Artificial intelligence and machine learning in cochlear implant rehabilitation: a narrative review of outcome prediction, speech enhancement, and therapy-support applications

1 Introduction

Cochlear implants (CI) are one of the most significant developments in auditory prosthetics, restoring functional hearing to hundreds of thousands of individuals throughout the world. Following Djourno and Eyriès' initial demonstration of electrical stimulation of the auditory nerve in 1957, subsequent technological advances, particularly in the 1960s and 1970s, resulted in the development of current multichannel devices and their broad clinical acceptance (Mudry and Mills, 2013). Despite these developments, the outcomes of CI remain highly variable. While some receivers see immediate and significant gains in speech perception and spoken language skills, others continue to struggle with speech recognition and ordinary communication, even after intensive rehabilitation (Geers, 2002; Blamey et al., 2013; Holden et al., 2013).

Decades of CI research have discovered key factors of this heterogeneity. Early age at implantation and exposure during sensitive phases of auditory cortex development are strongly associated with improved spoken-language outcomes in pediatric populations (Sharma et al., 2002, 2005; Nicholas and Geers, 2006, 2007). Long-term follow-up studies have shown that children with earlier implants are more likely to develop age-appropriate language and literacy skills (Geers, 2002; Geers and Nicholas, 2013). Speech recognition outcomes in adults have been linked to duration of deafness, residual hearing, etiology, and cognitive factors, with large cohort studies revealing significant interindividual differences even among recipients with similar audiological profiles (Blamey et al., 2013; Lazard et al., 2012; Holden et al., 2013). Together, these findings underscore that CI outcomes are shaped by a complex interaction of biological, experiential, and rehabilitative factors.

Post-implant rehabilitation has a significant impact on listening and communication outcomes. Traditional CI rehabilitation focuses on intense auditory training and speech-language therapy provided by speech-language pathologists (SLP), which is frequently augmented with family involvement and structured home-based listening activities (Eichler et al., 2022). Targeted auditory training has been found to increase speech perception under challenging listening settings (Fu and Galvin, 2007, 2008). Early computer-based auditory training programs showed that technology-assisted practice could enhance clinician-led therapy and promote perceptual learning (Sweetow and Sabes, 2006). Subsequent investigations validated the small but persistent effects of computer-based auditory training in adults with CI, while also emphasizing variability in outcomes and adherence (Henshaw and Ferguson, 2013). Importantly, cognitive skills such as working memory and attention have been demonstrated to influence rehabilitation improvements, adding to the diversity in outcomes (Pisoni et al., 2011).

Although pre-2015 research demonstrated the benefits of early intervention, therapy intensity, and tailored rehabilitation methodologies, service delivery remains resource-intensive and unequal. Access to specialized speech-language pathology treatments may be limited for those living in rural or underprivileged areas, and therapy intensity frequently decreases with time due to logistical and financial constraints. These challenges have sparked interest in scalable, adaptive systems that might facilitate personalized rehabilitation while expanding access beyond typical clinical environments.

Before the widespread implementation of modern artificial intelligence (AI) techniques, particularly machine learning (ML), CI research focused on signal processing, pattern recognition and algorithmic approaches to enhancing auditory perception. Prior to 2015, researchers investigated computational models for speech improvement, noise reduction, and feature extraction. For example, Loizou and colleagues created algorithms in the 2000s to increase speech intelligibility for CI users, particularly in difficult listening conditions, utilizing adaptive filters and pattern recognition methods. In the mid-2010s, some research began to combine basic statistical learning with classifier-based techniques, such as decision trees and support vector machines, to predict CI outcomes or improve device settings (Buechner et al., 2015). Although, these methods did not use the deep learning architectures used in current research, they were the first attempts to apply computational learning concepts to CI performance and rehabilitation.

These early contributions laid the groundwork for the broader integration of AI and ML methods into hearing research and audiology, which is discussed further. Modern ML- algorithms allow for the modeling of complicated, non-linear interactions between demographic, audiological, cognitive, and neurological characteristics, many of which have been identified as major determinants of CI outcomes in clinical research. Rather than abandoning traditional rehabilitation concepts, these approaches define and expand on them, improving result prediction, promoting tailored counseling, and enabling adaptive, technology-assisted therapy delivery. This historical and present approach emphasizes the changing significance of computational intelligence in improving CI outcomes and rehabilitation procedures for both pediatric and adult populations.

