Machine learning-enabled formulation development of implantable drug delivery systems for precision medicine: from ocular and cochlear implants to personalized therapeutics

1 Introduction

1.1 Overview of implantable drug delivery systems (IDDS)

Drugs were often taken orally in the past, either as liquids or as powders. New dosage forms containing the medicine or drugs were produced in order to prevent issues with the oral route of drug delivery. Delivery methods that could sustain a consistent medication release to the precise region of action became necessary as time went on. Drug delivery systems were therefore created to maximize the therapeutic qualities of pharmaceutical products and make them more dependable, safe, and successful. One such system that can be used therapeutically is an implantable drug delivery system (IDDS). IDDS fall into three main categories: implantable pump systems, the newest atypical class of implants, and degradable or non-degradable implants. Implants, both degradable and nonbiodegradable, can be found as reservoir or monolithic systems. The drug load, the drug’s solubility and diffusion coefficient in the polymer, and the substance’s rate of in vivo degradation, particularly in the case of biodegradable systems, all influence the release kinetics of pharmaceuticals from such systems. The microtechnology of electronic systems and remote-controlled flow rate manipulation through the maintenance of a constant pressure difference are typically used to provide regulated drug release from the embedded pump. Another unusual type comprises newly developed devices such as transurethral implants used to cure impotence, intraocular lens implants used to treat glaucoma, and ceramic hydroxyapatite antibacterial systems used to treat bone infections. Targeted local medication administration at a steady pace, fewer pharmaceuticals needed to treat the disease state, reduced potential adverse effects, and enhanced therapy efficacy are some of these methods’ main benefits (Stewart et al., 2018).

1.2 Need for precision medicine in drug delivery

A revolutionary change in healthcare, precision medicine seeks to customize medical procedures to each patient’s unique traits, enabling more effective and individualized treatments. Precision medicine represents a shift in perspective towards a more proactive, knowledgeable, and patient-centered healthcare system, and its importance in the development of healthcare and treatment goes beyond personalized care. Precision medicine, which offers a level of accuracy and efficacy that was previously thought to be the stuff of utopian imaginations, will surely play an increasingly important role in determining the next-generation of healthcare as our understanding of genetics increases and technology continues to advance. In the modern era, healthcare is more than just curing illnesses; it is also about comprehending and maximizing each person’s distinct genetic makeup for a future that is healthier and more resilient (Ghazizadeh et al., 2024). In contrast with conventional medicine, which frequently uses broad therapies, precision medicine customizes healthcare approaches to each patient’s particular genetic, environmental, and lifestyle traits. The classification and key features of implantable drug delivery systems are summarized in Table 1. This customized strategy has the potential to completely reinvent the healthcare experience in addition to changing the landscape of diagnosis and treatment. Healthcare professionals can detect possible health issues far earlier than symptoms appear because of genetic insights. A proactive approach to healthcare is made possible by this prompt detection, which enables the implementation of measures to either avoid the emergence of diseases or manage them at their earliest stages. Oncology, cardiology, neurology, ophthalmology, and otology are just a few of the medical fields where machine learning is being used more and more. ML helps physicians with diagnosis, prognosis, treatment optimization, and individualized therapeutic decision-making by evaluating intricate biomedical datasets. The development of implantable drug delivery systems (IDDSs) has also been expedited by these developments (Liu et al., 2023; Farina, 2023).

TABLE 1. Classification and key features of implantable drug delivery systems (Tian et al., 2022; He et al., 2024).

1.3 Role of machine learning in formulation development

The use of machine learning (ML), a subfield of artificial intelligence, to speed up advancements in the pharmaceutical sciences has attracted a lot of attention in the past few years. The creation of systems for data analysis and statistical pattern or relationship identification is known as machine learning (ML). These connections can be used to identify fundamental frameworks or traits in complicated datasets, generate insights for decision-making, and forecast fresh data. Applying optimization strategies to modify the method’s parameters and increase its accuracy is part of the process of learning from the data. Pharmaceutical preparation is essential to the creation of stable, safe, and effective medications. Pharmacological researchers can increase the safety and/or efficacy of medicinal treatments by optimizing medication formulations. Therapeutic success or failure may depend on these advancements. For example, it has been projected that between 2010 and 2017, up to 90% of novel treatments failed clinical trials because of inadequate drug-like qualities, intolerable toxicological effects, or low efficacy. Improved medication compositions, however, can increase pharmacological activity, decrease toxicity, and increase potency (Bao et al., 2023). By facilitating data-driven decision-making, automation, and predictive modeling, artificial intelligence is becoming a disruptive force in the pharmaceutical industry. By assisting with disease diagnosis, treatment optimization, biomaterial selection, and customized medication delivery, machine learning has shown tremendous promise in a variety of medical specializations. Its capacity to evaluate intricate biomedical data has speed up the creation of intelligent therapeutic systems in otology and ophthalmology, allowing for more individualized and accurate treatment plans. AI offers a substantial opportunity to overcome complicated morphological and physiologic hurdles that impede therapeutic efficacy in ocular medication administration. Its significance in improving eye care is highlighted by its capacity to create intelligent systems for delivery, optimize drug regimens, and customize treatments. In a similar vein, machine learning has become more significant in otology, especially in the creation of cochlear implants. The overall efficacy of cochlear implant therapy is increased by ML algorithms, which help anticipate hearing outcomes, optimize electrode programming, improve speech recognition, and facilitate customized auditory rehabilitation (Srivastava et al., 2026).

1.4 Scope and objectives of the study

This review’s goal is to provide an overview of current developments in machine learning-enabled formulation creation for implantable medication delivery systems, with a focus on cochlear and ocular implants. Biomaterials, formulation techniques, predictive modeling, tailored treatments, smart implant technologies, regulatory issues, present difficulties, and prospects for precision medicine are all covered in this review.

