicator of whether it has a relatively high antioxidant content or not, but this number does not necessarily translate into its exact antioxidant capacity in the body [117]. Besides, antioxidants are only one part of the diet. Every food contains other ingredients that are necessary for the body but do not have antioxidant activity; therefore, they may yield a “poor” score. The main objective is that the goal is not to maximize antioxidant intake for its own sake [118], but to address food insecurity [93].

In today’s world of stress, pollution, and UV exposure, natural antioxidant supplements are used as extra defense against oxidative damage, supporting skin, immunity (Figure 3), and even eye health [108]. This synergistic benefit of antioxidants is a big reason why many supplement regimens and fortified skincare products pair, for instance, vitamins C and E (Table 3) [109]. Whether in a daily multivitamin, an immune-boosting supplement, or an “anti-aging” skin formula, the two vitamins are thought to help regenerate and “recycle” one another to maintain a robust antioxidant network [119]. By using natural antioxidants in combination, supplement manufacturers aim to mimic how these nutrients naturally cooperate in fruits, vegetables, and our body systems to defend against oxidative damage (Table 5) [120]. The tolerable upper intake levels (ULs) for vitamin E from all dietary sources, previously established by the Scientific Committee on Food, are retained for all population groups 300 mg/day for adults, including pregnant and lactating women, 100 mg/day for children aged 1–3 years, 120 mg/day for ages 4–6 years, 160 mg/day for ages 7–10 years, 220 mg/day for ages 11–14 years, and 260 mg/day for ages 15–17 years [121]. The Daily Reference Intake (DRI) for vitamin C is 80 mg/day [122], and the reference value for vitamin C intake is set to 135 mg/day for female smokers and 155 mg/day for male smokers [123], although the European Food Safety Authority (EFSA) has not yet established a reference value [124]. Data from dietary surveys in European countries show that average vitamin C intakes from food alone range from 69 to 130 mg/day in men and from 65 to 138 mg/day in women [124]. Regarding polyphenols, increases in dietary exposure have been observed in the categories “Legumes, nuts, oilseeds, and spices” (+0.32 μg/kg per month), “Vegetables and vegetable products” (+0.17 μg/kg per month), and “Coffee, cocoa, tea and infusions” (1.28 μg/kg per month) [125]. However, no plant-origin food has yet obtained a favorable EFSA opinion to substantiate health claims related to its potential antioxidant properties [126]. While supplements are available and sometimes recommended for those with deficiencies or elevated needs, the ideal way to obtain these vitamins remains a balanced diet [127].

This is why there is an increasing interest and research efforts on the application of nanotechnology to food, alongside the potential of AI in science and the food industry, resulting in a significant increase in human exposure to these substances [4]. Consumers should, therefore, be very careful, not necessarily fear but accept the development of AI and its application in food science and industry. Besides, AI has already achieved great successes in other fields. Food packaging provides many benefits to both the product being packaged (e.g., freshness of fruits) and the consumer himself [128]. For example, the gelatin/curcumin composite films exhibited remarkable antimicrobial activity against foodborne pathogenic bacteria such as Escherichia coli and Listeria monocytogenes and showed strong antioxidant activity comparable to ascorbic acid [59].