Given the rapid expansion and variability of this developing research area, a narrative review approach was adopted to map the existing literature on AI and ML in CI rehabilitation without imposing predetermined thematic boundaries.

Specifically, the paper aims to:

• (1) Synthesize evidence on the application and clinical performance of AI- and ML-based approaches in CI rehabilitation.
• (2) Critically evaluate methodological strengths and limitations in the current literature, including sample characteristics, validation approaches, and outcome measures.
• (3) Identify key challenges and future directions for clinically meaningful, ethically sound, and person-centered integration of AI into CI rehabilitation practice.

2 Methods

This narrative literature review focuses specifically on two domains: ML-based predictive modeling of CI outcomes and AI-assisted speech therapy tools, including automated and tele-rehabilitation platforms. An extensive literature search was conducted in PubMed, Scopus, Web of Science, and Google Scholar to identify research published between 2015 and 2025. Search phrases included “cochlear implant,” “speech therapy,” “auditory rehabilitation,” “machine learning,” “artificial intelligence,” “prediction models,” and “AI-assisted therapy,” with Boolean operators and database-specific filters. A supplementary update search was conducted in June 2026; no additional eligible studies were identified. The reference lists of the included studies were further examined for other relevant articles. Reference lists of included studies were additionally screened for relevant publications. Studies were included if they were peer-reviewed, published in English, focused on AI/ML applications in CI rehabilitation, and reported objective or standardized speech, language, or auditory outcomes. Reviews, clinical trials, pilot studies, and original research were eligible for inclusion, while conference abstracts, editorials, studies without full text, case reports, and papers unrelated to AI-assisted CI rehabilitation were excluded. One study (Keshavarzi et al., 2019) included hearing-impaired listeners rather than confirmed CI users. It was retained because the experimental conditions employed acoustic CI simulation (vocoded speech) modeling CI hearing, and its findings directly inform the deep-learning speech enhancement pipeline evaluated in CI-specific studies reviewed here. This exception is noted explicitly in Table 1.

Table 1. Summary of included studies—domain 2: deep-learning approaches for speech enhancement and CI signal processing (n = 10).

[b] Keshavarzi et al. (2019) was conducted on hearing-impaired listeners using acoustic CI simulation (vocoded speech); retained on the basis of methodological relevance to CI signal processing pipeline (see Section 2 for justification). Study count for Domain 2 is n = 9 (10 rows; Keshavarzi counted separately as CI simulation study).

After the removal of duplicates, titles and abstracts were screened according to the inclusion and exclusion criteria, followed by a full-text assessment of potentially relevant studies, 22 studies were included in the final synthesis. Any disagreements during study selection were resolved through discussion. Data extracted from each study included study design, participant characteristics, AI/ML model type and architecture, rehabilitation modality, outcome measures, and key findings or limitations. Extracted data were narratively synthesized and grouped according to the type of AI application—predictive modeling or therapy augmentation—to identify trends, clinical relevance, strengths, limitations, and research gaps. A summary table outlining the inclusion/exclusion criteria and key study characteristics was prepared to enhance the transparency and replicability of the review (Table 2).

Table 2. Inclusion and exclusion criteria for study selection.

CI, Cochlear Implant; AI, Artificial Intelligence; ML, Machine Learning; DNN, Deep Neural Network; CNN, Convolutional Neural Network; LSTM, Long Short-Term Memory network; SVM, Support Vector Machine.

After deduplication, both authors independently screened the titles and abstracts according to the inclusion and exclusion criteria. Studies that passed abstract screening were fully reviewed. Disagreements were settled through organized discussion, and there was no need for formal third-party arbitration. After a complete review of the texts, 22 studies were included in the final synthesis (Figure 1).