By enhancing disease detection, medical imaging, medication development, biomaterial selection, and individualized treatment planning, machine learning has demonstrated impressive advancements in healthcare. Ocular and cochlear implants are two therapeutically significant systems among many implantable medical devices where accurate medication delivery and patient-specific optimization are crucial. In order to demonstrate the potential of machine learning-assisted formulation creation, these implants have been chosen as exemplary examples in this research.

Machine learning (ML) provides an intriguing solution to the many issues surrounding cochlear and ocular implants and aids in the delivery of precise and optimal medication for long-term problems. Unlike depending on predetermined hypotheses, machine learning algorithms may evaluate vast datasets, find common trends, and generate information-driven recommendations.

For instance, ML can deal with a variety of data sources and analyze complex correlations, offering a more thorough knowledge of the factors influencing cochlear implant (CI) results than typical general linear models (GLMs) (Ngiam and Khor, 2019; Azzolina et al., 2019; Verlato et al., 2015; Kim et al., 2018). While specific variables might have distinct impacts, they have coherent effects when combined, and machine learning can be used to holistically predict events from several variables. ML has the ability to predict speech perception results in CI recipients, according to recent investigations. Numerous methods for machine learning, such as random forests, standard decision trees, standard support vector machines, and neural networks, have been used in studies to assess client data as well as discover significant predictive variables (Tan et al., 2015; Shafieibavani et al., 2021).

Additionally, ML can greatly enhance CI development. Massive data sets from prior CI users can be analyzed by ML systems to find patterns and connections that conventional techniques can overlook. This renders it possible to comprehend coding variables that produce the best results for various standardized patient profiles.

Effective clinical imaging mapping, particularly for newborns, is made possible by standard prediction, which enables physicians to simulate individual reactions to multiple conditions based on parameters including age and the kind of hearing loss. Furthermore, ML may automatically modify CI parameters depending on immediate input, thereby resulting in the best possible programming of CI devices. These models can keep getting better as more data is gathered, possibly improving speech-in-noise recognition, which is still a major difficulty for many CI listeners (Nair et al., 2025).

2 Implantable drug delivery systems

2.1 Types of implantable drug delivery systems

2.1.1 Nonbiodegradable systems

Nondegradable matrix-based systems and reservoir-type systems are the two most popular types of nondegradable implantable drug delivery systems (IDDSs) now on the marketplace. The medicine is uniformly distributed throughout the matrix material in a matrix of polymer systems, also known as a monolithic system. The medicine is released from the distribution system gradually as it diffuses through the polymeric matrix material. In contrast, the reservoir-type device has a compacted drug core encircled by a permeable, non-biodegradable membrane whose thickness and permeability characteristics can regulate the medication’s diffusion throughout the body.

2.1.2 Biodegradable systems

Compared to nondegradable methods of delivery, biodegradable systems have become significantly more widely used. One of the main benefits of biodegradable systems is that the body gradually absorbs or eliminates the inert polymer substances used to make the delivery system. This increases client compliance and acceptance by reducing the need for surgical implant removal following therapy. But creating biodegradable systems is more difficult than creating nondegradable ones. Numerous factors need to be taken into account when creating new biodegradable systems. For example, the polymer’s in vivo kinetics must continue at a steady pace in order to maintain a continuous release of the medication (Li et al., 2020).

2.1.3 Implantable pump system

Delivery volume and pace must be externally controlled for a variety of medications. Except for magnetic delivery systems, neither biodegradable nor nondegradable delivery technologies provide for this kind of control. The control required in these circumstances has been provided via pump systems (Stewart et al., 2018).

2.1.3.1 Infusion pumps

Infusion pumps are implantable mechanical devices that provide medication in vivo using a fluorocarbon propellant. These pumps were first created to give insulin to people with diabetes. One of the earliest pumps for this purpose to be sold commercially was the infusion pump. Insulin-dependent diabetics typically need injections once or twice a day. Blood glucose levels exhibit unusual peaks and valleys as a result of this kind of dosage. Such inadequate blood glucose management is thought to increase the risk of diabetic complications like heart and renal disease.

2.1.3.2 Osmotic pumps

The most often used kind of implantable medication delivery systems is osmotic pumps. The osmotic pump, sometimes referred to as the gastrointestinal therapy system or Oros, is made out of a drug reservoir encased in a semipermeable membrane. Through the mechanism of osmosis, the surrounding membrane permits a constant flow of water and biological fluid into the reservoir. The medication is steadily released from an opening in the membrane known as the drug portal due to the hydrostatic pressure build-up caused by this influx. Until all of the drug in the reservoir is gone, the rate of drug release is either constant or zero-order.

2.1.3.3 Controlled-release micropumps

While a quickly swinging piston acting on a foldable disc of foam enhances the rate of drug administration, controlled-release pumping mechanisms have been created that use diffusion across a rate-controlling membrane to regulate basal delivery. An external source of electricity is not necessary for this kind of pump setup. The concentration differential at the drug reservoir and the site of action provides the necessary power to operate this pump. The medicine diffuses or is delivered basally as a result of this level differential. A current is passed through the solenoid coil to improve the drug’s distribution. Through a steel piston, this current compresses the foam disk (Vora et al., 2023).

2.1.3.4 Intraocular pumps

Because controlled-release polymer systems are simple to implant and remove, the eye is a perfect location for their usage. Glaucoma is one of the most common causes of blindness worldwide. Ocular ointments and gels or eye drops applied frequently throughout the day are the primary treatments for this disease state. These treatments take a lot of time, and contact with the medication delivery vehicle may result in a secondary infection in the eye. Ocusert, the first glaucoma ocular implant device utilized in clinical settings, was created by ALZA Corporation. An ethylene-vinyl acetate copolymer serves as a slow obstacle for the pilocarpine reservoir in the Ocusert device. The gadget degrades over the course of 1 week after being placed beneath the lower eyelid’s tarsus using a specialized tool. Two distinct systems have been created. One releases 20 mg per hour, whereas the other releases 40 mg per hour. When it comes to treating glaucoma, intraocular implants have demonstrated significant benefits over traditional topical medication (Tian et al., 2022; Seyedhamzeh et al., 2025).

