AI-powered breakthroughs in material science and biomedical polymers

Abstract

This commentary examines how Artificial Intelligence (AI) and Machine Learning (ML) are transforming biomedical polymers, drug delivery systems, wearable electronics, smart materials, advanced manufacturing, and neuromorphic technologies. AI enhances prediction accuracy, optimizes material properties, accelerates development, and enables innovative applications such as smart biomaterials, personalized medicine, and tissue engineering. Specific applications include predicting polymer properties, optimizing drug release kinetics, improving drug delivery system design, and creating responsive materials for advanced biomedical devices. AI also advances wearable sensors, flexible electronics, 3D/4D printing, sustainable materials, and neuromorphic computing, leading to breakthroughs in health monitoring, human-computer interaction, and environmental sustainability.

Keywords

Artificial intelligence biomedical polymers drug delivery systems machine learning smart biomaterials

Introduction

The rapid integration of Artificial Intelligence (AI) into biomedical engineering marks a transformative era in healthcare and materials science. With a growing need for more targeted and efficient therapeutic solutions, AI has emerged as a crucial tool in accelerating research and development in biomedical polymers and drug delivery systems. These computational techniques have enhanced the capabilities of traditional methodologies, enabling scientists to explore vast design spaces, optimize materials, and predict complex behaviors that were previously challenging. Moreover, the application of AI extends to wearable electronics, smart materials, advanced manufacturing, and neuromorphic technologies, pushing the boundaries of personalized medicine, tissue engineering, and sustainable solutions. This commentary delves into how AI is reshaping these diverse fields, exploring its multifaceted applications and implications. Moreover, specific examples of AI applications in these fields are discussed in the following sections.

AI in biomedical polymers and drug delivery systems

The integration of machine learning into biomedical polymers and drug delivery systems increases prediction accuracy, optimizes material properties, and speeds up development, leading to progress in smart biomaterials, drug delivery, tissue engineering, and personalized medicine. This synergy between advanced computational methods and biomedicine is opening up new possibilities for innovative and targeted therapeutic solutions.

Machine learning techniques have been widely used to predict polymer properties and streamline their development. For example, models like Bayesian logistic regression, K-nearest neighbors (KNN), and Naive Bayes have been applied to predict the anti-inflammatory properties of polymers, accelerating biomaterial development by analyzing cellular assay data.¹ Furthermore, computational models such as artificial neural networks (ANN) and least squares support vector machines (LS-SVM) have shown high accuracy in predicting the deswelling behaviors of pH- and temperature-responsive hydrogels, enhancing the understanding and application of these materials.²

Significant advancements are also being made in drug delivery systems (DDS) and tissue engineering. Techniques like high-content liquid handling combined with machine learning are expected to open up new opportunities in constructing Layer-by-Layer (LbL) films, which play a key role in drug delivery and tissue engineering.³ Additionally, when paired with 3D printing technology, these computational methods improve the precision and effectiveness of DDS by optimizing drug release kinetics, supporting personalized medicine, and increasing patient compliance by creating customized dosage forms that are easier for patients to use and adhere to.⁴ For instance, patient-specific implants or oral dosage forms tailored to individual needs can enhance comfort and acceptance, thereby improving compliance. Simulations based on these advanced techniques are being used to refine DDS design, resulting in more effective pharmaceutical treatments.⁵

In the development of smart biomaterials and nanocomposites, machine learning plays a vital role. For instance, it has been integrated into creating biomimetic elastomeric and conductive nanocomposites for skeletal muscle regeneration, enabling the design of multifunctional materials that mimic muscle-like properties.⁶ In stimuli-responsive biomaterials, intelligent modeling facilitates behaviors such as self-folding, controlled drug release, and guiding stem cell behavior for tissue regeneration, which has significant implications for advanced biomedical applications.⁷

Applications extend to self-healing polymers, enhancing the material’s ability to repair physical damage, thereby broadening its use in biomedical monitoring, radiation shielding, and environmental sustainability.⁸ Big data analytics are also key in personalized medicinal therapy, particularly in optimizing treatment outcomes for patients after heart stent implantation through pharmacogenomic selection and risk-scoring systems.⁹

In refining gene delivery systems, advanced computational approaches analyze polymer component distribution (PCD) in gene delivery vectors, creating highly efficient polymer vectors that outperform existing commercial systems.¹⁰ Moreover, they optimize delivery systems such as those based on polyurethane (PU), leading to more targeted and effective treatments.¹¹ Polyurethane possesses versatile properties, such as biocompatibility, mechanical strength, and biodegradability, making it an excellent candidate for drug delivery applications. The use of advanced computational approaches allows for the optimization of PU’s properties to enhance drug loading and release profiles, tailoring them for specific therapeutic needs. In the field of nanotechnology, these methods support the development of carbon dot-based drug delivery systems designed to penetrate the blood-brain barrier, offering potential solutions for conditions like Alzheimer’s disease and brain tumors.¹²

In tissue engineering, machine learning assists in designing and fabricating resorbable scaffolds, improving the replication of natural bone structures and enhancing therapeutic outcomes.¹³ A genetic algorithm (GA)-coupled ANN model has been developed to predict the porosity of alginate gel scaffolds, showing high accuracy in optimizing scaffold fabrication parameters.¹⁴ Additionally, optimization techniques in the production of electrospun scaffolds have advanced wound healing, particularly in promoting neoangiogenesis.¹⁵

The role of computational modeling in biomedical polymers also extends to emerging technologies such as organ-on-a-chip (OoC) devices, where data-driven methods enable precise and cost-effective clinical monitoring and personalized treatment.¹⁶ Furthermore, these methods are being explored in designing colon-targeted therapeutics by integrating in vitro, in vivo, and in silico preclinical investigations to fine-tune drug formulation and minimize off-target effects.¹⁷ Deep learning is also being proposed to enhance the cleaning, disinfection, and sterilization processes of reusable medical devices, addressing infection control challenges and ensuring patient safety.¹⁸

The advancements in AI and ML within drug development and broader biomedical research have direct implications for material science and biomedical polymers. The computational power of AI/ML technologies, which reduces both the time and costs associated with drug development,¹⁹ can similarly streamline the research and development of biomedical polymers by facilitating the prediction and optimization of polymer properties. This ability is pivotal for the creation of smart biomaterials and drug delivery systems that require precision engineering.