Data extracted from each study encompassed: study design; participant characteristics (sample size, age group, CI device); AI/ML model type and architecture; rehabilitation modality; primary outcome measures; key findings; and reported limitations. A formal risk-of-bias assessment utilizing PROBAST (Wolff et al., 2019) was not conducted for all studies; however, the methodological quality of prediction model studies was assessed according to PROBAST criteria as a reference framework. The lack of a formal PROBAST scoring is recognized as a constraint.

Extracted data were narratively synthesized and grouped according to AI application domain: (1) outcome prediction, (2) speech enhancement, (3) AI-assisted therapy tools, and (4) tele-rehabilitation. Within each domain, studies are discussed in terms of methodological approach, key findings, clinical relevance, and limitations.

3 Results

Over the past decade, there has been a marked increase in research focused on the application of AI resp. ML in CI rehabilitation. The systematic search yielded 1,842 records after deduplication. Following screening of titles and abstracts, 214 studies underwent a full-text review. Of these, 22 met all the eligibility criteria and were included in the narrative synthesis. Following full-text review of the 22 included studies, four primary application domains emerged inductively from the literature:

• (1) Machine-learning–based prediction of CI outcomes;
• (2) Deep-learning approaches for speech enhancement and signal processing;
• (3) AI-assisted auditory or speech therapy tools; and
• (4) Tele-rehabilitation platforms integrating AI-based automated feedback.

Sample sizes in these studies ranged from small pilot groups of 10 to 25 participants to larger datasets with over 200 CI users. Most research focused on adults, though some included children or mixed-age groups. Researchers often used supervised machine learning methods like random forests, support vector machines, and elastic net regression, as well as deep-learning models such as deep neural networks (DNNs), convolutional neural networks (CNNs), and long short-term memory (LSTM) models. Although the studies varied in design and outcomes, they determine a growing interest in using AI to give better input with clinical decisions and rehabilitation for CI users.

3.1 Machine-learning models for predicting CI outcomes

A total of seven recent research focuses on the use of machine-learning models to forecast post-implantation outcomes such as speech perception, auditory function, and overall rehabilitation success. Studies using supervised learning approaches generally show better prediction ability than standard statistical methods, especially when multidimensional clinical data is included (Table 3).

Table 3. Summary of included studies—domain 1: machine-learning-based cochlear implant outcome prediction (n = 7).

[a] Demographic Reporting column indicates which of the following characteristics were reported in each study: Sex, Age range, SES (socioeconomic status), and Geographic origin. ‘No' indicates the characteristics was not reported in the study.

For example, Holden et al. (2016) and later Moberly et al. (2021) showed that models incorporating demographic characteristics, length of deafness, audiological measures, and cognitive-linguistic factors could estimate speech recognition results with clinically relevant accuracy. Several studies have demonstrated the predictive significance of cognitive factors such as working memory and phonological processing, which are not often included in traditional CI candidacy or follow-up examinations (Moberly et al., 2017, 2021). Random Forest models have also been used to predict speech intelligibility in children with CI, identifying CI wear time, vocabulary, and socioeconomic factors as key predictors (Byeon, 2018). Imaging-based factors such as electrode position and cochlear architecture have also been demonstrated to improve model performance in specific cohorts (Holden et al., 2016).

Despite these promising findings, most prediction studies are retrospective and use single-center datasets (Han et al., 2019; Demyanchuk et al., 2025; Shew et al., 2025). External validation and prospective evaluation are uncommon, limiting the generalizability of current models and emphasizing the importance of bigger, multicenter datasets before widespread clinical use. It should further be noted that multicenter external validation does not in itself address individual neurobiological heterogeneity if the predictor variables employed remain group-averaged demographic features (Luo et al., 2020, 2021)—a concern developed fully in Section 4.1.2.

3.2 Deep-learning approaches for speech enhancement and CI signal processing

Another common application is the use of deep-learning algorithms to improve the auditory input provided by CI, especially in acoustically demanding locations. Ten research studies using deep neural networks for speech augmentation show considerable gains in speech intelligibility and listening comfort for CI users (Table 1). Speech enhancement using deep-learning algorithms represents the technically most mature application domain within this review. Multiple studies have demonstrated that DNN-based noise reduction and signal separation algorithms can produce meaningful improvements in speech intelligibility for CI users, particularly under adverse listening conditions characterized by high-level background noise or competing speech (Borjigin et al., 2024).

