In the world of health technology, data quality is of great importance. In a special webinar, experts in this field discussed the high importance of data standards, accurate coding of medical concepts and synonymy in improving solutions based on artificial ielligence and natural language processing in the health field. This meeting was dedicated to examining the impact of these issues on improving patie outcomes, creating innovation in health technology, and improving the efficiency of artificial ielligence systems.
The impact of data quality on the success of artificial ielligence and NLP in health
Quality data is the basis of systems efficiency Nice processingnatural And language models are great in health. Since AI models operate on their input data, if the input data is not accurate, the output will not be as reliable. In this webinar, promine experts from reputable companies emphasized the need to increase data quality to achieve more accurate solutions.
Data quality challenges in medical care
Participas in this session poied out various problems that exist in the way of improving data quality, including the complexity of unstructured medical data. In the examples preseed by each of the speakers, the obstacles in the quality of clinical data and the challenges in training artificial ielligence models were discussed. As an example, IQVIA’s Dr. Calum Yacobian poied out the complexity of extracting key data from medical notes and emphasized that the use of artificial ielligence in this direction has helped reduce time and increase accuracy.
The importance of governance systems in data manageme
Ivana Naimirod, Chief Operating Officer of IMO Health, poied out the importance of implemeing governance systems to monitor data quality and ensure the security of sensitive patie data. According to him, if organizations do not have strict monitoring processes and specific standards in the use of artificial ielligence data, the possibility of problems related to privacy and data security will increase.
Using artificial ielligence to improve data
Experts poied out that the use of artificial ielligence as a tool to clean and improve the quality of data has provided a new possibility in this field. Gigi Yuan of Cohere Health explained how her team uses big language models to ideify and correct erroneous data. This process allows professionals to ideify and correct poor quality data more quickly and accurately.
The future of artificial ielligence in the field of health
At the end of the session, each of the participas noted the exciting opportunities that artificial ielligence provides for improving the quality of medical care. They believe that in the future, artificial ielligence can bring better results to paties and improve decision-making processes with the help of more accurate tools that are familiar with clinical language and coe.
This webinar showed that data quality plays a key role in the success of AI technologies in health and that cooperation between technical teams and medical staff is necessary to achieve sustainable solutions.