AI’s applications, including virtual compound screening and ADMET (absorption, distribution, metabolism, excretion, and toxicity) modeling,^20,21 are transferable to the design of biocompatible polymers, where optimizing characteristics like biodegradability, mechanical strength, and response to environmental stimuli are essential. The use of ML to analyze large datasets^20,22 is similarly beneficial in the development of responsive materials that interact effectively with biological environments, thus supporting more personalized and efficient drug delivery systems.

The success of platforms such as Chemistry42 in generating novel molecular structures²¹ suggests that similar frameworks can be applied to developing new polymer composites with targeted properties for applications in tissue engineering and regenerative medicine. Moreover, industry collaborations facilitated by AI, like those between Numerate and Takeda or Atomwise’s partnerships with academic labs,^20,21 highlight the potential for cross-sector innovations that can extend to polymer science. Finally, breakthroughs like AlphaFold2′s protein structure prediction and Insilico Medicine’s Chemistry42¹⁹ exemplify how AI-driven models can solve complex structural problems, a capability that can potentially translate into better design and application of biomedical polymers for therapeutic use.

AI in wearable electronics, sensors, and smart materials

The integration of advanced computational techniques into wearable electronics, sensors, and smart materials is transforming these technologies in areas like healthcare, robotics, and human-computer interaction. This enhances the functionality, responsiveness, and applicability of flexible electronics, smart materials, and biocompatible sensors. Such advancements pave the way for innovative solutions in personalized medicine, health monitoring, and intelligent interfaces, shaping the future of wearable technology and smart materials.

Machine learning and other computational methods are increasingly used in developing flexible electronics, particularly Organic Thin Film Transistors (OTFTs) utilized in neuromorphic computing that mimics the neural architecture of the human brain. These OTFTs are essential for advancing Internet of Things (IoT) devices and flexible sensors.²³ Similarly, signal processing in smart tactile sensing systems made from carbon nanotube/polypropylene composites benefits from artificial neural networks, leading to highly accurate wearable sensor systems.²⁴

In the field of wearable sensors, data-driven techniques play a key role. For example, they are involved in developing a fiber strain sensor for wearable electronics, which forms part of a data glove linking gestures to an intelligent gesture-expression control system.²⁵ Furthermore, such systems are utilized in designing biodegradable wearable sensors for human motion and sweat monitoring, facilitating real-time data collection and analysis.²⁶

These techniques significantly contribute to the advancement of smart materials, including electro-stimulated gels, which are expected to find increasing use in robotics and wearable artificial muscles.²⁷ They also enhance electronic skin (E-skin) properties, improving stretchability and self-healing capabilities for applications in prosthetics, robotics, and personal health monitoring devices.²⁸ Additionally, advanced design approaches have led to the creation of silk fibroin-based flexible pressure sensors, useful in human-computer interaction, personalized medicine, and sustainable electronics.²⁹

In personalized medicinal platforms, the integration of flexible biosensors, heaters, and processing units enables automated, continuous therapy conformal to the skin. This system is cost-effective and particularly suitable for low-resource environments.³⁰ Data-driven design is also critical in advancing skin-interfaced triboelectric sensors (SITSs) made from biocompatible polymers for human-machine interfaces and physiological sensing.³¹

In bioprinting, particularly for wound healing, machine learning helps optimize processes, enhancing the design and functionality of bioprinted devices and supporting personalized wound care solutions.³² This technology is also utilized in developing stimuli-responsive hydrogels with programmable shape changes for soft robots, 4D printing, and biomedical devices, highlighting its role in creating intelligent materials.³³

The development of multifunctional composite hydrogel sensors capable of detecting various stimuli like force, temperature, and UV light benefits significantly from these computational methods, broadening their application in flexible electronic devices.³⁴ Additionally, the design of flexible piezoelectric nanogenerators for energy harvesting from mechanical activities sees improvements in efficiency and performance for integration with wearable systems and biomedical applications.³⁵

In creating conductive hydrogels with antibacterial properties for flexible electronic devices, these techniques enhance functionality as strain sensors for monitoring physiological activities.³⁶ They also assist in designing and optimizing biodegradable, flexible, and biocompatible memory devices made from carboxymethyl cellulose and graphene oxide nanocomposites, offering potential for wearable electronics and e-skin technologies.³⁷

In 4D printing technologies, computational methods are used to develop flexible artificial intelligence materials (AIM) for biomedical applications, such as shape memory polymers for tissue engineering.³⁸ In this context, 4D printing refers to 3D-printed objects that can change their shape or properties over time in response to external stimuli like temperature, light, or moisture—the fourth dimension being time. This technology enables the creation of dynamic structures that can adapt to their environment, which is particularly useful in biomedical applications. Design improvements extend to water-responsive shape memory films used in resistive bending sensors for smart textiles, mimicking natural human skin and expanding applications in electronic skin and health-monitoring devices.³⁹

The role of advanced analytics in health monitoring is evident in its integration with piezo- and pyro-electric wearable sensors for remote monitoring of physiological signals, which has been particularly relevant during the COVID-19 pandemic for early detection and continuous health status monitoring.⁴⁰ Algorithms also support the development of disposable triboelectric nanogenerator (TENG) sensors for evaluating urination conditions, providing an innovative approach to healthcare monitoring with applications in telemedicine.⁴¹

AI in 3D printing, 4D printing, and advanced manufacturing

The integration of advanced computational techniques in 3D and 4D printing is transforming advanced manufacturing by enhancing precision, efficiency, and customization. These techniques optimize processes, improve material properties, and drive innovation in fields like biomedical engineering, robotics, and pharmaceuticals. This synergy between intelligent systems and additive manufacturing enables the creation of responsive materials and devices, leading to groundbreaking advancements in technology and medicine.