2.2 Materials used in implant formulation

Because it directly affects biocompatibility, mechanical strength, degradation behavior, drug loading capacity, and drug release kinetics, material selection is one of the most important factors in the creation of implantable drug delivery systems (IDDSs). Biocompatibility, non-toxicity, mechanical stability, and the ability to deliver consistent and sustained medication release over the intended treatment duration are all desirable characteristics of an ideal implant material. Biodegradable biomaterials, non-biodegradable biomaterials, and newly created functional or smart biomaterials that react to internal or external stimuli for regulated drug administration are the three main categories of implant biomaterials (Li et al., 2020; Ratner et al., 2020) Implant applications have been further increased by new hydrogel-based smart biomaterials. While MnO₄-potentiated bifunctional hydrogels have shown simultaneous tumor ablation and bone regeneration, spermidine-functionalized Janus hydrogel microneedles have shown inhibition of ferroptosis and accelerated healing of oral ulcers, underscoring the increasing potential of multifunctional biomaterials in precision medicine (Li et al., 2023; Costello et al., 2023; Li and Mooney, 2016).

2.2.1 Biodegradable biomaterials

Because they progressively break down into non-toxic metabolites and do not require surgical removal upon medication depletion, biodegradable polymers are frequently used in implant formulations. The most studied synthetic biodegradable polymers are poly (lactic acid) (PLA), poly (glycolic acid) (PGA), and their copolymer poly (lactic-co-glycolic acid) (PLGA). The Krebs cycle is used to metabolize the lactic and glycolic acids that are produced when these polymers hydrolyze. By altering the polymer molecular weight, copolymer ratio, crystallinity, and implant geometry, their breakdown rate and drug release profile can be customized. Another biodegradable polymer with superior mechanical flexibility and slower breakdown is polycaprolactone (PCL), which makes it appropriate for long-term implants like orthopedic and ophthalmic medication delivery systems. Due to their exceptional biocompatibility, bioactivity, and capacity to promote tissue regeneration, natural polymers such as chitosan, alginate, gelatin, collagen, hyaluronic acid, and silk fibroin have also drawn a lot of interest. But compared to synthetic polymers, natural polymers frequently show less mechanical strength and more batch-to-batch variability (Makadia and Siegel, 2011; Danhier et al., 2012; Dash and Konkimalla, 2012; Ulery et al., 2011; Asghari et al., 2017).

2.2.2 Nonbiodegradable

Non-biodegradable polymers are chosen for uses where a protracted, tightly regulated medication release over several months or years is necessary. Commercially implanted devices frequently use silicone elastomers, polyurethane, polyethylene, polyvinyl alcohol (PVA), and ethylene-vinyl acetate (EVA). Excellent chemical stability, predictable diffusion-controlled drug release, and structural integrity are all provided by these materials. For instance, EVA has been employed in ocular implants like Vitrasert® and Retisert® for prolonged intraocular drug delivery, whereas silicone elastomers are frequently used in contraceptive implants due to their flexibility and long-term durability. After full drug release, surgical removal may be required because these polymers do not break down within the body, which is a significant drawback (Ulery et al., 2011; Li et al., 2023). Composite and Inorganic Materials. To enhance mechanical characteristics and tissue integration, inorganic biomaterials like titanium, titanium alloys, stainless steel, ceramics, hydroxyapatite, and bioactive glass are commonly included in implant formulations. Because of its exceptional osseointegration, mechanical strength, and resistance to corrosion, titanium-based materials are frequently utilized in cochlear, dental, and orthopedic implants. Better mechanical performance, increased drug loading efficiency, and more accurate control over release kinetics are all provided by composite biomaterials that combine polymers with ceramics, nanoparticles, or bioactive fillers. Implants made of nanocomposite materials that incorporate hydroxyapatite, calcium phosphate, graphene oxide, or mesoporous silica nanoparticles have shown improved therapeutic effectiveness in localized drug delivery and bone regeneration (Stewart et al., 2018; Ratner et al., 2020; Ratner et al., 1996).

2.2.3 Functional biomaterials for smart implants

The creation of stimuli-responsive or “smart” implantable systems that can release medications in response to physiological or external stimuli like pH, temperature, enzymes, magnetic fields, ultrasound, or electrical signals has been made possible by recent developments in biomaterials. Intelligent implant formulations are increasingly using hydrogels, shape-memory polymers, conductive polymers, and nanostructured biomaterials. Because they simultaneously increase electrical conductivity and allow for regulated drug release, conductive polymers like polypyrrole and poly (3,4-ethylenedioxythiophene) (PEDOT) have drawn a lot of attention for neural interfaces and cochlear implants. These multipurpose materials promote the shift to personalized and precision medicine by making integration with biosensors and wearable monitoring devices easier (Li et al., 2023; Green et al., 2008).

2.3 Drug release mechanisms

The complicated mechanics of regulated drug release from Poly (lactic-co-glycolic acid) (PLGA)-based dosage formulations have attracted a lot of attention lately. Hydrolytic degradation of the polymer, water uptake, osmotic effects, pH gradients within the dosage form, drug-polymer interactions (or lack thereof), drug solubility, internal pore closure, system swelling, and erosion of the dosage form (Li et al., 2023) are among the physicochemical processes and characteristics that have been found to play significant roles in the drug release process. Drug release from each distinct PLGA-based formulation is ultimately determined by a unique combination of these elements; the relative contribution of these phenomena to drug release may vary throughout the course of the sustained release period (Makadia and Siegel, 2011; Costello et al., 2023; Visan and Negut, 2024).