Wang et al. (2017) provided an early demonstration that DNN-based processing outperformed conventional noise reduction in conditions with competing speech. Lai et al. (2018) subsequently reported statistically significant improvements in sentence intelligibility scores for adult CI users under steady-state and fluctuating noise conditions using a DNN architecture trained on large speech-in-noise corpora. Goehring et al. (2019) extended these findings using a crossover trial design, demonstrating that DNN-processed CI stimuli not only improved intelligibility but also reduced subjective listening effort—a clinically important outcome that may facilitate the generalization of therapy gains to everyday communicative situations.

Smaller studies with pediatric CI cohorts have reported similar trends, suggesting that the benefits of deep-learning enhancement are not restricted to adult users. However, pediatric-specific speech enhancement models have not yet been developed; existing systems are trained primarily on adult speech corpora, which may limit their performance for children given differences in vocal tract characteristics and speech acoustics (Mamun and Hansen, 2024; Gurunath Shivakumar and Georgiou, 2020; Patel and Scharenborg, 2024).

A consistent challenge across this literature is the gap between laboratory performance and real-world clinical deployment. Current deep-learning enhancement systems require offline or near-real-time processing and are not yet integrated into clinical CI processors. Computational latency, hardware constraints of current implant architectures, and the need for device-specific calibration remain significant engineering barriers (Kang et al., 2021; Gajecki and Nogueira, 2024; Gaultier and Goehring, 2024). Authors consistently acknowledge that translation from controlled laboratory conditions—in which acoustic environments and signal-to-noise ratios are carefully controlled—to the acoustic variability of everyday life represents the critical unsolved challenge in this domain (Goehring et al., 2017).

3.3 AI-assisted auditory and speech therapy tools

A total of three studies were found in this area of research (Table 4). AI-assisted therapy platforms that deliver adaptive auditory training and speech practice represent the domain most directly relevant to speech-language pathology clinical practice. These systems typically employ adaptive algorithms that adjust task difficulty and feedback delivery in real time based on user performance, enabling individualized practice at scale outside the clinic.

Table 4. Summary of included studies—domain 3: AI-assisted auditory and speech therapy tools (n = 3).

Schumann et al. (2015) demonstrated that adaptive, performance-contingent phoneme discrimination training produced significantly greater gains than non-adaptive training in experienced adult CI users, establishing the foundational principle that algorithmic adaptation enhances training efficiency. It should be noted, however, that the Schumann et al. (2015) platform predates modern deep-learning architectures and relied on rule-based adaptive algorithms; it should therefore be characterized as an adaptive technology tool rather than an AI system in the contemporary sense. More recent platforms incorporating deep-learning-based speech models and reinforcement learning-based adaptation algorithms have been reported in feasibility studies, though robust efficacy data from randomized trials remain limited.

Studies examining AI-assisted therapy tools report improvements in user engagement, better adherence to home-based training protocols, and gains in targeted auditory discrimination or speech production accuracy (Schumann et al., 2015; Reis et al., 2021; Dornhoffer et al., 2024). Automated feedback mechanisms paired with periodic clinical review appear to improve listening effort, motivation, and practice consistency relative to self-directed home training. Gamified pediatric applications—which incorporate visual reward mechanisms and difficulty-scaling algorithms responsive to real-time performance—show particular promise for supporting early auditory skill development in children with CIs (Dornhoffer et al., 2024). However, the evidence base for pediatric AI-assisted therapy remains limited by heterogeneous intervention designs, short follow-up periods, and a near-complete absence of active control conditions, making it difficult to isolate the specific contribution of algorithmic adaptation relative to increased practice time alone.

A notable gap across this domain is the absence of studies examining whether AI-assisted therapy gains are maintained at follow-up and whether they transfer to real-world communicative function, as opposed to performance on laboratory discrimination tasks. Future studies should incorporate functional communication outcome measures—such as the Abbreviated Profile of Hearing Aid Benefit (APHAB), the Speech Spatial Qualities of Hearing Scale (SSQ), or the Hearing Handicap Inventory—alongside standard audiometric indices.

