Kesava Reddy, Chief Revenue Officer, E2E Networks highlights that traditional imaging techniques face difficulties in detecting real-time change in a patient’s condition. AI/ML technologies can be beneficial for tracking the patient’s condition and detecting even the smallest change in vast amounts of information
Medical imaging in radiology has come a long way, and the latest Artificial Intelligence (AI)-driven techniques are exploiting the massive computing abilities of AI and machine learning to analyse X-rays, MRIs, CT scans, and ultrasound images for differences that the human eye can miss.
According to GlobalData, the medical imaging market is expected to reach global sales of $31.9 billion in 2023 and increase to $45.8 billion by 2030.
Need for deep learning in healthcare
Deep learning, a subset of Machine Learning (ML), has emerged as a game-changer in medical imaging. Deep learning models, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), are designed to process complex data, including images, in a manner that mimics human neural networks. CNNs have been exceptionally successful in image analysis tasks due to their ability to automatically extract hierarchical features from images.
It is important to remember that these advances in ML come at a time when post the COVID-19 pandemic, healthcare companies are catching up with a significant backlog in imaging-related demands. Also, globally, there’s an increase in the number of aging patients who need monitoring; and inaccurate or missed diagnosis due to human error or subjective interpretations is a continuing challenge that the healthcare industry faces. Most importantly, however, the key area where AI can help is with detecting problems earlier with improved speed and efficiency.
Areas of impact
Computer vision AI can potentially be used for early detection of cancer, stroke, diabetes and so on, identifying subtle abnormalities in medical images that might escape the human eye, thereby reducing human error. Also, unlike humans, AI systems do not suffer from fatigue and they can maintain consistent performance throughout the day.
Trained computer vision models can be particularly powerful at pattern recognition, particularly useful for cancer treatment. For instance, AI algorithms can analyse lung CT scans to identify early signs of lung cancer, thereby increasing survival rates. The other domains where diagnostic imaging using computer vision AI can be very useful are neurological conditions such as Alzheimer’s disease, multiple sclerosis, and brain tumors and fractures and musculoskeletal injuries like hip fractures in elderly patients.
In cardiovascular diseases like coronary artery disease and congestive heart failure, a trained AI model can assess factors like blood flow and cardiac function. In diabetes prediction, it can analyse blood results. In ophthalmology, it can scan retinal images to detect diabetic retinopathy, macular degeneration, or glaucoma, allowing for early intervention to prevent vision loss.
Improving patient outcomes
Traditional imaging techniques face difficulties in detecting real-time change in a patient’s condition – for example, providing immediate analysis during medical procedures like surgeries or biopsies or determining the percentage of tumor cells that are dead or alive in a cancer patient. AI/ML technologies can be beneficial for tracking the patient’s condition and detecting even the smallest change in vast amounts of information. In critical care scenarios like strokes, these tools can save time in diagnosis.
As AI adoption goes up, it will vastly improve the accuracy of precision medicine. For instance, AI tools can differentiate between different types of lung cancer, or more accurately predict the survival rate of tumor patients based on the measured grade and stage. This will reduce the workload on medical practitioners and can even help overcome the global shortage of healthcare professionals.
Stages of medical imaging
The process of AI-driven medical imaging involves multiple stages, including data collection from existing X-rays, MRIs, CT scans, or ultrasounds; data preprocessing, where before the images are fed into the AI algorithm, they often undergo preprocessing to enhance the quality and remove any noise; and algorithm training, where the machine learning algorithms are trained to identify patterns, anomalies, or specific features within the images that are indicative of particular medical conditions.
Once trained, the AI algorithm learns to analyse new medical images to detect and diagnose different medical conditions. The final interpretation and treatment planning can be done by a medical professional with assistance from the AI system.
Challenges with generative AI in healthcare
One of the most pressing issues is that of ethics and privacy in the use of patient data to feed the LLMs powering the AI. While this data is crucial for the functioning and learning of AI algorithms, it raises questions about data security and patient consent. This is why it is important to launch Medical AI systems using Cloud GPU clusters that are deployed by the organisation, thereby keeping patient data in a secure space, instead of using proprietary AI platforms, where sensitive patient data is shared with an external entity.
Another concern is the limitations and potential biases in AI algorithms. The algorithms are trained on existing datasets, which may not be representative of the broader underrepresented population. This can exacerbate existing healthcare disparities and lead to unequal treatment. Therefore, it is important to constantly improve and evaluate the AI model’s performance.
Moreover, while AI can process information and make recommendations, it does not possess the emotional intelligence that doctors can offer. Therefore, it acts best as an assistant to a trained medical professional, instead of offering answers directly to patients. Being aware of the shortfalls of AI in healthcare is key to effective usage and deployment.
Future of diagnostic imaging with generative AI
The future of AI in medical imaging looks incredibly promising, especially with multi-modal imaging on the horizon – where AI algorithms analyse multiple types of medical images collectively for a more comprehensive diagnosis. As AI technology becomes more reliable, we will see it assist healthcare professionals in making diagnostic imaging more accessible to remote and underserved populations.
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