AI in Healthcare: Facilitating Greater Possibilities in Medical Sciences
Technology and medicine are starkly different fields, but they are intricately intertwined, with the latter being more dependent on the former. Even the latest tech “bigwigs” like Artificial Intelligence and Machine Learning are becoming more prevalent in healthcare.
AI/ML models trained on customized data make it easier to predict diagnostic results with greater accuracy in various healthcare scenarios, thanks to their algorithms. Today, AI in healthcare is a prolific market valued at around one billion US dollars as of 2016. Estimates predict this value will exceed $28 billion by 2025. Globally, the market for AI in medical imaging alone takes a hefty chunk of this value. The current figure stands at around $980 million and is projected to rise at a CAGR of 26.77%, which is a whopping $3215 million by 2027.
In this scenario, researchers are exploring opportunities to implement AI into medical imaging. This technology can open the doors to precision diagnosis for cardiac, thoracic, and neurological issues. It can also improve medical screenings to simplify the assessment of patient risk factors and reduce doctors’ workload.
Medical Image Annotation: A Remarkable Technology
Medical image annotation involves labeling imaging data like MRI, CT scans, ultrasounds, text-based medical records, etc. The annotated data is then used to train ML models with deep learning algorithms for more accurate medical diagnosis. Also, a valuable ML model can only be developed with accurately annotated text, notes, and metadata.
The most commonly annotated medical documents include:
- Medical images and X-rays
- CT scans and ultrasounds
- MRI and mammograms
- Medical videos and photos
- Physician dictation audio
- DICOM
- EEG
- NlfTI
- EHR dataset
Artificial Intelligence holds great potential for the medical field. It unleashes many prospects for the healthcare industry in North America and across the world to provide quicker, more accurate, and more reliable diagnoses.
Medical Image Annotation: How it Helps Improve Healthcare
1. Brain injury diagnosis
When trained with precise annotated images, ML models can detect brain tumors, blood clots, and other neuro-related conditions. AI facilitates neuro-imaging by properly annotating and feeding data about brain injuries into CT scans and MRIs. Once the model is fully trained, it will, one day, replace our radiologists and make medical imaging easy.
2. Cancer detection
Deep learning models can be pre-trained with cancer image data that has been labeled to accurately predict cancer cells. Once trained, the model can recognize abnormal regions in new image data and can effectively aid in early cancer detection, eliminating the need for human judgment and possible errors. It can also predict if a person is healthy or suffering from undetected cancer.
3. Liver ailment diagnosis
Usually, doctors assess and characterize liver images on ultrasounds or CT scans to diagnose the occurrence of liver diseases. In such cases, there are possibilities for inaccurate diagnosis due to an unintended bias that could stem from their experience. Medical image annotation eliminates such inaccuracies by training AI models to perform quantitative assessment rather than qualitative reasoning. This enables the model to produce a more accurate and unbiased imaging diagnosis.
4. Kidney stone detection
The use of AI for kidney-related ailments is yet to attain significance, although the technology is currently used for:
- Diagnostic guidance
- Prognoses evaluation
- Guiding in treatments
- Alerting mechanisms
However, the prospects of the AI model diagnosing kidney failure in the near future are very much possible. It will likely happen when the algorithms have access to the appropriate annotated data sets.
5. Fracture detection
X-ray images be used to train models to visualize the bone structure. They can then be annotated to identify fractured areas. This annotated data is fed to the model, which is trained to accurately detect and predict bone fractures.
6. Eye cell analysis
Eye scans are a great tool for doctors to detect a wide range of eye and retinal complications. Using the right AI techniques, it is possible to annotate the visible eye-related symptoms to enable accurate diagnosis of ocular diseases, cataracts, and other ailments.
7. Dentistry
AI-enabled models will become very handy for dentists to diagnose structural abnormalities of the teeth, deep-rooted cavities, gum-related issues, and other dental diseases.
8. Pathology
Medical imaging annotation improves pathologists’ ability to diagnose tumors and other abnormalities by leveraging deep learning algorithms trained on massive datasets of medical records. This timely diagnosis will considerably reduce the time for patients to receive medical care for the condition.
How to Ensure HIPAA Compliance With AI-based Models
In the USA, anything related to medicine should be HIPAA-compliant. The same goes for medical image annotation because AI models are trained and tested on massive volumes of annotated medical images.
What is HIPAA?
Known as the Health Insurance Portability and Accountability Act of 1996, HIPAA is a federal law governing the safety of electronically transmitted health information. According to HIPAA, healthcare providers must protect patient information from being disclosed without consent.
Hence, it is imperative to choose an AI model training platform that ticks the following criteria:
- Has a system for healthcare information and storage management.
- Stores, maintains, and updates backups for all systems.
- Prevents unauthorized access to sensitive medical data.
- Ensures data encryption during rest and transfer.
- Prevents users from exporting and storing medical images on personal devices.
Let’s Wrap Up
Training AI models on accurately annotated datasets will enable groundbreaking progress in the field of medicine. In turn, it will simplify diagnosis and disease identification to promote early intervention.
As a reputed AI model training company in North America, Opporture offers high-quality data annotation services to support many US-based clients in the healthcare industry with medical image annotation. Want to know how we leverage our advanced AI technology for medical image annotation? Give us a call, and let’s discuss.
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