Although pediatric-focused applications show potential for assisting with the development of early auditory skills, significant diversity in intervention length, outcome measures and study design prevents direct comparison across studies and clear conclusions about long-term efficacy. Another limitation of the current evidence base that warrants specific attention is that existing AI-assisted therapy tools for CI users have almost exclusively been evaluated on isolated voice production, single-sound discrimination, and word-level speech perception tasks. To date, no published tool provides automated feedback for connected speech or spontaneous language production—the most ecologically valid and relevant communicative contexts to everyday functional outcomes. This is a particularly significant issue for pediatric CI users, as their rehabilitation goals extend beyond phoneme discrimination to include narrative competence, conversational turn-taking and productive use of complex syntax in natural interactions (Crowe and Dammeyer, 2021; Benassi et al., 2021; Arras et al., 2023; Porcar-Gozalbo et al., 2024). Developing automated feedback systems that can analyze connected and spontaneous speech is a critical next step for the field and would substantially extend the clinical utility of AI-assisted rehabilitation beyond the structured, controlled tasks that have been evaluated to date.

3.4 AI-integrated tele-rehabilitation platforms

Two studies described AI integration in tele-rehabilitation providing early evidence for the feasibility of algorithmically supported remote cochlear implant (CI) care (Table 5). Meeuws et al. (2020) demonstrated that remote cochlear implant (CI) fitting could be guided by psychoacoustic self-tests combined with AI algorithms, achieving outcomes comparable to in-person fitting without requiring direct audiologist involvement in every adjustment decision. This is a significant application of AI in remote care, as the algorithm participates in clinical decision-making regarding device programming, rather than merely facilitating communication. Maruthurkkara et al. (2022) evaluated the Remote Check test battery, an automated remote monitoring platform that uses signal analysis algorithms to assess device function, hearing performance, and patient-reported outcomes, flagging abnormal results for clinical review. Results from this proof-of-concept study demonstrated acceptable accuracy and patient satisfaction, supporting the potential of automated signal monitoring as a scalable and clinically safe approach to routine CI follow-up.

Table 5. Summary of included studies—domain 4: tele-rehabilitation platforms and remote AI-enabled monitoring (n = 02).

The evidence from AI-integrated remote studies currently rests on a narrow foundation of two studies, both of which are of a feasibility or proof-of-concept design. This limits the conclusions that can be drawn regarding efficacy, generalization across populations, and long-term outcomes. Notably, neither study examined pediatric populations specifically, nor did either address the full scope of CI rehabilitation beyond device programming and monitoring. Therefore, the application of AI to the remote delivery of auditory training, speech and language therapy support, and family-centered rehabilitation guidance remains largely unexplored in the current literature, representing a substantive gap that future research should prioritize.

Nevertheless, a critical limitation runs across the pediatric tele-rehabilitation literature: existing platforms have been evaluated almost exclusively on structured auditory discrimination tasks and clinician-administered speech perception measures delivered remotely. No published tele-rehabilitation system to date provides automated feedback for connected speech or spontaneous language production in pediatric CI users—the communicative contexts most relevant to real-world functional outcomes and educational participation. The development of automated tools capable of analyzing naturalistic, spontaneous speech samples collected in tele-rehabilitation contexts represents a critical next step for the field, and one that would substantially extend the clinical utility of remote platforms beyond the structured tasks evaluated to date (Schepers et al., 2019; Luryi et al., 2020; Cullington et al., 2022; Verma et al., 2022; Brewer et al., 2024; Holcomb et al., 2025; Morton et al., 2025; Nassiri et al., 2025).