2.3.1 Diffusion-controlled drug release

One of the most prevalent mechanisms in implantable drug delivery systems, especially in non-biodegradable implants, is diffusion-controlled release. Following implantation, the drug molecules are dissolved by biological fluids that enter the device and diffuse into the surrounding tissue through the polymer matrix or a rate-controlling membrane. Drug transport is propelled by the concentration gradient between the implant and the surrounding environment. Polymer permeability, drug solubility, molecular weight, implant geometry, and membrane thickness are some of the variables that affect drug release. While matrix-type implants usually follow Higuchi diffusion kinetics, reservoir-type implants typically provide near-zero-order release. Diffusion controlled drug release is illustrated in Figure 1.

2.3.2 Degradation-controlled drug release

Drug release in degradation-controlled systems is controlled by the slow disintegration of biodegradable polymers like PCL, PLA, and PLGA. Water seeps into the implant, causing polymer chains to hydrolyze and lowering the matrix’s molecular weight. Pores form as degradation advances, allowing the encapsulated medication to diffuse until the polymer is fully resorbed. Polymer composition, crystallinity, molecular weight, implant dimensions, and the local physiological environment, which includes pH and enzymatic activity, all affect the rate of degradation. These systems are common in long-acting implants and do not require surgical removal following therapy. Components and Placements of a Cochlear Implant Figure 2.

2.3.3 Swelling-controlled drug release

Hydrogel-based and other hydrophilic polymer implants exhibit swelling-controlled drug release. Following implantation, the polymer expands and absorbs physiological fluids, increasing the polymer network’s free volume. Drug molecules from the implant can more easily diffuse into the surrounding tissues thanks to this swelling. The degree of swelling, drug diffusivity, water uptake, and polymer cross-linking density all affect the release rate. Swelling and polymer degradation can happen concurrently in biodegradable hydrogels, creating a combined release mechanism that offers long-term therapeutic concentrations. Swelling-controlled drug release is illustrated in Figure 3.

2.3.4 Osmotically controlled drug release

A semipermeable membrane used in osmotically controlled implants allows water to pass through while stopping medication leakage. Osmotic pressure is created inside the device by water influx, which forces the dissolved medication through a carefully designed delivery orifice at a regulated pace. These systems can achieve nearly constant zero-order drug delivery for extended periods of time because osmotic pressure, rather than diffusion, governs drug release. The physicochemical characteristics of the formulation, osmotic agent concentration, device design, and membrane permeability all affect drug release (Stewart et al., 2018; Vora et al., 2023). Osmotically controlled drug release is illustrated in Figure 4.

3 Machine learning in formulation development

3.1 Fundamentals of machine learning

In general, machine learning refers to computational techniques that leverage experience to enhance performance or generate precise forecasts. Right here. “Experience” is the term used to describe the prior knowledge that the learner has access to, usually in the form of electronic data that has been gathered and made available for analysis. Digitized human-labeled training sets or other kinds of information gathered through interaction with the environment could be examples of this data. Creating effective and precise prediction algorithms is the essence of machine learning. These algorithms’ time and space complexity are important indicators of their quality, just like in other branches of computer science. However, in order to assess the sample size needed for the algorithm to learn a family of concepts, machine learning will also require an understanding of sample complexity. In general, the size of the training sample and the complexity of the concept classes taken into consideration determine an algorithm’s theoretical learning guarantees (Bao et al., 2023; Mohri et al., 2018).

3.2 Data-driven formulation design

From improvements in cancer diagnosis to the identification of novel pharmaceuticals and pharmacological targets, machine learning (ML) has made it possible to make ground-breaking advancements in the pharmaceutical and healthcare industries. Rug formulation is a crucial step in the search and development of new medications. Pharmaceutical experts can develop key characteristics of novel medications, such as enhanced bioavailability and tailored administration, by designing drug formulations. Iterative trial-and-error is the foundation of the conventional approach to drug formulation development, necessitating numerous resource-intensive and time-consuming in vitro and in vivo trials. The fundamental ideas of ML-directed workflows are presented in this paper, along with how these tools can be applied to support the creation of different kinds of medication formulations. Unmatched potential to accelerate development efforts, find novel materials and formulations, and provide new information in drug formulation research is provided by machine learning-directed drug formulation development. It also showcases the newest advances in artificial intelligence, such as generative models, which have been gaining traction in chemistry and drug discovery and may be useful in the development of therapeutic formulations (Vora et al., 2023; Bannigan et al., 2021; Kassa et al., 2025).

3.3 Predictive modeling and optimization

It uses mathematical programming and deep learning to create a predictive, selective maintenance system. We examine a multi-component system that carries out successive production missions with planned intermission maintenance intervals. At a certain expense, several maintenance procedures can extend the useful life of each component during the intermission breaks. To determine a subset of maintenance tasks to carry out on the components, an optimization model is created. The goal is to reduce overall expenses while adhering to the intermission break time restriction. Maintenance and failure costs make up the total cost, which is determined by the likelihood that the succeeding missions will be successful. The optimization model communicates with a lengthy short-term memory network in order to evaluate these probabilities (Hesabi et al., 2022; Kassa et al., 2025).

3.4 Regulatory and ethical considerations

Prior to clinical translation, machine learning (ML)-enabled implanted medication delivery devices must adhere to strict ethical and regulatory standards. Regulatory bodies like the European Medicines Agency (EMA) and the U.S. Food and Drug Administration (FDA) need dependable performance across a range of patient demographics, transparent machine learning algorithms, and strong clinical validation. To reduce bias and enhance model generalizability, training data quality and source should be well recorded. The proper validation metrics, including as accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC), should be used to assess the model’s performance. Before being used in clinical settings, implant formulations should also go through extensive in vitro drug-release research, biocompatibility assessment, mechanical testing, and long-term stability evaluation. The safety, dependability, and clinical acceptability of ML-driven implantable drug delivery devices will be improved by ensuring explainable AI, consistent validation procedures, and adherence to legal requirements.

