Improving Neurology Clinical Care With Natural Language Processing Tools

Abstract

The integration of Natural Language Processing (NLP) tools into neurology workflows has the potential to significantly enhance clinical care. However, it is important to address the limitations and risks associated with integrating this new technology. Recent advances in Transformer-based NLP algorithms (e.g., GPT, BERT) could augment neurology clinical care by summarizing patient health information, suggesting care options, and assisting research involving large datasets. However, these NLP platforms have potential risks including fabricated facts and data security as well as substantial barriers for implementation. Although these risks and barriers need to be considered, the benefits for providers, patients, and communities are substantial. With these systems achieving greater functionality and the pace of medical need increasing, integrating these tools into clinical care may prove not only beneficial but necessary. Further investigation is needed to design implementation strategies, mitigate risks, and overcome barriers.

Neurological Diagnosis

“…The authors concluded, despite encouraging results with an improved model, Med-Pal.M, they remain inferior to clinicians. A current report on the use of natural language tools in neurological care 12 comments on the need for external validation that includes expert opinion to insure accuracy, reliability, and suitability for “real-world applications,” yet no prior studies are cited in this regard. A recent study has shown AI with a 64% success rate for including correct diagnosis in the differential list and 39% for the top diagnosis, 13 while another showed diagnostic generators achieved a correct diagnosis in 58 to 68% of cases 14 ; however, no head-to-head studies have been reported.…”

“…AI displays a disclaimer of responsibility, thus liability rests with the end user, and as such comparison to expert opinion and/or diagnostic generators needs to be demonstrated before the user can be confident in AI clinical diagnosis. Lack of authentication for the various diagnostic considerations, black box concept, and confabulations, 12 furthers the need for validation. Caution is advised considering the optimistic outlook with AI’s first outing in 2011 when IBM’s Watson won Jeopardy and could pass the United States Licensing Medical Examination yet, failed at patient diagnosis in the clinical setting and since been abandoned 17 .…”