The majority of currently available tele-rehabilitation platforms remain clinician-guided in their core therapeutic decision-making, and fully autonomous AI-driven tele-rehabilitation—in which algorithms independently adapt therapy content, intensity, and scheduling based on real-time outcome data—has not yet been demonstrated in CI populations. Nevertheless, these platforms constitute the infrastructure on which AI-driven automation will be built. The integration of ML outcome prediction models with tele-rehabilitation delivery systems—enabling real-time identification of children experiencing performance plateaus, automatic adjustment of therapy difficulty, and proactive clinical outreach—represents the most important near-term research and clinical development priority for this domain. Achieving this will require not only technical development but prospective clinical trials that evaluate fully integrated AI-tele-rehabilitation systems against standard care, using outcome measures that reflect functional communication in everyday life rather than laboratory speech perception scores alone.

4 Discussion

This narrative review synthesized evidence from four areas in which AI and ML are being used for CI rehabilitation: predicting outcomes, enhancing speech, creating AI-assisted therapy tools and developing tele-rehabilitation platforms. A consistent pattern emerges across all four domains: technically promising approaches supported by early or moderate evidence that are currently constrained by methodological limitations preventing clinical translation at scale.

Outcome prediction models demonstrate improvements over conventional regression, yet they are still limited to single-center retrospective datasets. Speech enhancement algorithms demonstrate reliable gains in laboratory settings, yet remain underexplored in clinical CI processors. AI-assisted therapy tools improve auditory discrimination performance. However, they have not addressed the broader communicative demands of pediatric rehabilitation. Tele-rehabilitation platforms demonstrate the feasibility and clinical equivalence of remote delivery, but incorporate AI in only limited and preliminary ways. Rather than revisiting these domain-specific findings in detail, the following discussion addresses four cross-cutting issues that must be resolved if AI and ML are to achieve meaningful clinical impact in CI rehabilitation: (1) the systematic underrepresentation of pediatric populations and the population diversity deficit (2) the lack of model explainability and clinical transparency, (3) regional regulatory constraints and readiness, and (4) the high methodological heterogeneity preventing cumulative scientific progress.

4.1 Systematic underrepresentation of pediatric populations and population diversity deficit

4.1.1 Population-level sampling bias

A defining limitation of the current AI and ML literature in CI rehabilitation is a clear adult-centric bias. The majority of ML models across all four domains reviewed have been developed on datasets drawn from high-income countries and predominantly adult populations, raising concerns about systematic underperformance for pediatric users, patients from diverse linguistic backgrounds, and CI recipients in low- and middle-income settings. This bias is not confined to outcome prediction alone—it extends equally to speech enhancement models trained on adult speech corpora, AI-assisted therapy tools evaluated exclusively in adult CI users, and tele-rehabilitation platforms where pediatric evidence remains sparse and largely feasibility-level in design (Boisvert et al., 2020).

This pattern reflects a broader challenge that has become increasingly recognized in biomedical research: the limited demographic diversity of study populations. This restricts the generalisability of research findings to populations beyond those represented in the original datasets (Rudroff, 2026). It is important to note that this issue cannot be addressed solely by increasing the sample size within the same demographic group. Rather, improving population diversity necessitates deliberate recruitment strategies, such as demographic oversampling, international multi-center collaborations, and prospective validation studies involving CI populations from low- and middle-income countries. At a minimum, future AI studies in CI rehabilitation should adopt standardized demographic reporting, including sex, age range, socioeconomic status, geographic origin, and etiology of hearing loss. This would enable readers to better evaluate the representativeness and external validity of published findings.

4.1.2 Individual neurobiological heterogeneity as a distinct validity threat

A second validity threat is present throughout the literature on outcome prediction, but has not previously been identified as such: individual neurobiological heterogeneity. This threat is conceptually and practically distinct from population diversity deficit. While population diversity deficit concerns which individuals are represented in the training data, individual neurobiological heterogeneity concerns the extent to which the predictor variables employed capture the biological variability within any given sample. These two threats are not interchangeable, and the interventions required to address them differ.

The evidence from the outcome prediction studies reviewed here illustrates this clearly. For example, Holden et al. (2016) found that age was only modestly associated with speech recognition outcomes, whereas spectral ripple discrimination—an individual perceptual measure—correlated strongly. This demonstrates that group-level demographic variables carry little explanatory weight relative to individual biological capacity. Kim et al. (2018) found that Random Forest substantially outperformed linear regression (r = 0.96 vs. 0.70), implying that much of the variance in outcomes is non-linear and not adequately captured by variables treated as simple additive predictors in conventional models—a pattern consistent with strong individual-level interactions. Moberly et al. (2021) demonstrated that the contribution of top-down cognitive-linguistic processing to speech recognition depends on the quality of bottom-up sensory input—an interaction effect that varies by individual, rather than by diagnostic or demographic category.