One key application is in optimizing 3D printing processes, particularly for enhancing the mechanical properties of polymer composites. Incorporating machine learning into additive manufacturing (AM) significantly improves the quality and performance of printed materials, shaping the future of 3D printing (3DP) and exploring the emerging field of 4D printing (4DP).⁴² Additionally, machine learning models are used to predict the printability and drug dissolution profiles of 3D-printed tablets based on rheological data, facilitating high-throughput screening and more efficient drug formulation processes.⁴³

Advanced computer-aided design (CAD) software, powered by intelligent algorithms, enhances the design and fabrication of smart products, such as eyeglass frames. These “smart” eyeglass frames are not only customized for individual ergonomic comfort but may also incorporate adaptive features like shape memory materials that adjust fit over time or integrate sensors for health monitoring, thereby qualifying them as smart products. This allows for greater customization and improved mechanical properties, making these products more sustainable and functional.⁴⁴ Similarly, these techniques are applied in designing and manufacturing shape memory elastomers (SMEs), predicting and optimizing SME properties, which are crucial for applications in robotics, space engineering, and biomedical devices.⁴⁵

In 4D printing, the design and functionality of materials that change shape or properties in response to external stimuli are greatly enhanced. The fourth dimension in 4D printing is time and stimuli, representing the ability of the printed object to change its shape or properties over time when exposed to specific stimuli like heat, light, or moisture. For instance, shape memory polymers (SMPs) reinforced with MAX and MXene fillers exhibit significantly improved shape recovery speed and thermal/electrical properties due to computational optimization.⁴⁶ The concept extends to 5D printing, where additional degrees of freedom are introduced during the printing process, allowing for even more complex geometries and functionalities. In 5D printing, the print head can move along axes beyond the x, y, and z axes, enabling the creation of structures with enhanced strength and intricate designs that are not possible with traditional 3D printing. The additive manufacturing of smart polymeric composites also benefits from process optimization, creating materials that react to external stimuli, which is essential for advanced biomedical devices and personalized structures.⁴⁷

The impact on biomedical applications is especially prominent in the fabrication of 3D, 4D, and even 5D printed formulations and devices for drug delivery. These computational methods facilitate real-time sensing, adaptation, and prediction during the printing process, improving precision, speed, and complexity, particularly in personalized medicine.⁴⁸ Furthermore, integration with 4D printing technology has led to the development of customized cardiovascular devices, offering both preventive and reactive treatment options for cardiovascular diseases.⁴⁹

In materials science, machine learning models assist in the de novo design of organic molecules and polymers, enhancing the accuracy and efficiency of predicting material properties. This model-assisted design accelerates molecular generation and inverse molecular design, meeting the demand for new materials with tailored properties across various fields.⁵⁰ An artificial neural network (ANN) model has also been developed to predict the electrical conductivity of polymer nanocomposites, such as graphene-polypyrrole composites, thereby reducing experimentation time and costs.⁵¹

Optimization techniques play a crucial role in the design and functionality of polymer therapeutics, aiding in high-throughput synthesis and screening. Establishing quantitative structure-property relationships (QSPRs) enhances the development of effective drug delivery systems.⁵² Furthermore, research is exploring these technologies in the context of shape memory polymers (SMPs) for applications in robotics, aerospace, and biomedical engineering, showcasing notable improvements in material properties.⁴⁶

In the broader field of chemical engineering, process simulation, control, and numerical techniques, such as neural networks, have expanded chemical engineering into areas like biotechnology, renewable energy, and nanotechnology, driving significant advancements.⁵³ Within microfluidics, intelligent methods are integrated with flow chemistry and lab automation to streamline the fabrication of polymer multilayered nanofilms, improving efficiency and enabling potential biomedical applications.⁵⁴

AI in material design, characterization, and optimization

Advanced computational techniques are transforming material design, characterization, and optimization by leveraging machine learning. Researchers can speed up material development, enhance characterization accuracy, and create novel materials with tailored functionalities. These data-driven techniques optimize processes, predict material properties, and increase the efficiency of material synthesis, fostering innovation in advanced materials and enabling new applications in biomedical engineering and beyond.

Machine learning (ML) plays a crucial role in accelerating the design and development of biomedical polymers. Data-driven approaches manage extensive material databases, predict material properties, and generate new molecular structures, addressing challenges in the vast design space of biomedical polymers.⁵⁵ For example, ML techniques aid in the design and characterization of graphene quantum dots (GQD) in polymer nanocomposites, allowing researchers to efficiently explore parameter spaces to achieve desired properties.⁵⁶ Additionally, optimization methods enhance the development of polymer gel materials, which have applications in oil and gas drilling, biomedical fields, and firefighting, improving their intelligent functionalities and performance characteristics.⁵⁷

