While AI aids personalised therapies in many medical fields, mental health care is still widely based on ‘trial and error.’
A new EEG and AI-powered brainwave analysis model by Flow Neuroscience showcases that patient-specific, more timely and effective mental health treatment is possible.
From AI therapy trends to brain data being titled ‘the new gold,’ scientists are increasingly exploring how it could improve the treatment of various health issues. While AI-powered data analysis streamlines the precision of person-specific treatment in oncology, cardiology and other healthcare sectors, mental health care has been slower to catch up.
Healthcare experts from Flow Neuroscience, a company that develops neuromodulation devices for depression treatment, weigh in on the unique challenges of implementing personalised, data-driven treatments in mental health and reveal a new AI and EEG-based medical development model that could finally change that.
When guesswork guides treatment
“The implementation of personalised treatments is one of the biggest challenges in modern mental health care,” says Dr. Hannah Nearney, Psychiatrist and Founder of Anchor Psychiatry Group.
The limitations of standard treatment methods can explain such difficulty.
“Lacking clinically applicable biomarkers of mental health conditions, psychiatrists are left to rely on a one-size-fits-all ‘trial and error’ approach. It can lead to unnecessarily long periods of ineffective and often expensive care,” says Dr. Nearney. “Inability to receive timely help ultimately brings patients more suffering, and in the worst cases, can cost lives.”
The mismatch between prescribed therapies and patients’ unique health needs is reflected in research studies: approximately two-thirds of depression patients fail to achieve remission after their first antidepressant trial, and up to 30 per cent don’t respond to at least two different medications.
Patient-derived data for precise mental health care
However, new approaches are being developed to individualise mental health treatment.
Flow Neuroscience is now developing a personalised predictive model to shortcut the path to effective mental health treatment.
Powered by AI and EEG, the model will record and analyse patients’ brainwaves during neurostimulation to identify individual response patterns. By providing this data, the technology will help clinicians determine whether brain stimulation therapy will be effective for each patient – before long-term treatment even begins.
“Brainwave data is a crucial step towards individually matched therapies. Relying on symptom evaluation alone is too limited – two people might look the same on paper, but their brains will respond differently to the same treatment,” says Erin Lee, CEO of Flow Neuroscience.
For the mental health brain stimulation devices in particular, the EEG- and AI-based model will not only help personalise treatment but also pave the way for more proactive, adaptive care.
“In the future, the model could enable therapy that responds to a patient’s changing needs in real time – for example, by adjusting the current or frequency of stimulation based on brain feedback as a clinical marker,” says Dr. Nearney.
Dr. Nearney shares that personalised and data-based approaches could help prevent overmedication.
“When relying on the traditional approaches, clinicians may prescribe multiple medications simultaneously to alleviate symptoms or mitigate side effects from initial treatments,” says Dr. Nearney. “Predicting individual response to a drug and dosage could help patients start effective treatments earlier, surpassing side effects.”
The expanding AI healthcare market, combined with advances in predictive technology, reflects the ongoing shift toward personalised, safer and minimally invasive mental health treatments.
“Mental health care has long been trial and error. Finally, the predictive technology promises precise, data-driven treatments, carefully tailored to each patient,” says Erin Lee.
Global market reports project that the AI in the healthcare sector will reach USD 196.91bn by 2030, expanding at a CAGR of 37.6 per cent from its current value of USD 39.92bn. Such a rise is driven by the demand for more personalised treatment options, the report states.
For more information from FLOW, visit Scientific Evidence | Research & Clinical Trials on tDCS – Flow Neuroscience
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