Neuroimaging research has demonstrated that biological variability in brain metabolism can diverge substantially from what a patient's diagnostic category alone would predict. For example, Rudroff (2024) found marked differences in frontal-striatal glucose metabolism between patients with multiple sclerosis (MS) and long-COVID despite shared fatigue symptomatology. Similar attention to individual biological variability is required in CI outcome prediction research, where group-averaged demographic predictors may not adequately capture the neurobiological diversity that drives outcome heterogeneity.

The development of pediatric-specific models that account for developmental differences in vocal tract acoustics, cognitive-linguistic trajectories, and functional rehabilitation goals therefore constitutes a core priority for the field as a whole.

4.2 Model explainability and clinical transparency

Explainability is a critical concern running across all digital rehabilitation domains. Clinicians cannot be expected to act on predictions from opaque, “black-box” models that provide no insight into the features driving a given output. This is particularly consequential for speech-language pathologists and audiologists whose professional and ethical obligations require that clinical decisions be transparent and justifiable to patients and caregivers. The adoption of explainable AI (XAI) methods—including SHAP values and attention visualization mechanisms—should be integrated as a standard methodological practice in any AI architecture intended for clinical CI rehabilitation.

However, it is important to note that explainability does not substitute for model validity. An interpretable model trained on a sample with a deficit of population diversity, or one in which individual neurobiological variability has been collapsed into group-averaged predictors, will produce a transparent account of a biased and underspecified relationship. SHAP values and attention visualization methods reveal what a model has learned from its training data. They cannot correct for what was absent from, or inadequately represented in, that data. Therefore, clinicians and developers should treat explainability as a necessary but not sufficient condition for trustworthy AI in CI rehabilitation, pursuing it alongside efforts to address population diversity deficits and individual neurobiological heterogeneity (Tjoa and Guan, 2023).

4.3 Regulatory readiness and frameworks

Beyond algorithmic transparency, clinical translation is heavily bound to evolving international regulatory readiness. For example, the European Parliament and Council of the European Union (2024) classifies healthcare outcome prediction tools as “high-risk AI,” mandating strict conformity assessments and continuous post-market monitoring. This classification has deep global implications not only for raw predictive modeling but for any software architecture that influences active clinical decision-making in CI rehabilitation, including automated therapy adaptation algorithms and remote monitoring setups. Developers of CI rehabilitation tools must actively align with these emerging international compliance frameworks from the earliest stages of software architecture design.

4.4 The lab-to-real-world translation gap

Across all four domains, a consistent and unresolved tension exists between performance under controlled laboratory conditions and functional utility within the acoustic and communicative variability of everyday life. Speech enhancement algorithms demonstrate reliable intelligibility gains in laboratory noise conditions, yet they have not been deployed in clinical CI processors used in real-world settings. Similarly, outcome prediction models perform exceptionally well on retrospective, single-center datasets but lack prospective validation across diverse, multi-site clinical populations. This translational gap is especially pronounced in therapeutic applications. AI-assisted therapy tools have been evaluated almost exclusively on isolated phoneme discrimination tasks rather than the connected speech and spontaneous language production that constitute everyday functional communication. Likewise, tele-rehabilitation platforms have demonstrated clinical equivalence for highly structured auditory tasks delivered remotely, but they have not yet been evaluated in naturalistic speech contexts. This convergent limitation across all domains indicates that a fundamental reorientation of research priorities is needed. Rather than continuously optimizing performance benchmarks on static, controlled datasets, future research must prioritize demonstrating clinical utility using ecological outcome measures that reflect a patient's true communicative success in daily life.