3.5 Data limitations and challenges

The quality and variety of the available datasets have a significant impact on how well machine learning (ML) models work in implanted medication delivery systems. However, the accuracy and generalizability of prediction models may be diminished by the limitations of current datasets, which frequently include small sample sizes, class imbalance, inadequate clinical information, and inconsistency in experimental techniques. Furthermore, selection bias may be introduced and the applicability of ML models to wider clinical contexts may be limited by datasets gathered from a single institution or particular patient population. Model building is made more difficult by variations in biomaterial characteristics, drug-release testing techniques, and patient-specific factors that further enhance data heterogeneity. Therefore, to increase the dependability, robustness, and clinical translation of ML-enabled implantable drug delivery systems, it is crucial to generate standardized, high-quality, multicenter datasets in addition to rigorous data preprocessing and external validation (Singh et al., 2025).

3.6 Data sharing and privacy considerations

Access to diverse and high-quality datasets while maintaining patient privacy and data security is necessary for the effective application of machine learning (ML) in implanted medication delivery systems. Anonymized datasets can be shared among several research centers to increase the generalizability of predictive algorithms, decrease bias, and strengthen model resilience. Prior to data exchange, patient information should be de-identified in compliance with applicable ethical guidelines and data protection laws like the General Data Protection Regulation (GDPR). Furthermore, consistent data formats, safe data management procedures, and cooperative multicenter research can support the creation of trustworthy machine learning models while upholding privacy and legal compliance. These steps will expedite the clinical translation of ML-driven implantable drug delivery systems (IDDSs) and facilitate repeatable research.

3.7 Suggested reporting guidelines for research on machine learning

Machine learning applications in implantable drug delivery systems should adhere to standard reporting procedures to enhance repeatability, transparency, and study comparison. Crucial information like model construction, validation processes, dataset features, and performance assessment should be explained in depth. Researchers may evaluate model dependability, replicate results, and make clinical translation of ML-enabled implantable drug delivery systems (IDDSs) systems easier with standardized reporting (Voigt and Von dem Bussche, 2017). The recommended reporting items for machine learning studies in implantable drug delivery systems are summarized in Table 2.

TABLE 2. Recommended Reporting items for machine learning studies in implantable drug delivery systems (Voigt and Von dem Bussche, 2017).

3.8 Machine learning-driven formulation development workflow

Implantable medication delivery devices can be developed in a methodical and data-driven manner thanks to machine learning (ML). Physicochemical, biological, and clinical data about pharmacological properties, biomaterials, and patient characteristics are first gathered. Appropriate machine learning algorithms are taught and validated to forecast formulation performance, including drug release kinetics, biodegradation behavior, and biocompatibility, after data preprocessing and feature selection. Before moving on to clinical validation, the improved formulation is created and assessed by in vitro and in vivo investigations. This integrated approach facilitates the development of customized implantable drug delivery devices, speeds up formulation optimization, and lessens the burden of experiments. The workflow of ML-driven formulation development is illustrated in Figure 5.

3.9 Restrictions and prospects

Even while machine learning (ML)-assisted formulation development has made great strides, there are still a number of obstacles in the way of its successful clinical translation. The majority of machine learning models are created using small experimental or retrospective datasets, which might not accurately reflect the biological complexity and patient variability found in clinical settings. Because of differences in physiological circumstances, drug-release behavior, and biomaterial degradation, computational projections may not match actual in vitro, in vivo, and clinical outcomes. Model dependability and generalizability can also be diminished by problems including data heterogeneity, model overfitting, insufficient external validation, and a lack of standardized datasets (Mishra et al., 2026).

4 Ocular implants

4.1 Ocular drug delivery challenges

The intricate structure of the eye and its protective barriers, which restrict medication penetration and lower bioavailability, make ocular drug administration difficult. Due to their short residence duration and quick tear clearance, conventional formulations, like eye drops, frequently need to be administered, which results in low patient compliance. Implantable ocular medication delivery systems improve therapeutic efficacy by providing localized and prolonged drug release. By anticipating medication release, improving formulation and implant design, and facilitating the creation of customized implants using cutting-edge technologies like 3D printing, machine learning (ML) advances the development of these systems. Precision ocular therapies could benefit from combining machine learning with implantable technologies (Singh et al., 2025). By providing prolonged, site-specific, and patient-tailored drug release, recent developments in implantable drug delivery systems (IDDSs) have enhanced the accuracy and control of ocular therapy. By improving formulation design, forecasting drug release behavior, and assisting in the creation of customized implants, machine learning (ML) further improves these systems. ML-driven implantable systems have the potential to enhance therapeutic effects, decrease dose frequency, and expedite the integration of precision ocular care into clinical practice when paired with cutting-edge biomaterials and digital fabrication technologies (Voigt and Von dem Bussche, 2017). Long-term medication distribution is especially difficult in the eye since it is a highly specialized organ with intricate anatomical and physiological barriers. While the management of vision-threatening diseases has been greatly enhanced by implantable ocular devices, their clinical success depends on the selection of biocompatible materials with suitable mechanical and physicochemical properties to ensure safety, durability, and sustained therapeutic performance. By facilitating data-driven optimization of biomaterials, implant design, and drug release characteristics, machine learning is becoming a useful tool to address these issues and promote the creation of more efficient and customized ocular implant therapies for precision medicine (Mishra et al., 2026). The anatomy of the eye showing an ocular implant is illustrated in Figure 6.

4.2 Types of ocular implants

After eye removal surgeries like evisceration and enucleation, ocular implants are intended to restore orbital volume, enhance cosmetic appeal, and increase prosthesis movement. While reducing issues like implant migration and exposure, developments in biomaterials and nanotechnology have greatly enhanced implant biocompatibility, mechanical stability, and long-term clinical results. Ocular implants can be roughly categorized as either integrated (porous) or non-integrated (non-porous) based on how they interact with the surrounding tissues. Because each category has unique structural traits, benefits, and drawbacks, the choice of implant depends on the patient’s clinical condition, the surgical needs, and the intended functional and cosmetic results (Tian et al., 2022; Kassa et al., 2025; Chauhan et al., 2022).