The integration of computational methods with advanced characterization techniques provides deeper insights into material properties. For instance, combining inverse gas chromatography (IGC) with computer vision—a field of AI that enables computers to interpret and understand visual information— enhances material analysis, particularly in understanding surface texture and roughness. By analyzing images of the material’s surface, computer vision algorithms extract quantitative data about surface features, which, when combined with IGC data, provide a comprehensive understanding of surface properties. This approach offers more detailed and accurate characterization data, facilitating research in advanced materials.⁵⁸ Furthermore, algorithms like XGBoost, CNN, and MLP are employed to predict the viscosities of complex polymers, significantly improving the reliability of these predictions, which are crucial for various polymer applications.⁵⁹

In the production of bioplastics, integrating genetic and metabolic engineering with computational optimization enhances the commercial synthesis of bioplastics from microalgae, reducing costs and increasing efficiency.⁶⁰ Within polymer informatics, machine learning accelerates performance prediction and process optimization for new polymers. These data-driven methods utilize large databases and advanced algorithms to streamline the development process in polymer science and engineering.⁶¹

In biomedical applications, intelligent systems improve the functionality and application of biomaterials. Software powered by these techniques evaluates the vascularization induced by collagen and non-collagen scaffolds in biomaterials, providing an alternative to animal experimentation during pre-screening.⁶² Additionally, the study of thermosensitive conducting hydrogels (TCH) benefits from computational analysis, helping to understand the chemical interactions that affect the volume phase transition (VPT) of these materials, which is crucial for their use in biomedical and environmental fields.⁶³

AI in environmental sustainability and green materials

Advanced computational methods are transforming the development of environmentally sustainable and green materials by optimizing synthesis, production processes, and material properties. These techniques enhance the sustainability and efficiency of materials used in health and environmental monitoring. This approach enables the creation of eco-friendly products, optimizes biodegradable materials, and advances health-monitoring technologies, driving innovation in sustainable material science and green technology.

One crucial application is in optimizing the synthesis and fabrication of green materials. For example, computational modeling is used in synthesizing superabsorbent polymers (SAPs), which are vital for sustainable menstrual health management. By predicting polymer properties and optimizing synthesis pathways, these methods contribute to producing more affordable and eco-friendly menstrual products.⁶⁴ Similarly, teaching-learning-based optimization (TLBO) algorithms have been applied to improve weld quality in laser transmission welding of dissimilar polymers, enhancing process efficiency and welding outcomes.⁶⁵

In the domain of biodegradable materials, data-driven techniques optimize the fermentation process for the biosynthesis of pullulan, a biodegradable hydrogel biopolymer. Using decision tree learning algorithms helps identify key variables in the process, reducing costs and improving sustainability.⁶⁶ Furthermore, they influence the development of environmentally self-degradable conjugated polymers, essential for flexible electronics and smart devices, showcasing the broader impact on material innovation.⁶⁷

Research into hemicellulose-based hydrogels for biomedical applications also benefits from computational methods. These hydrogels, known for their stimuli-responsive and biocompatible properties, are optimized through data-driven approaches, expanding their potential in drug delivery systems and tissue engineering.⁶⁸ These techniques further enhance the fabrication and application of such materials, increasing their effectiveness in various biomedical fields.

Another significant contribution is in evaluating the bioactivity of essential oils (EOs) based on their chemical composition and structure. By predicting the biological activity of EOs, researchers can bypass complex laboratory analyses, saving time and costs while improving product consistency.⁶⁹ Additionally, pattern recognition algorithms are employed in analyzing fluorescence response patterns generated by polymer-based chemical tongues, enabling non-invasive monitoring of osteogenic stem-cell differentiation and cancer-cell contamination detection.⁷⁰

In the development of novel composite materials for real-time environmental and physiological monitoring, these methods play an integral role. For instance, they are involved in designing a graphene/leather-based composite for mechano-monitoring human motion. This enhanced sensor improves the monitoring of physiological signals, contributing to more effective human-machine interactions and health-monitoring technologies.⁷¹

AI in neuromorphic computing and brain-machine interfaces

Neuromorphic computing and brain-machine interfaces (BMIs) are being transformed by advanced computational methods, enhancing the design, performance, and adaptability of the materials used in these technologies. These methods optimize neuromorphic devices, improve the biocompatibility and functionality of BMIs, and contribute to advanced biomedical applications. These innovations push the limits of bioelectronics, enabling sophisticated systems that mimic neural processes and enhance human-machine interaction.

Machine learning plays a pivotal role in developing neuromorphic computing devices designed to emulate the synaptic functions of the human brain. For instance, computational techniques are employed in creating organic phototransistors (OPTs) that function as artificial synapses, allowing for more accurate emulation of biological synaptic functions and facilitating advanced applications in neuromorphic computing.⁷² Enhancements in the functionality of biomemristors—critical components in bioelectronics for information processing and human-machine interaction—are achieved through optimization methods, making these biomaterial-based devices more suitable for both biomedical and environmental uses.⁷³

In designing and optimizing materials for BMIs, machine learning techniques aid in the development of advanced materials, such as conducting polymer-based nanostructures, which combine biocompatibility, electrical conductivity, and mechanical properties essential for neurological applications.⁷⁴ Additionally, these methods boost the performance of flexible electronics made from 2D materials. The optimization of such devices, which integrate multiple functionalities, shows potential to surpass current technologies in creating high-performance, flexible, and wearable electronics.⁷⁵

In the field of biomedical devices, especially for managing chronic conditions like diabetes, artificial neural networks (ANNs) are utilized to predict blood glucose levels in diabetic patients. These predictions are then integrated into hardware systems using organic polymer-based electronics, paving the way for developing implantable devices that could potentially function as a fully artificial pancreas, revolutionizing diabetes management.⁷⁶ The combination of big data and 5G communication with flexible sensors further builds cyber-human interaction systems, enhancing the sensitivity and stability of monitoring signals—crucial for intelligent e-healthcare and biomedical applications.⁷⁷

