Background: Cochlear implants (CIs) serve diverse populations with hearing loss, but patients with single-sided deafness (SSD) often show lower post-implantation usage and satisfaction than bilateral CI users. This disparity may stem from their normal contralateral ear providing sufficient auditory input for many daily situations, reducing the perceived need for consistent CI use. Consequently, uniform screening and evaluations, typically designed for bilateral hearing loss, often fail to address SSD’s unique needs. Methods: This narrative review synthesizes the current literature to explore patient and device factors shaping CI integration, outcomes, and experience in SSD. It highlights implications for developing personalized care strategies distinct from those used in bilateral hearing loss. Results: SSD patients face unique challenges: reliance on compensatory behaviors and significant auditory processing difficulties like acoustic–electric mismatch and place–pitch discrepancy. Anatomical factors and deafness of duration also impact outcomes. Traditional measures are often insufficient due to ceiling effects. Music perception offers a sensitive metric and rehabilitation tool, while big data and machine learning show promise for predicting outcomes and tailoring interventions. Conclusions: Optimizing CI care for SSD necessitates a personalized approach across candidacy, counseling, and rehabilitation. Tailored strategies, including individualized frequency mapping, adaptive auditory training, advanced outcome metrics like music perception, and leveraging big data for precise, data-driven predictions, are crucial for improving consistent CI usage and overall patient satisfaction.

Background: Cochlear implants (CIs) are transformative neuroprosthetics that restore speech perception for individuals with severe-to-profound hearing loss. However, temporal envelope cues are well-represented within the signal processing, while spectral envelope cues are poorly accessed by CI users, resulting in substantial deficits compared to normal-hearing individuals. This profoundly impairs the perception of complex auditory stimuli like music and vocal prosody, significantly impacting users’ quality of life, social engagement, and artistic expression. Methods: This narrative review synthesizes research on CI signal-processing limitations, perceptual and production challenges in music and singing, the role of the auditory–motor feedback loop, and strategies for improvement, including rehabilitation, technology, and the influence of neuroplasticity and sensitive developmental periods. Results: The degraded signal causes marked deficits in pitch, timbre, and vocal emotion perception. Critically, this impoverished input functionally breaks the high-fidelity auditory–motor feedback loop essential for vocal control, transforming it from a precise fine-tuner into a gross error detector sensitive only to massive pitch shifts (~6 semitones). This neurophysiological breakdown directly causes pervasive pitch inaccuracies and melodic distortion in singing. Despite these challenges, improvements are possible through advanced sound-processing strategies, targeted auditory–motor training that leverages neuroplasticity, and capitalizing on sensitive periods for auditory development. Conclusions: The standard CI signal creates a fundamental neurophysiological barrier to singing. Overcoming this requires a paradigm shift toward holistic, patient-centered care that moves beyond speech-centric goals. Integrating personalized, music-based rehabilitation with advanced CI programming is essential for improving vocal production, fostering musical engagement, and ultimately enhancing the overall quality of life for CI users.

Objective: To quantify the comprehensive financial impact from implementing a deep learning algorithm for assistive patient triage within a high‐volume Head and Neck Surgical Oncology center operating under a fee‐for‐service framework. Methods: A prospective cost–benefit analysis compared a deep learning algorithm to standard manual triage on all incoming new patient referrals ( n = 214) in one fiscal quarter (FY24 Q3). Financial impact was calculated using a model aggregating two economic components: (1) annualized labor displacement savings, and (2) revenue impact of reallocating verified non‐indicated clinic slots to appropriate surgical candidates. Results: Automated head and neck referral triage demonstrated a net annual operating gain of $535,989, yielding a 14.9× return on investment. Directly measured labor savings ($236,788.80; 5.6× ROI) represent the conservative estimate; capacity value is modeled, contingent on slot reallocation. The AI‐assisted workflow displaces 1951.20 h of manual work (0.94 FTEs) annually per 6‐provider division processing 30 referrals/week, and up to 351,936 h (169.20 FTEs) per hospital system. Furthermore, the algorithm's superior accuracy identified 80 verified recoverable clinic slots per year for the division; reallocating these to appropriate candidates generated an additional $6.2 million in downstream revenue. Conclusion: The integration of a deep learning algorithm for Head and Neck Surgical Oncology referral triage delivers substantial financial benefits through the dual mechanisms of direct reduction of administrative labor and the strategic optimization of clinical capacity. By identifying non‐indicated referrals, the system allows for the reallocation of limited resources to patients requiring high‐acuity surgical oncological care, offering a scalable model for financial sustainability. Level of Evidence: NA.