4.2.1 Ocular devices

4.2.1.1 Orbital implants

After evisceration or enucleation, orbital implants are utilized to improve esthetic results and restore orbital volume.

Silicone and PMMA are examples of non-integrated (non-porous) implants that offer volume replacement with restricted fibrovascular ingrowth and motility.

Hydroxyapatite (HA), porous polyethylene, and porous alumina are examples of integrated (porous) implants that enable fibrovascular integration, enhancing stability and prosthesis mobility.

Hybrid designs that combine porous and non-porous materials for balanced performance are known as quasi-integrated implants.

CAD/CAM and 3D-printed customized implants for better anatomical fit and surgical accuracy are patient-specific implants.

Emerging smart/drug-eluting implants: ML and nanotechnology-assisted devices that allow customized orbital repair and regulated drug distribution.

4.2.1.2 Contact lenses

• Soft lenses: Materials made of hydrogel or silicone hydrogel are frequently utilized to correct vision.
• Rigid gas-permeable (RGP) lenses: Offer exceptional optical quality and assistance for corneal health.
• Drug-eluting lenses (emerging): Designed for sustained ocular drug delivery.
• Smart contact lenses: Under development for therapeutic and biosensing uses.

4.2.1.3 Keratoprostheses and intracorneal implants

• Boston Keratoprosthesis (KPro): For severe corneal disease, artificial corneal replacement is used.
• Osteo-odonto-keratoprosthesis (OOKP): Tooth-bone-supported corneal prosthesis.
• Intracorneal ring segments (ICRS): Used to enhance corneal shape in keratoconus.
• Corneal inlays: implanted to enhance refractive results, especially in cases of presbyopia.

4.2.1.4 Scleral buckles

• Silicone sponge buckles: Adaptable indentation tools for fixing retinal detachment.
• Solid silicone bands: During retinal surgery, they offer structural support.
• Hydrogel buckles (historical): used in the past but mostly stopped.

4.2.1.5 Glaucoma filtration implants (GFIs)

• Ahmed glaucoma valve: Drainage implant with flow control.
• Baerveldt implant: High-flow drainage device without valves.
• Molteno implant: One of the first devices for draining glaucoma.
• PRESERFLO microshunt: Instrument for minimally invasive glaucoma surgery (MIGS).

4.2.1.6 Intraocular lenses (IOLs)

• Monofocal IOLs: Standard lenses for cataract replacement.
• Multifocal IOLs: Give both close-up and far-off vision.
• Extended depth of focus (EDOF) IOLs: Expand your field of vision.
• Toric IOLs: Treat astigmatism in the cornea.
• Accommodating IOLs: Intended to resemble the focusing of a natural lens.
• Drug-eluting IOLs (emerging): Permit long-term intraocular medication administration.

4.2.1.7 Silicone oil

• Standard silicone oil: As a vitreoretinal tamponade.
• Heavy silicone oil: Used in cases of complicated retinal detachment.

4.2.1.8 Viscoelastic agents

• Cohesive ophthalmic viscosurgical devices (OVDs): Preserve the surgical area.
• Dispersive OVDs: Give protection and a tissue coating.
• Viscoadaptive OVDs: Integrate dispersive and cohesive qualities (Mishra et al., 2026; Kassa et al., 2025; Catalu et al., 2018; Hsueh et al., 2023).

4.3 Machine learning applications in ocular therapeutics

By assisting in the development and improvement of drug and gene delivery systems, machine learning (ML) is becoming a more significant factor in the advancement of ocular therapies. Achieving effective therapeutic administration is still difficult due to the intricate anatomical and physiological barriers of the eye. To forecast the effectiveness of drug or gene delivery, cellular absorption, and treatment response, machine learning algorithms can incorporate formulation features, ocular tissue characteristics, and patient-specific data. The ability of sophisticated models, like artificial neural networks, to recognize intricate nonlinear interactions facilitates the creation of individualized treatment plans, enhances formulation design, and speeds up the application of precision medicine in clinical ophthalmology (Seyedhamzeh et al., 2025). The design of continuous ocular medication delivery systems is becoming more and more supported by machine learning as it develops. Multifunctional peptide-drug conjugates that improve cellular uptake, bind to ocular melanin, and offer sustained drug release have been made possible by ML-guided peptide engineering. These methods can increase therapeutic efficacy and lessen the requirement for frequent dosing by using melanin as a natural drug reservoir. These intelligent design strategies are a promising step toward precision medicine in ophthalmology and individualized, long-acting ocular medicines (Chauhan et al., 2022). Machine learning-driven applications in ocular therapeutics are illustrated in Figure 7.

5 Cochlear implants

5.1 Drug-eluting cochlear implants

By recovering auditory function in people with severe to profound hearing impairment, cochlear implants (CIs) have revolutionized the treatment of sensorineural hearing loss. Implantation can cause inflammatory reactions that result in the creation of fibrous tissue and, in certain situations, the growth of new bone surrounding the electrode array, notwithstanding its therapeutic success. These tissue reactions could make revision or reimplantation procedures more difficult, lower stimulation efficiency, and raise electrode impedance. Drug-eluting cochlear implants have been created to address these issues by delivering therapeutic drugs, especially corticosteroids like dexamethasone, locally and continuously. These cutting-edge devices are a viable approach for next-generation cochlear implant therapy because they improve electrode performance, maintain cochlear function, and improve long-term clinical results by reducing inflammation and fibrosis at the implantation site (Catalu et al., 2018; Topol, 2019). The components and placement of a cochlear implant are illustrated in Figure 8.