Advancements in materials used for neuromorphic computing and BMIs also benefit from data-driven techniques. These methods assist in studying PEDOT-ZnO nanoparticle hybrid film-based memristors designed to mimic synaptic behavior in intelligent systems, thereby helping create more efficient neuromorphic computing architectures.⁷⁸ Furthermore, the development of carbon-based magnetic polymers for applications in spintronics, high-density data storage, and quantum computing sees significant improvements in magnetic properties, facilitating their use in next-generation technologies.⁷⁹

The concept of chemical artificial intelligence systems represents an emerging field where principles of computational analysis are applied to chemical systems to mimic human intelligence. This includes exploring computational power in chemical reactions, such as the Belousov-Zhabotinsky reaction, to replicate neural dynamics and the human mind’s processing capabilities.⁸⁰ Additionally, enhancing biomaterials for bioelectronics is supported by image processing and information extraction techniques in electron microscopy, allowing for improved analysis and performance of materials like nanostructured polymers.⁸¹

Table 1 outlines various patterns in AI/ML applications within material science and biomedical polymers. It categorizes challenges and objectives such as prediction of material properties, optimization of drug delivery systems, and the design of smart biomaterials. Techniques employed include machine learning algorithms, artificial neural networks (ANN), and computational modeling. Application areas span biomedical polymers, tissue engineering, and wearable electronics. Outcomes highlight benefits like improved material properties, enhanced therapeutic results, and sustainability.

Table 1.

AI/ML applications in material science and biomedical polymers.

Common challenges/Objectives	AI/ML techniques employed	Application areas	Outcomes/Benefits	References
Prediction of material properties	- Machine learning algorithms - ANN - LS-SVM	- Biomedical polymers - Hydrogels - Nanocomposites	- Accurate prediction of properties like anti-inflammatory effects, deswelling behaviors, electrical conductivity	Akkache et al.¹, Boztepe et al.², Folorunso et al.⁵¹, Dananjaya et al.⁵⁶
Optimization of drug delivery systems (DDS)	- Computational modeling - Optimization techniques	- Drug release kinetics - Personalized medicine	- Enhanced precision in drug delivery - Customized dosage forms - Increased patient compliance	Saxena and Malviya⁴, Salahshoori et al.⁵, Song et al.¹¹, McCoubrey et al.¹⁷
Design of smart biomaterials	- Intelligent modeling - Machine learning	- Stimuli-responsive materials - Self-healing polymers	- Development of materials with self-folding, controlled release, and repair capabilities	Amukarimi et al.⁷, Kim et al.⁸, Liu et al.³³
Enhancing tissue engineering scaffolds	- Genetic algorithms - ANN	- Scaffold porosity optimization - Bone tissue engineering	- Improved scaffold designs that mimic natural bone structures - Enhanced therapeutic outcomes	Alavi et al.¹³, Das et al.¹⁴
Advancement in wearable electronics and sensors	- Data-driven techniques - ANN	- Flexible sensors - E-skin - Health monitoring	- High-accuracy sensors for physiological data - Stretchable, self-healing electronic skin	Atoche-Ensenat et al.²⁴, Li et al.²⁶, Benight et al.²⁸, Ghosh and Mandal⁴⁰
Innovation in advanced manufacturing	- Machine learning models - Computational methods	- 3D/4D/5D printing - Smart products	- Improved material properties - Creation of responsive materials - Complex geometries with enhanced strength	Arora et al.⁴², Tsegay et al.⁴⁴, Jaiswal et al.⁴⁶, Sharma⁴⁸
Sustainable material development	- Computational modeling - Decision tree learning	- Bioplastics synthesis - Biodegradable hydrogels	- Cost-effective production processes - Eco-friendly materials with optimized properties	Adetunji and Erasmus⁶⁰, Kumar et al.⁶⁵, Tian et al.⁶⁷, Chiriac et al.⁶⁹
Neuromorphic computing and brain-machine interfaces (BMIs)	- Computational techniques - AI algorithms	- Artificial synapses - Bioelectronics	- Devices emulating neural functions - Advanced materials for BMIs - Improved human-computer interaction	Sun et al.⁷³, Ziai et al.⁷⁴, Hou et al.⁷⁵, Zhang et al.⁷⁷
Personalized healthcare solutions	- Big data analytics - ANN - AI algorithms	- Personalized therapy - Chronic disease management	- Optimized treatment plans - Predictive health monitoring - Integration into implantable devices	Mohamad et al.⁹, Zhang et al.⁷⁷, Fan et al.⁷⁸
Material characterization and design	- Machine learning (Polymer informatics) - Computer vision	- Surface property analysis - Molecular design	- Accelerated discovery of new materials - Detailed understanding of surface textures and interactions	Xie⁵⁵, Basivi et al.⁵⁸, Sha et al.⁶¹
Process optimization in manufacturing	- Teaching-learning-based optimization (TLBO) - Optimization algorithms	- Welding processes - Energy harvesting devices	- Improved weld quality and efficiency - Enhanced performance of energy devices	Yao et al.³⁵, Saber et al.⁶⁶
Enhancing biomedical device sterilization	- Deep learning	- Cleaning and disinfection processes	- Improved infection control - Ensured patient safety	Rowan et al.¹⁸
Non-invasive health monitoring techniques	- Pattern recognition algorithms	- Chemical tongues - Wearable sensors	- Detection of stem-cell differentiation - Monitoring of physiological signals	Qin et al.⁷¹, Tavasli et al.⁷²

Concluding remarks

This commentary emphasizes the transformative impact of Artificial Intelligence (AI) in advancing biomedical polymers, drug delivery systems, and neuromorphic technologies. AI has greatly improved predictive modeling, optimized material properties, and accelerated the development of novel biomedical solutions, leading to faster development cycles and more targeted applications. Additionally, AI has been instrumental in optimizing drug release kinetics, designing drug delivery systems (DDS), and creating smart biomaterials that mimic biological functions, enhancing tissue regeneration and personalized medicine. AI’s integration with wearable electronics and neuromorphic technologies has also driven significant progress in healthcare monitoring and human-computer interaction. However, challenges like data availability, computational complexity, and the interpretability of AI models persist. These limitations can restrict AI’s predictive power and complicate its adoption in clinical settings where transparency is crucial. Ethical and regulatory concerns, particularly related to data privacy and potential biases in AI-driven decision-making, further complicate the broader use of AI in biomedical applications.

