Dr. Achin Bhowmik Details the Many Ways Today’s Hearing Aids are Not Like Your Father’s

Send us Fan Mail Our Chief Technology Officer and EVP of Engineering, Achin Bhowmik , Ph.D., tells Dave what brought him to Starkey, how AI is a hearing technology game changer, and the many ways his team is turning hearing aids into want-to-have devices. Link to full transcript

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A systematic review of machine learning techniques for real-time speech and noise classification in hearing aids

No immediate practice change warranted, but audiologists should monitor this fast-moving area as ML-based classifiers are approaching real-time clinical deployment and may soon influence device selection counseling.

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Real-time brain-controlled selective hearing enhances speech perception in multi-talker environments

This is a promising proof-of-concept, but the technology requires real-time neural decoding hardware not yet suitable for clinical hearing aids; no practice change is warranted now, though audiologists should monitor this research area closely.

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Human versus AI in audiological practice: A comparative evaluation of ChatGPT and physician treatment decisions in idiopathic sudden sensorineural hearing loss

Findings are preliminary and do not yet justify substituting or formally integrating ChatGPT into clinical decision-making for sudden sensorineural hearing loss; audiologists should await further validation before changing practice.

PubMed·Journal article·AI & technology·13d ago

Therapeutic Hybrid Intelligence with Neural and Knowledge-based Expert Reasoning for SRS (THINKERS): a mixture-of-experts AI model for vestibular schwannoma

No immediate change to clinical audiology practice; THINKERS is a research-stage AI tool for neurosurgical outcome prediction in vestibular schwannoma and requires prospective validation before clinical adoption.

PubMed·Journal article·AI & technology·8d ago

Consensus on the use of artificial intelligence in the management and measurement of vestibular schwannomas: A protocol for a modified delphi consensus

Standardising AI-based volumetric measurement of vestibular schwannomas could reduce inter-centre variability in management decisions and accelerate reliable AI adoption in neuro-otology.

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EEG-audio fused auditory attention decoding represents a key research frontier for next-generation neuro-steered hearing aids, making this directly relevant to audiology's technology pipeline.

PubMed·Journal article·AI & technology·6w ago

Single-trial EEG-based classification reveals instrument-specific timbre perception via traditional machine learning classifiers

Demonstrating that instrument-specific sound qualities (timbre) can be decoded from single-trial EEG opens a long-term research pathway toward neurofeedback-driven, music-optimised hearing aid signal processing.

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In-ear EEG wearables represent a convergence of audiology hardware and neurotechnology that could reshape how hearing devices are designed and what cognitive services audiologists provide.