Moreover, the mention of AI is important in laying down the relationship between preventive medicine or treatment techniques and patient outcome [129]. Antioxidants can be used in health applications either for prediction or for prevention of diseases as nutraceuticals (Figure 3) [129] in the fields of functional foods [115] or innovative pharmaceuticals and high-value cosmetics [116]. Biosensors and real-time data are transforming how consumers understand, manage, and optimize their diets [15], but also help health professionals in practice to predict the proper combinations for pharmacological use [81]. The synergistic use of AI in functional foods has advanced development in the food, health, and nutrition sectors [130]. In solid tumors, the challenge is not just finding biomarkers, but ensuring they are predictive, reproducible, and strategically actionable. Traditional single-gene or even multi-gene approaches rarely capture this complexity. A more powerful approach combines dynamic systems modeling with network validation: chaos-theoretic analysis of protein–drug interactions can flag promising therapeutic leads at the very early stages (Table 4) [131]. Interactions between polyphenols and proteins in the body are a major area of research in nutritional biochemistry, pharmacology, and food science. These interactions can alter protein conformation and stability, inhibit or enhance enzyme activity (e.g., inhibition of digestive enzymes like α-amylase or trypsin), and affect transport proteins such as serum albumin, thereby influencing the bioavailability and pharmacokinetics of drugs and other metabolites [132]. By identifying subtle nonlinear patterns that precede resistance or failure, this approach reduces costly late-stage surprises and saves resources. Network-based validation then confirms whether these leads are anchored in stable disease modules or critical protein–protein interaction hubs. Such network context ensures biomarkers are not false positives, but true indicators of disease-driving mechanisms. When layered with multiomics integration, these methods reveal biomarker signatures that both stratify patients and point to novel therapeutic vulnerabilities. The result is a biomarker strategy that goes beyond “finding signals” [133]. It builds strategic assets—early-warning markers of relapse, network hubs that double as drug targets, and validation frameworks that directly inform trial design [134]. Together, chaos-informed discovery and network-guided validation can help oncology pipelines concentrate investment where it matters most: leads with the highest probability of success in patients [131].

The AI in healthcare market is experiencing explosive growth due to (1) AI enabling personalized treatment plans by analyzing genetic, lifestyle, and clinical data [135]; (2) hospitals, clinics, and research institutions generating vast amounts of data, creating demand for AI-powered analytics [136]; (3) faster diagnostics by using antioxidants and AI tools for radiology, pathology, and diagnostics reducing wait times and increasing accuracy; and (4) AI offering efficiency gains and automation that save both time and money [137].

On the other hand, a recent report reveals a sobering reality: 95% of generative AI pilots in companies (healthcare and pharmaceuticals) fail to deliver measurable financial impact [138] because expectations of AI are exceptionally high. While generative AI holds extraordinary promise, most corporate initiatives are stalling [139]. A recent study pinpoints the reasons: (1) a “learning gap”—the main issue is not weak AI models but poor integration into workflows; (2) misguided investment—more than half of corporate AI budgets are directed toward sales and marketing, even though the highest returns come from back-office automation; and (3) partnership advantage—companies that purchase AI tools from specialized vendors achieve a 67% success rate, far higher than those that develop solutions in-house. The winners in generative AI will not be those who dabble in pilots, but those who learn, partner, and scale responsibly [138]. Despite the failings highlighted, the pharmaceutical sector shows how generative AI can succeed when applied with focus and expertise [138].

While nano-antioxidants show great promise for biomedical, cosmetic, and food applications, they also raise safety and nanotoxicity concerns. Extremely small particles (<10 nm) can penetrate cell membranes, mitochondria, and nuclei, causing DNA damage or organelle dysfunction [140]. The core material (e.g., metal oxides like CeO₂, TiO₂, and ZnO) and surface modifiers (e.g., polymers and ligands) strongly influence toxicity. Chronic exposure can lead to bioaccumulation in organs like the liver, spleen, kidneys, or brain. Long-term retention may trigger inflammatory or immune responses, fibrosis, or genotoxicity [141]. Nanoparticles interact with proteins, forming a protein corona that changes their biological identity. This interaction affects cellular uptake, biodistribution, and immune recognition, leading to unpredictable in vivo responses [140]. Limited data exist on ecosystem-level impacts. Moreover, no unified global framework specifically addresses nano-antioxidant safety. Regulatory agencies (e.g., the FDA, EFSA, and OECD) are still developing nano-specific guidelines for toxicity, labeling, and risk assessment. Ethical concerns include human exposure in insufficiently tested applications and limited data transparency from manufacturers [142].