While AI is propelling significant advancements in biomedical engineering, addressing challenges related to data quality, computational complexity, model interpretability, and ethical considerations is necessary to fully harness its potential. Future research directions include developing AI-integrated multiscale models, optimizing AI for sustainable materials, and enhancing real-time monitoring in biomedical applications. Establishing ethical frameworks and regulatory guidelines will be vital for the safe and equitable use of AI in healthcare. Continued interdisciplinary collaboration will be essential to overcome these challenges and further advance the field.

Footnotes

Declaration of conflicting interests

The author declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Authors partly used OpenAI Large-Scale Language Model to maximize accuracy, clarity, and organization.

Funding

The author received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Hamid Omidian

References

Akkache

Clavier

Mezhenskyi

, et al. Machine learning-based prediction of immunomodulatory properties of polymers: toward a faster and easier development of anti-inflammatory biomaterials. Adv NanoBiomed Res 2024; 4(3): 11.

Boztepe

Yüceer

Künkül

, et al. Prediction of the deswelling behaviors of pH- and temperature-responsive poly(NIPAAm-co-AAc) IPN hydrogel by artificial intelligence techniques. Res Chem Intermed 2020; 46(1): 409–428.

Borges

Zeng

Liu

, et al. Recent developments in layer-by-layer assembly for drug delivery and tissue engineering applications. Adv Healthc Mater 2024; 13(8): e2302713.

Saxena

Malviya

3D printable drug delivery systems: next-generation healthcare technology and regulatory aspects. Curr Pharm Des 2023; 29(35): 2814–2826.

Salahshoori

Golriz

Nobre

MAL

, et al. Simulation-based approaches for drug delivery systems: navigating advancements, opportunities, and challenges. J Mol Liq 2024; 395: 57.

, et al. Biomimetic elastomeric, conductive and biodegradable polycitrate-based nanocomposites for guiding myogenic differentiation and skeletal muscle regeneration. Biomaterials 2018; 157: 40–50.

Amukarimi

Ramakrishna

Mozafari

Smart biomaterials—a proposed definition and overview of the field. Curr Opin Biomed Eng 2021; 19: 11.

Kim

Jeon

Koo

, et al. Practical applications of self-healing polymers beyond mechanical and electrical recovery. Adv Sci 2024; 11(16): e2302463.

Mohamad

Jyotsna

Farooq

, et al. Individualizing medicinal therapy post heart stent implantation: tailoring for patient factors. Cureus 2023; 15: e43977.

10.

Z, A S

, et al. Artificial intelligence (AI)-aided structure optimization for enhanced gene delivery: the effect of the polymer component distribution (PCD). ACS Appl Mater Interfaces 2023; 15(30): 36667–36675.

11.

Song

Muhammad

Dang

, et al. The state-of-art polyurethane nanoparticles for drug delivery applications. Front Chem 2024; 12: 1378324.

12.

Zhang

Sigdel

Mintz

, et al. Carbon dots: a future blood–brain barrier penetrating nanomedicine and drug nanocarrier. Int J Nanomedicine 2021; 16: 5003–5016.

13.

Alavi

Gholami

Shahmabadi

, et al. Resorbable GBR scaffolds in oral and maxillofacial tissue engineering: design, fabrication, and applications. J Clin Med 2023; 12(22): 34. Review 20231107.

14.

Das

Karthika

Bhasarkar

, et al. GA-coupled ANN model for predicting porosity in alginate gel scaffolds. J Mech Behav Biomed Mater 2023; 148: 106204.

15.

Virijević

Živanović

Nikolić

, et al. AI-driven optimization of PCL/PEG electrospun scaffolds for enhanced in vivo wound healing. ACS Appl Mater Interfaces 2024; 16: 22989–23002.

16.

Nahak

Mishra

Preetam

, et al. Advances in organ-on-a-chip materials and devices. ACS applied bio materials 2022; 5(8): 3576–3607.

17.

McCoubrey

Favaron

Awad

, et al. Colonic drug delivery: formulating the next generation of colon-targeted therapeutics. J Control Release 2023; 353: 1107–1126.

18.

Rowan

Kremer

McDonnell

A review of Spaulding's classification system for effective cleaning, disinfection and sterilization of reusable medical devices: viewed through a modern-day lens that will inform and enable future sustainability. Sci Total Environ 2023; 878: 162976.

19.

Soni

Hasija

. Artificial intelligence assisted drug research and development. In: IEEE Xplore, New Delhi, India, 2022. IEEE.

20.

Smalley

AI-powered drug discovery captures pharma interest. Nat Biotechnol 2017; 35(7): 604–605.

21.

Ivanenkov

Polykovskiy

Bezrukov

, et al. Chemistry42: an AI-driven platform for molecular design and optimization. J Chem Inf Model 2023; 63(3): 695–701.

22.

M S Chattu

. A review of artificial intelligence, big data, and blockchain technology applications in medicine and global health. Big Data Cogn Comput 2021; 5(3): 41.

23.