4.5 Heterogeneity and the standardization imperative

Across all four domains, extensive heterogeneity in study design, participant characteristics, outcome measures, and algorithmic frameworks substantially limits the ability to draw definitive conclusions or compare findings across independent research groups. The field of audiological AI currently lacks agreed accuracy thresholds, standardized outcome batteries, or common open-source data infrastructure analogous to the frameworks that have successfully driven progress in other medical AI specialties. Without rigorous standardization, establishing cumulative progress across research groups will remain difficult, and the translation of isolated digital innovations into widespread clinical guidelines will be substantially delayed (Mo et al., 2025). To overcome this barrier, the field must mature through the collaborative establishment of shared benchmarking datasets, agreed minimum reporting standards for AI architectures in audiology, and core consensus outcome batteries developed jointly by data scientists, engineering teams, and practicing clinicians.

5 Strengths and limitations

This review makes several contributions to the literature. It provides a structured synthesis of AI and ML applications across four distinct domains of CI rehabilitation, explicitly linking technical developments to clinical practice implications for speech-language pathologists and audiologists. The inclusion of diverse study designs—RCTs, prospective cohorts, retrospective studies, and systematic reviews—captures a broader evidence base than reviews restricted to experimental designs. The review was conducted using systematic search procedures aligned with PRISMA-ScR, enhancing transparency and replicability relative to unstructured narrative reviews.

Several limitations must be acknowledged. The narrative synthesis approach, while appropriate for a heterogeneous and rapidly evolving literature, does not permit quantitative aggregation of effect sizes, and findings are therefore descriptive rather than statistical. Formal risk-of-bias assessment using PROBAST was not applied to all prediction model studies, meaning that the strength of evidence depends in part on the quality of reporting in the primary literature. The rapid pace of AI development means that some recent advances—particularly in generative AI and large language model applications—may not yet be represented in indexed databases at the time of this review. Publication bias is likely to favor positive findings, and the true effect sizes of AI-assisted interventions may be smaller than those reported in the current literature and may further exclude populations with distinct underlying neurobiological profiles beyond differences in research infrastructure alone (Rudroff, 2026). Finally, the focus on English-language publications may exclude relevant research from non-English speaking research communities, particularly East Asian countries that have been active in CI signal processing research.

6 Conclusions

The increasing use of AI and ML in CI rehabilitation is highlighted in this narrative review, with special attention on speech therapy and hearing outcomes. AI- and ML-based techniques provide supplementary tools that can assist more customized, data-informed rehabilitation paths for both pediatric and adult CI users, rather than taking the place of well-established therapeutic procedures. Personalized outcome estimates, better auditory input through sophisticated signal processing, and improved therapeutic administration via adaptive and remote platforms are just a few of the clinically significant domains where AI applications show promise in present research. All of these advancements hint to a future where technology-assisted care may increase access to services, therapy intensity can be increased outside of the clinic, and rehabilitation tactics can be more sensitive to individual variability.

However, the integration of AI-based innovations into standard clinical practice is still in its infancy. Additional validation across a variety of demographics and real-world rehabilitation settings is necessary for several of the current tools. Prospective, multicenter trials, functional communication-reflective standardized outcome measures, and close collaboration between data scientists, engineers, and clinicians should be the main focus of future research. To guarantee that AI-driven solutions are clinically successful, morally sound, and in line with the objectives of person-centered cochlear implant rehabilitation, it will be crucial to address these demands. In particular, the establishment of collaborative open-source benchmarking infrastructure—including shared datasets, agreed minimum AI reporting standards for CI rehabilitation research, and consensus outcome batteries developed in partnership with clinicians, patients, and regulatory bodies—represents a concrete and achievable near-term priority that would substantially accelerate the translation of technically promising findings into clinical practice. In conclusion, AI and ML are potent new tools that can help CI users with speech therapy and auditory rehabilitation. These technologies have the potential to significantly improve long-term communication results and supplement, not replace, professional skills with cautious integration and thorough assessment.

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Footnotes

Edited by: Mehdi Abouzari, University of California, Irvine, CA, United States

Reviewed by: Thorsten Rudroff, University of Turku, Finland

Saurabh Varshney, All India Institute of Medical Sciences, Deoghar (AIIMS Deoghar), India