AI for Brain Implants Stuck in Lab Stage, Study Finds
Artificial intelligence tools designed to improve deep brain stimulation for Parkinson's disease show promise in research but rarely leave the laboratory, a comprehensive review reveals. The bottleneck isn't flawed algorithms—it's inadequate real-world testing and small datasets prone to failure, delaying treatments that could benefit millions of patients.
Originaltitel: Artificial intelligence in deep brain stimulation for movement disorders: a systematic review and technology readiness assessment.
**AI i djup hjärnstimulering: validering hämmar marknadsmognad** Djup hjärnstimulering (DBS) för rörelserubbningar får allt oftare AI-assistans, men systemen är ännu långt från klinisk tillämpning. En systematisk granskning av 239 studier från 2000–2025 visar att forskningen dominerats av Parkinsons sjukdom och målpunkt i subthalamiska kärnor, medan andra indikeringar är underrepresenterade. Även om enskilda studier rapporterar lovande resultat internt, saknas extern validering nästan helt. Över en fjärdedel av studierna baserades på små dataset med högriskfaktorer för överanpassning. Teknisk mognadsbedömning klassificerar de flesta systemen som tidiga till mellanstadier. Begränsningen är inte algoritmer utan valideringsbrist, försämrad av biologisk heterogenitet i DBS-respons. Senare prospektiva studier indikerar dock rörelse mot klinisk mognad inom målpunktsfunktion, programmeringsstöd och adaptiv terapi. För inköpare och regulatorer signalerar detta ett område i utveckling — främst lämpat för prospektiva multicenterstudier före bred distributionsplanering.
Artificial intelligence (AI) is increasingly explored across deep brain stimulation (DBS) for movement disorders, yet whether current systems are approaching deployment remains unclear. To characterise their scope, validation maturity, and translational readiness, we systematically evaluated 239 peer-reviewed studies published between 2000 and 2025, assessing AI methods, validation practices, and barriers constraining clinical translation. Research was dominated by Parkinson's disease and subthalamic nucleus targeting, with limited coverage of other disorders and targets. Most studies reported encouraging internal performance; however, external validation was rare, evaluations remained predominantly retrospective and single-centre, and more than one-quarter involved small-sample, high-dimensional datasets with elevated overfitting risk. Technology readiness assessment revealed that most systems remain at early-to-intermediate translational stages, constrained more by limited validation than by algorithmic inadequacy, compounded by the biological heterogeneity and dynamic complexity inherent to DBS. Nevertheless, emerging external and prospective studies suggest a field moving toward clinical maturity, with promising applications in targeting, programming, outcome prediction, and adaptive therapy delivery.