Ajayan

Sreejith

Manikandan

, et al. An intensive study on organic thin film transistors (OTFTs) for future flexible/wearable electronics applications. Micro Nanostructures 2024; 187: 22.

24.

Atoche-Ensenat

Perez

Hernandez-Benitez

, et al. A smart tactile sensing system based on carbon nanotube/polypropylene composites for wearable applications. IEEE Sens J 2023; 23(3): 2948–2955.

25.

Chen

, et al. Knitted Ti(3)C(2)T(x) MXene based fiber strain sensor for human-computer interaction. J Colloid Interface Sci 2021; 604: 643–649.

26.

Yin

, et al. A biodegradable wearable flexible sensor based on natural wheat flour polymer for human motion and sweat monitoring. Ceram Int 2023; 49(13): 22062–22067.

27.

Ali

Xudong

Xiaoqing

, et al. A review of electro-stimulated gels and their applications: present state and future perspectives. Mater Sci Eng C Mater Biol Appl 2019; 103: 109852.

28.

Benight

Wang

Tok

JBH

, et al. Stretchable and self-healing polymers and devices for electronic skin. Prog Polym Sci 2013; 38(12): 1961–1977. Review.

29.

Chen

Liu

, et al. Silk fibroin-based flexible pressure sensors: processing and application. Mater Futures 2024; 3(3): 032501.

30.

Babatain

Gumus

Wicaksono

, et al. Expandable polymer assisted wearable personalized medicinal platform. Adv Mater Technol 2020; 5(10): 12.

31.

de Souza Barbosa

Biopolymers-based skin-interfaced triboelectric sensors. Nano Res 2023; 16(9): 11753–11782. Review.

32.

Wang

Liang

Wang

, et al. Emerging bioprinting for wound healing. Adv Mater. Epub ahead of print 11 August 2023. DOI: 10.1002/adma.202304738.

33.

Liu

Huang

Liu

, et al. Reconfiguring hydrogel assemblies using a photocontrolled metallopolymer adhesive for multiple customized functions. Nat Chem 2024; 16(6): 1024–1033.

34.

Wang

Zhao

, et al. Multi-functional and multi-responsive layered double hydroxide-reinforced polyacrylic acid composite hydrogels as ionic skin sensors. Adv Compos Hybrid Mater 2023; 6(2): 10.

35.

Yao

Wang

, et al. Mechanical energy harvesting and specific potential distribution of a flexible piezoelectric nanogenerator based on 2-D BaTiO3-oriented polycrystals. ACS Sustain Chem Eng 2022; 10(10): 3276–3287.

36.

Tang

Zhuang

, et al. Nitric oxide releasing polyvinyl alcohol and sodium alginate hydrogels as antibacterial and conductive strain sensors. Int J Biol Macromol 2023; 241: 124564.

37.

Liu

Liao

, et al. Fabrication of carboxymethyl cellulose and graphene oxide bio-nanocomposites for flexible nonvolatile resistive switching memory devices. Carbohydr Polym 2019; 214: 213–220.

38.

Oladapo

Kayode

Akinyoola

, et al. Shape memory polymer review for flexible artificial intelligence materials of biomedical. Mater Chem Phys 2023; 293: 12. Review.

39.

Zhang

Cai

, et al. Rapid water-responsive shape memory films for smart resistive bending sensors. Nano Today 2021; 38: 8.

40.

Ghosh

Mandal

Envisioned strategy for an early intervention in virus-suspected patients through non-invasive piezo- and pyro-electric-based wearable sensors. J Mater Chem A 2021; 9(4): 1887–1909.

41.

Sathiyanathan

Saatchi

Boo

, et al. Nitrogen and sulfur rich covalent organic framework-chitosan triboelectric nanogenerator for disposable urination evaluation tool. Adv Mater Technol 2024; 9(4).

42.

Arora

Dua

Singh

, et al. A comprehensive review on fillers and mechanical properties of 3D printed polymer composites. Mater Today Commun 2024; 40: 29.

43.

Elbadawi

Gustaffson

Gaisford

, et al. 3D printing tablets: predicting printability and drug dissolution from rheological data. Int J Pharm 2020; 590: 119868.

44.

Tsegay

Ghannam

Daniel

, et al. 3D printing smart eyeglass frames: a review. ACS Appl Eng Mater 2023; 1(4): 1142–1163.

45.

Prathumrat

Nikzad

Hajizadeh

, et al. Shape memory elastomers: a review of synthesis, design, advanced manufacturing, and emerging applications. Polym Adv Technol 2022; 33(6): 1782–1808. Review.

46.

Jaiswal

Vishwakarma

Bhatt

, et al. Layered titanium carbide-mediated fast shape switching and excellent thermal and electrical transport in shape-memory-polymer composites for smart technologies: MAX versus MXene. Adv Eng Mater 2023; 25(17): 12.

47.

Ikram

Al Rashid

Koç

Additive manufacturing of smart polymeric composites: literature review and future perspectives. Polym Compos 2022; 43(9): 6355–6380. Review.

48.

Sharma

KS.

Artificial intelligence assisted fabrication of 3D, 4D and 5D printed formulations or devices for drug delivery. Curr Drug Deliv 2023; 20(6): 752–769.

49.

Faruque

Lee

Wyckoff

, et al. Application of 4D printing and AI to cardiovascular devices. J Drug Deliv Sci Tech 2023; 80: 9.

50.

Chen

Shen

Iyer

, et al. Machine-learning-assisted de novo design of organic molecules and polymers: opportunities and challenges. Polymers 2020; 12(1): 45.

51.

Folorunso

Olukanmi

Shongwe

, et al. Development of series-parallel and neural-network based models for predicting electrical conductivity of polymer nanocomposite. IEEE Access 2023; 11: 92875–92886.

52.

