Gemma is Content Director at AIMed, with responsibility for engaging and growing the AIMed community and ownership for events from concept through to delivery. An experienced science graduate with a background in veterinary and nonprofit sectors, she also volunteers as a Wish Granter for Make a Wish UK.
Artificial Intelligence (AI) is only as good as the data it’s trained on. Without high-quality, accurately labelled data, even the most sophisticated AI algorithms will produce flawed results. This is where data labellers come in.
Data labellers are responsible for manually labelling and annotating large amounts of data used to train machine learning algorithms. This is a crucial part of the development process. Accuracy is essential, and errors can be catastrophic. For example, if an AI system trained to diagnose cancer misdiagnoses a patient, it could lead to delayed treatment or even unnecessary treatment, causing harm to the patient. Therefore, data labelling requires precision, consistency, and attention to detail.
The labelling process involves creating a set of instructions or guidelines for the labellers to follow, ensuring consistency and accuracy across all the data. The labellers then review and label each data point, verifying that the labels are correct and accurate. Finally, the labelled data is validated to ensure its accuracy and consistency.
Given the importance of data labelling in medical AI, it’s not surprising that there is a growing demand for high-quality data labelling services. Some companies specialize in providing data labelling services for medical AI, offering highly skilled and trained teams of labellers.
Dr Aparna Bhasin is a Radiologist that uses her experience to annotate and review data for Cogito, and provides clinical input and feedback for various use cases, such as chest x-rays, stroke protocols, COVID database, fracture identification and anatomical segmentations on MRI to help ease hospital organisational workflows and prioritise and improve patient triage. Dr Priti Gupta does the same work, as a dentist that specialises in Oromaxillofacial Radiology. She says “as a radiologist, I feel very comfortable with this technology. Automation helps to save time and enhance procedures. New technological advances and diagnostic aids have paved the way for the revolutionization of conventional dental treatment. The application of AI in medicine has been researched and practised for many years. In clinical medicine, prediction of disease risk, diagnosis of pathologies and abnormalities, detection of disease, treatment planning, and prognosis of disease have played an important role. The application of artificial intelligence is becoming a reality and part of day-to-day human life.”
Dr Sarvesh Mishra is a GP that works as Medical Consultant at Cogito, and harnesses his NLP data annotation expertise to provide high-quality training data to automate clinical processes for improved healthcare delivery and diagnostics. Sarvesh says “we combine human intelligence with automated applications to deliver accurate training data and accelerate the time to market healthcare space with model, process and application-specific medical AI”.
Some of the data annotation capabilities include:
- Image annotation (e.g. X-rays, MRI and CT scans) where the model is trained to identify specific anatomy and pathology
- Video annotation (e.g. laparoscopic surgical videos) where the model is helped to identify important surgical landmarks that can aid in performing robotic surgery with high precision
- Audio annotation (e.g. heart sounds annotation) where the models can be trained for specific sounds, both normal and pathological
- Waveform annotation (e.g. E.C.G annotation) where the model can be trained to identify various medical conditions on the basis of waveform pattern
- AI medical coding (e.g. I.C.D, SNOMED, Rx Norm) not only for medical billing and insurance claims but also for research purposes and basic medical record-keeping for patients
- Text annotation (e.g. medical summary, conversations, and medical reports) where the data is classified into various medical entities (N.E.R) and relationships are established between them as a part of ontology linking
Data labelling is not only important for the development of medical AI but also for ensuring ethical considerations are met. Privacy is a critical issue, and patient data must be handled with care. Data labelling companies must ensure that they have strict protocols in place to protect patient data and ensure compliance with regulations like HIPAA.
Without high-quality, accurately labelled data, AI systems in the medical industry cannot function effectively and as the demand for medical AI continues to grow, so too will the need for high-quality data labelling services.
This fascinating topic of data in healthcare, along with others will be discussed at the annual AIMed Global Summit, scheduled for June 4-7th 2023 in San Diego. Book your place now!
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