5.2 Machine learning for cochlear implant optimization

Through improved signal processing, speech perception, and patient-specific device optimization, machine learning (ML) is increasingly improving the performance of cochlear implants (CI). By reducing background noise, separating overlapping sound sources, and enhancing automatic speech recognition, machine learning and deep learning models, let CI users perceive sounds more clearly in difficult listening situations. In addition to improving signals, machine learning (ML) facilitates customized cochlear implant programming by forecasting ideal stimulation levels, helping with electrode mapping, and modifying sound processing techniques according to unique auditory responses and hearing profiles. These data-driven strategies promote more accurate and flexible cochlear implant optimization for specific patients, increase speech comprehension, and improve rehabilitation outcomes (Aghajanpour et al., 2025; Esteva et al., 2019).

5.3 Personalized hearing therapies

Artificial intelligence (AI) and automatic speech recognition (ASR) technologies are increasingly supporting personalized hearing therapies to improve communication outcomes for people with hearing impairment, especially those who utilize cochlear implants (CI). Incoming analog voice inputs are transformed into electrical stimulation in CI systems using signal-processing algorithms; nonetheless, constraints like a limited number of electrodes can cause speech distortion, particularly in noisy or multi-speaker settings. To solve these problems, AI-based methods have been developed to enhance speech quality, reduce background noise, and improve signal clarity in real-world situations. Furthermore, machine learning models enable more accurate speech interpretation and adaptation of CI processing strategies, thereby enhancing auditory perception. All things considered, CI users benefit from more effective, tailored hearing rehabilitation and improved communication outcomes due to these astute, data-driven tactics (Hsueh et al., 2023).

Artificial intelligence (AI) and machine learning (ML) are used in personalized hearing therapies to improve auditory rehabilitation and results for people with hearing loss, especially those who utilize cochlear implants (CI). Early and efficient intervention is crucial since hearing impairment has a substantial impact on communication, cognitive development, and quality of life in all age groups. By avoiding damaged areas of the ear and directly stimulating the auditory nerve via an electrode array, cochlear implants offer a vital treatment for severe to profound hearing loss. However, performance may be limited, particularly in complicated auditory environments, by variations in patient anatomy, illness development, and environmental listening conditions. AI-driven methods are being employed more and more to support adaptive rehabilitation techniques, enhance signal clarity, and optimize speech processing in order to address these issues. Real-time speech perception improvement, noise reduction, and personalized implant setting modification based on user response and aural feedback are all made possible by contemporary ML-based systems. Additionally, new sophisticated solutions like cognitive hearing aids help consumers manage their hearing health. When taken as a whole, these data-driven and adaptive technologies are opening the door to fully customized hearing treatments that enhance CI users’ social engagement, speech comprehension, and general quality of life (Aghajanpour et al., 2025; Hsueh et al., 2023).

6 Personalized therapeutics

6.1 Patient-specific drug delivery strategies

The goal of personalized therapies is to provide care that is specific to each patient’s biological makeup. Via enabling targeted medication delivery in which therapeutic substances are delivered specifically to sick tissues via nanoscale carriers, nanomedicine has become a promising strategy that improves treatment efficacy while reducing systemic toxicity.

Liposomes, polymeric nanoparticles, dendrimers, and exosomes are examples of nanocarriers that offer better targeting, increased stability, and regulated drug release. Individualized therapy is supported by precise drug localization and on-demand release made possible by surface modification with particular ligands and stimuli-responsive designs. These developments are enhanced by machine learning (ML), which analyzes formulation and patient-specific data to optimize nanocarrier design, forecast medication release profiles, and customize treatment plans. ML can enhance formulation development and offer tailored therapeutic outcomes in implantable drug delivery devices, including ocular and cochlear implants (Dhanasingh and Hochmair, 2021; Kelly et al., 2019).

6.2 Smart implants and digital health integration

By fusing real-time health monitoring with customized medication delivery, smart implants are revolutionizing precision medicine. Implantable devices may now continually monitor physiological parameters while providing medicines in a controlled manner due to advancements in tiny sensors, biocompatible materials, wireless connectivity, and low-power electronics. Personalized illness management and remote patient monitoring are further supported by integration with wearable technology and digital health platforms.

By evaluating patient-specific data to optimize drug delivery, forecast illness development, and support adaptive treatment techniques, machine learning (ML) improves these systems. ML may enhance formulation performance and provide customized therapeutic outcomes in implantable drug delivery devices, including ocular and cochlear implants (Essaid et al., 2024).

In general, more proactive and individualized healthcare is being made possible by the combination of digital health technology and smart implants. These technologies can enhance medication distribution, enable prompt clinical decision-making, and continually monitor patient health when combined with machine learning. The development of next-generation implantable drug delivery systems is anticipated to be accelerated by continuous advancements in intelligent sensing, wireless communication, and AI-driven analytics, even though issues with biocompatibility, cybersecurity, data privacy, and regulatory approval still exist. Precision medicine for ocular, cochlear, and other implant-based therapeutic applications will be made safer, more flexible, and patient-centered because of these developments (Zhang et al., 2025).

6.3 Precision medicine applications

By combining genetic, clinical, physiological, and lifestyle data, precision medicine seeks to maximize treatment outcomes through customized medicines. Biomedical big data, which includes genomes, proteomics, metabolomics, electronic health records (EHRs), medical imaging, and sensor-generated data, has grown quickly, opening up new possibilities for early diagnosis, treatment monitoring, illness prediction, and personalized healthcare. Clinicians can better grasp patient-specific disease characteristics and create more effective treatment plans because of these varied datasets. In order to derive valuable insights from these massive and intricate datasets, machine learning (ML) is essential. Machine learning (ML) can forecast illness development, optimize medication release profiles, and assist individualized treatment decisions by integrating biological data with information from implantable drug delivery systems. ML-driven analysis enhances formulation design, therapeutic efficacy, and long-term patient outcomes in cochlear and ocular implants. Advanced data management technologies, such as cloud computing, big data platforms, interoperable health databases, and AI-enabled analytical tools, are also necessary for the successful application of precision medicine. By facilitating the safe integration and analysis of diverse healthcare data, these technologies speed up the adoption of personalized medicines into standard clinical practice and allow for real-time clinical decision-making. Precision medicine will benefit greatly from the combination of biomedical big data, machine learning, and intelligent implanted medication delivery systems as digital health infrastructure develops (Bao et al., 2023; Dey et al., 2024; Holzinger et al., 2020). The current challenges and future directions in implantable drug delivery systems are summarized in Table 3.

