Language AI that can detect medical conditions in patient data plays a pivotal role in disease detection and early diagnostics, but as these models become more advanced and target specific conditions across the spectrum of mental and physical health, what role do translation services like linguistic validation play in reshaping our ability to leverage patient data and more accurately detect underlying conditions? This week, at the intersect between natural language processing (NLP) and mental health, comes news of advanced language AI models that are being trained to find patterns and irregularities in patient data to indicate the presence of mental health conditions in pediatric patients. For mental health professionals, developments to this scale provide some insight into new ways in which patient data, and specifically self-reported patient data, improves our ability to provide more accurate and patient-focused care. But in the wake of NLP being used to analyze documents such as patient reported outcomes (PROs), these technological advancements are creating a new demand for translation services in medical localization, most notably in the form of linguistic validation, to ensure that the data needed for diagnosis remains consistent and reflective of all patient populations.
Recently, doctors from Cincinnati Children’s Hospital and researchers from the Oak Ridge National Laboratory teamed up to train NLP models using the world’s second most powerful supercomputer, IBM’s Summit model. Using a collection of handwritten patient notes, medical journals, and PRO documents, these NLP algorithms identify linguistic inconsistencies in self-reported pediatric patient data, enabling clinicians to potentially diagnose any number of mental health conditions. As Forbes reports, technology of this scale can allow for doctors to treat up to 50% of mental health conditions if correctly identified in the pediatric population. With Mental Health Awareness month underway, this news echoes our previous discussion with partner of CSOFT Health Sciences, Dr. Nelson Handal, who provided insights into his company’s diagnostic and assessment tool that leverages patient data and is currently operational in multiple languages. Founded by Dr. Handal, Clinicom is a comprehensive digital platform for addressing mental health conditions that analyzes self-reported patient data extracted from questionnaires to help provide clinicians with a basis for diagnosing over 80 different conditions. While mental health conditions are complex and involve a wide range of contributing factors, PROs and the resources used for training diagnostic AI are required to be scrutinized and assessed for clinical accuracy. In the growing space of clinical research into mental health and the deployment of AI to enhance our capabilities to diagnose specific conditions, it is essential to validate all types of patient data, ensuring that the accuracy of this technology is not hindered by inconsistent patient data.
With strides being taken to assess and diagnose mental health conditions, leveraging the best aspects of medical localization through linguistic validation is necessary for accurately collecting and documenting patient data across different languages. During any type of clinical trial or situation when PROs need translation, linguistic validation is used to ensure the data remains conceptually consistent and highly accurate. During the process of linguistic validation, two translators will independently translate two separate versions of the same document into a target language, before an additional linguist analyzes both translations and combines them into a single, more accurate translation. Next, the document is translated back into the source language, a step designed to eliminate any remaining linguistic and grammatical inconsistencies. Lastly, the finalized document is assessed by the linguist and an expert in the field of study, such as a clinician, which is a necessary step for analyzing the clinical accuracy of the translations in addition to the cultural and linguistic relevance. Once complete, linguistic validation ensures that the translation is consistent while also maintaining the highest degree of clinical accuracy.
For essential patient facing documents used for clinical outcome assessments (COAs), linguistic validation is necessary to ensure that patient data collected from linguistically and culturally diverse populations does not lose its value and accuracy when going through the translation process. As an integral part of any data collection process in the clinical setting, CSOFT Health Sciences provides expert linguistic validation services to help companies through this process. To learn more about our medical translation services, visit lifesciences.csoftintl.com.