AI models have shown great promise in food quality assessment, authentication, and safety prediction; however, their performance is often limited by challenges such as overfitting, data leakage, and poor generalization to real-world food matrices. Overfitting occurs when a model learns patterns and noise specific to the training data rather than the underlying relationships, resulting in high accuracy during model development but poor predictive performance when applied to new samples [143]. This issue is particularly problematic in food systems, where models trained on homogeneous datasets (e.g., samples from a single production batch or controlled laboratory conditions) may fail when tested on products from different sources or with variable compositions. Data leakage represents another critical issue, arising when information from the test set inadvertently influences the training process—such as through improper data preprocessing or batch overlap between training and validation sets—leading to artificially inflated performance metrics and misleading conclusions about model robustness. Furthermore, poor generalization is a major concern due to the inherent complexity and variability of food matrices. Factors such as differences in raw materials, processing conditions, environmental factors (e.g., temperature and humidity), and instrumental variations (e.g., calibration differences among spectroscopic devices) can cause significant discrepancies between laboratory and industrial conditions [144]. As a result, models that perform well in controlled experimental settings often fail to replicate their accuracy in real-world applications. These limitations underscore the need for robust model validation, including the use of diverse and representative datasets, rigorous cross-validation techniques, regularization methods to reduce overfitting, and external validation on independent, real-world samples to ensure reliability and scalability of AI-based food analysis systems [143].

AI is not the strategist; it is the catalyst. In today’s volatile landscape, leaders must embrace AI not as a replacement for judgment but as a force multiplier for clarity, speed, and precision. The real competitive edge lies in how we frame the questions, not just how fast we get the answers. Strategy is no longer a slow, linear process, but it is dynamic, data-infused, and deeply human. The leaders who thrive will not just adopt AI tools; they will cultivate AI fluency across their teams, build proprietary insight engines, and remain relentlessly curious. Because in the end, it is not about having smarter machines; it is about building wiser organizations. These organizations could use laboratory data, either microbiology or chemical analysis, to its fullest potential. Standardized guidelines for nanomedicine approval are still evolving. Future directions include green synthesis approaches, smart nanoantioxidants responsive to oxidative microenvironments, and personalized nanotherapy based on oxidative stress biomarkers. These directions may also highlight AI tool variability and explore the challenges in developing a healthy and sustainable food industry that can be maintained even with the increasing entry of nanotechnology into food systems.

5. Conclusions

This review article highlights how AI is emerging as a catalyst for a new era of healthcare—one that is more agile, insightful, and human-centered. With tools like ORAC, achieving optimal dietary control and personalized nutrition has become easier than ever and, thus, would lead to further healthcare promotion. Managing antioxidant assessment is now more innovative and user-friendly. In 2025, cutting-edge tools are transforming food monitoring, meal planning, and lifestyle adjustments into processes that are not only easier but also more precise. These management tools offer to consumers the ability to know the proper use of antioxidants because they allow real-time antioxidant capacity readings, thus providing immediate alerts for high or low levels. This empowers both consumers and health professionals by offering better control and actionable insights for dietary and antioxidant adjustments. Combining nanotechnology with natural antioxidants opens up a powerful new approach to improving human health by overcoming limitations of conventional antioxidant delivery. This synergy enhances the efficacy, stability, and bioavailability of antioxidants, unlocking their full potential for preventive and therapeutic applications. Moreover, the incorporation of natural antioxidants into food packaging offers a dual benefit for food safety and public health. It helps reduce spoilage, lowers the risk of chronic diseases linked to oxidized fats and synthetic preservatives, supports eco-friendly and health-conscious consumption, and encourages innovation in smart and active packaging solutions. The main finding is that the combination of nanotechnology with natural antioxidants and their inclusion in food packaging offers a dual advantage for food safety and public health. In conclusion, the convergence of AI and natural antioxidants creates transformative frontiers in food science.