TABLE 3. Current challenges and future directions in implantable drug delivery systems (Andreu-Perez et al., 2015; Gaobotse et al., 2022; Yang et al., 2024; Zeng et al., 2008; Zakay et al., 2021).

7 Challenges and future perspectives

7.1 Technical and regulatory challenges

The difficult process of bringing implantable technologies from research to clinical use is exemplified by cochlear implants, which call for very close coordination between engineers and physicians as well as rigorous adherence to safety and regulations. Early research on electrical stimulation highlighted the need for accurate and dependable device design by revealing problems such as signal distortion, inconsistent responses, and safety concerns.

Implantable systems continue to struggle with long-term stability, biocompatibility, and reliable functioning despite clinical success. Additionally, regulatory approval is still stringent and necessitates thorough safety and efficacy evaluation. These difficulties are exacerbated by contemporary machine learning-enabled implantable drug delivery systems, necessitating a delicate balancing act between innovation, patient safety, and regulatory compliance (Andreu-Perez et al., 2015; Topol, 2019; Esteva et al., 2019; Rajpurkar et al., 2022).

7.2 Ethical considerations

The use of cochlear and ocular implants raises significant ethical issues, especially when it comes to children from linguistic and cultural minority groups like the Deaf community. Clinicians and parents frequently make decisions, and their viewpoints on language, identity, and disability may be different from those of the impacted populations (Blebea et al., 2022, Humphries et al., 2012, Kleinlogel et al., 2020). Ensuring fully informed consent and clearly defining parental responsibility while taking into account the long-term effects of implantation on identity formation, communication development, and cultural belonging are important issues, particularly in pediatric cases where caregivers make decisions on behalf of the child (Gaobotse et al., 2022; Yang et al., 2024). The deployment of these technologies necessitates a balanced strategy that integrates therapeutic effectiveness with respect for individual autonomy and cultural and linguistic norms, despite the fact that they offer significant functional and rehabilitative benefits (Yang et al., 2024; Rajpurkar et al., 2022).

7.3 Emerging trends and future directions

Quality of life is significantly affected by vision and hearing loss, and existing treatments such as cochlear/retinal implants, glasses, and hearing aids only provide partial restoration. More basic restoration of sensory function is now possible thanks to recent developments in optogenetics and gene therapy. In comparison to traditional electrical stimulation, gene-based strategies and optogenetic techniques seek to enhance cellular-level targeting and precision, potentially overcoming significant constraints in current implant technology (Yang et al., 2024; Zeng et al., 2008; Kelly et al., 2019). In general, research on cochlear and ocular implants is shifting toward more sophisticated, physiologically guided approaches that give equal weight to long-term tissue preservation and functional restoration. To better protect and sustain sensory cells, new pharmacological, regenerative, and gene-based strategies are being investigated in addition to well-established surgical and device-based advancements. This change is part of a larger movement in implant technology that aims to improve durability, safety, and patient outcomes in sensory rehabilitation by moving away from merely electrical stimulation and toward integrated, protective, and regenerative solutions (Zakay et al., 2021; Holzinger et al., 2020; Meskó and Görög, 2020; He et al., 2024).

7.4 Case study

Optimizing PLGA-based ocular implants for long-term medication administration is an example of machine learning in action. ML models may precisely predict drug release kinetics and find ideal formulation parameters with fewer experimental runs by integrating formulation variables such polymer composition, molecular weight, drug loading, and release profiles. This data-driven strategy shows how machine learning (ML) may speed up the integration of implantable drug delivery systems into precision medicine by cutting down on development time, using fewer resources, and improving formulation performance (Stewart et al., 2018).

8 Conclusion

By enabling quicker, more precise, and patient-specific drug delivery platform design, machine learning is revolutionizing the formulation development of implantable drug delivery systems. It reduces the need for lengthy experimental trials while improving implant performance, medication release patterns, and biomaterial selection using predictive modeling and data-driven optimization. Its uses in cochlear and ocular implants show promise for improving therapeutic efficacy, reducing adverse effects, and supporting individualized treatment plans. The relevance of precision medicine in enhancing patient outcomes is further reinforced by the incorporation of machine learning with smart implants, biosensors, and digital health technology. Ongoing developments in artificial intelligence and biomedical engineering are anticipated to overcome obstacles such as restricted data availability, regulatory limitations, model interpretability, and clinical validation. All things considered, machine learning-enabled implantable medication delivery devices offer a viable strategy for creating safer, more efficient, and customized treatments for the future of medicine.

Literature search methodology

A thorough literature search utilizing PubMed, Scopus, Web of Science, ScienceDirect, and Google Scholar was used to compile this narrative review. Keywords including “machine learning,” “artificial intelligence,” “implantable drug delivery systems,” “biodegradable implants,” “ocular implants,” “cochlear implants,” “precision medicine,” and “drug release” were used to find pertinent publications published between 2015 and June 2026. The inclusion of machine learning-assisted implanted drug delivery devices was limited to English-language original research articles, review papers, and clinical studies. Excluded were duplicate records, conference abstracts, non-English publications, and research unrelated to the review’s purview. Out of the 150 publications that were first found, about 85 important articles were chosen based on their scientific caliber, applicability, and current developments in the field. While a small number of seminal publications were included to provide crucial background information, recent studies (2020–2026) were given preference.

Generative AI statement

The author(s) declared that generative AI was not used in the creation of this manuscript.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Footnotes

Edited by: Antonio Fernandes, University of Porto, Portugal

Reviewed by: Lina Niu, Fourth Military Medical University, China

Dan-Alexandru Szabo, George Emil Palade University of Medicine, Romania