Upadhya

Kosuri

Tamasi

, et al. Automation and data-driven design of polymer therapeutics. Adv Drug Deliv Rev 2021; 171: 1–28.

53.

Mehrotra

Soares

JBP

Nandakumar

, et al. A perspective on The Canadian Journal of Chemical Engineering commemorating its 100th volume: 1929–2021. Can J Chem Eng 2022; 100(9): 1983–2010.

54.

Miao

Ding

Kashem

MNH

, et al. A microfluidic platform for automatic continuous flow layer-by-layer assembly of polymer multilayered nanofilms. J Polym Sci 2024; 62(17): 4015–4024.

55.

Xie

Perspectives on development of biomedical polymer materials in artificial intelligence age. J Biomater Appl 2023; 37(8): 1355–1375. Review 20230111.

56.

Dananjaya

Marimuthu

Yang

, et al. Synthesis, properties, applications, 3D printing and machine learning of graphene quantum dots in polymer nanocomposites. Prog Mater Sci 2024; 144: 68. Review.

57.

Bai

Liu

Yang

, et al. Application and research prospect of functional polymer gels in oil and gas drilling and development engineering. Gels 2023; 9(5): 23.

58.

Basivi

Hamieh

Kakani

, et al. Exploring advanced materials: harnessing the synergy of inverse gas chromatography and artificial vision intelligence. Trends Analyt Chem 2024; 173: 25.

59.

Wang

Sadowski

Strategy of coupling artificial intelligence with thermodynamic mechanism for predicting complex polymer viscosities. ACS Sustain Chem Eng 2024; 12(11): 4631–4643.

60.

Adetunji

Erasmus

Green synthesis of bioplastics from microalgae: a state-of-the-art review. Polymers 2024; 16(10): 25.

61.

Sha

Tang

, et al. Machine learning in polymer informatics. InfoMat 2021; 3(4): 353–361.

62.

Salvante

Popoiu

Barb

, et al. Artificial intelligence (AI) based analysis of in vivo polymers and collagen scaffolds inducing vascularization. In Vivo 2024; 38(5): 620–629.

63.

Naranjo

Paulo-Mirasol

Lanzalaco

, et al. Exploring the effects and interactions of conducting polymers in the volume phase transition of thermosensitive conducting hydrogels. Chem Mat 2024; 36(9): 4688–4702.

64.

Mogale

Schutte-Smith

Erasmus

, et al. Toward sustainable menstrual health management: focus on super absorbent polymers. J Mater Sci 2024; 59(15): 6138–6168. Review.

65.

Kumar

Sarkar

Acherjee

, et al. Beam wobbling effects on laser transmission welding of dissimilar polymers: experiments, modeling, and process optimization. Opt Laser Technol 2022; 146: 16.

66.

Saber

WIA

Al-Askar

Ghoneem

KM.

Exclusive biosynthesis of pullulan using Taguchi’s approach and decision tree learning algorithm by a novel endophytic aureobasidium pullulans strain. Polymers 2023; 15(6): 23.

67.

Tian

Yue

Liu

, et al. Complete degradation of a conjugated polymer into green upcycling products by sunlight in air. J Am Chem Soc 2021; 143(27): 10054–10058. Article 20210628.

68.

Liu

Chen

Dong

, et al. Biomedical applications of hemicellulose-based hydrogels. Curr Med Chem 2020; 27(28): 4647–4659.

69.

Chiriac

Rusu

Nita

, et al. Polymeric carriers designed for encapsulation of essential oils with biological activity. Pharmaceutics 2021; 13(5): 31.

70.

Tomita

Ishihara

Kurita

A polymer-based chemical tongue for the non-invasive monitoring of osteogenic stem-cell differentiation by pattern recognition of serum-supplemented spent media. J Mater Chem B 2022; 10(37): 7581–7590. Article 20220928.

71.

Qin

Luo

Feng

, et al. A novel eco- and user-friendly graphene/leather-based composite for real-time mechano-monitoring of human motion. J Clean Prod 2022; 371: 13.

72.

Tavasli

Atila

Faraji

, et al. Organic phototransistors and their applications as artificial synapses. Sens Actuator A-Phys 2024; 371: 18.

73.

Sun

Zhou

Guo

, et al. Biomemristors as the next generation bioelectronics. Nano Energy 2020; 75: 20.

74.

Ziai

Zargarian

Rinoldi

, et al. Conducting polymer-based nanostructured materials for brain-machine interfaces. Wiley Interdiscip Rev Nanomed Nanobiotechnol 2023; 15(5): e1895.

75.

Hou

Zhang

Liu

, et al. Boosting flexible electronics with integration of two-dimensional materials. InfoMat 2024; 6(7): 50.

76.

Kurt

Krauhausen

Spolaor

, et al. Predicting blood glucose levels with organic neuromorphic micro-networks. Adv Sci 2024; 11(27): e2308261.

77.

Zhang

Liu

, et al. Highly adhesive and stretchable polymers for the interface of cyber-human interaction. Chem J Chin Univ-Chin 2021; 42(1): 1093–1113.

78.

Fan

Feng

Gao

, et al. PEDOT-ZnO nanoparticle hybrid film-based memristors for synapse emulation in neuromorphic computing applications. ACS Appl Nano Mater 2024; 7(5): 5661–5668.

79.

Ranade

Gautam

Chae

KH.

Magnetism in carbon-based fiber materials. J Magn Mater 2023; 586: 11.

80.

Gentili

PL.

Small steps towards the development of chemical artificial intelligent systems. RSC Adv 2013; 3(48): 25523–25549. Review.

81.

Chen

Advanced electron microscopy of nanophased synthetic polymers and soft complexes for energy and medicine applications. Nanomater 2021; 11(9): 25.