AI and Medical Imaging: Enhancing Accuracy and Efficiency in Diagnosis

Importance of AI in medical imaging

Medical imaging is a technique that uses X-rays, gamma rays, waves or any other ionising and non-ionising radiation to create images of the inside of the body, allowing us to see internal organs, bones, and tissues. The application of artificial intelligence in medical imaging continues to advance, especially its ability to detect abnormalities and tissue changes. Early signs of diseases can be found with the help of AI algorithms. In this way, the risk of missed diagnoses can be reduced, and more consistent patient treatment can be achieved.

The most common techniques are:

• X-ray
• MRI (Magnetic Resonance Imaging)
• CT scan (Computed Tomography)
• Ultrasound
• PET scan (Positron Emission Tomography

Role of machine learning in medical imaging

Machine learning (ML) is the technology that primarily teaches the empty model to make decisions or predictions based on data that has such knowledge (historical data, previous decisions, labelled content, etc.). For example, you can predict some value in the future based on the series of historical values, classify images based on image labels set by humans in advance, and generate text based on huge corpora (books, forums, articles) seen by the model.

Traditional machine learning relies more on clearly defined features and rules created by data scientists, while deep learning uses complex neural networks to automatically learn from large amounts of imaging data.

Machine learning techniques are often easier to understand and interpret, making it simpler to explain why a certain diagnosis or prediction was made. This helps doctors trust the results and make better decisions. Machine learning also works well with smaller datasets and can be combined with other patient information, offering practical tools to improve clinical care alongside deep learning methods.

Deep learning techniques in medical imaging

Deep learning (DL) is the technology that focuses on the Deep Neural Nets (MLPs, CNNs, RNNs, Seq2Seq, GANs, Transformers, Diffusion models, KANs, etc.) usage as the primary models, but for the same tasks from the forecasting and ending with the text generation.

Deep learning has made major progress in recent years due to the ability to learn complex patterns and features from raw data. In healthcare, DL systems can also check for mistakes or unusual patterns in medical data. They can even verify whether medical images are real or have been tampered with—an important feature in the digital transformation era. DL can also predict how a disease might progress or how a patient may respond to treatment.

Deep learning uses different neural networks for many medical imaging tasks:

• Convolutional Neural Networks (CNNs) learn features from images using layers that detect patterns, reduce data size, and classify content, making them highly effective for tasks like image recognition and medical image analysis.
• Recurrent Neural Networks (RNNs) are deep learning models designed to process sequential data. They use internal memory to retain information from previous steps, making them ideal for evaluating a series of images from slices (MRI) or from different timeframes.
• Generative Adversarial Networks (GANs) generate realistic data by training two networks, a generator and a discriminator in competition. The generator creates fake data; the discriminator tries to detect if it's real or fake. In imaging tasks, GANs are used for synthetic data generation and image enhancement.

Adopt machine learning solutions

Integrate digital technologies

Current challenges in medical imaging

1. Low-quality medical images

Medical images can sometimes suffer from reduced quality due to various factors: the skill and experience of the operator, patient movement or positioning, and specific imaging conditions.

AI solution

AI learns by looking at many pairs of blurry and clear versions of the same picture. By studying these pairs, it figures out how to turn a blurry image into a clearer one. After learning this, the AI can make low-quality images sharper, improve their detail, and keep important things like tumours or small changes in tissue visible.

2. Radiologist shortage and diagnostic delays

Modern medicine uses more imaging tests than ever before. The delay is typically caused by the chain of doctors' reviews (diagnose/radiology technologist is different from pure radiologist and therapist). So, according to medical protocols, the diagnosis is provided by different doctors with different qualifications. As a result, patients may wait a long time for results, even when every minute is critical.

AI solution

AI tools can quickly scan medical images and spot signs of serious problems like bleeding or tumours. When they find something dangerous, they can automatically send it to a doctor for an urgent review.

This helps doctors deal with the most critical cases first and shortens the time patients wait for answers in emergencies.

3. Variability in interpretation

The interpretation of medical images depends on the experience and knowledge of radiologists. Even experienced doctors can evaluate the same images differently; some may spot small lung nodules that others might overlook.

AI solution

Artificial intelligence analyses medical images objectively in a standardised way. AI algorithms identify suspicious areas and act as a “second reader”. This helps make image interpretation more consistent and:

• Identify abnormalities that a radiologist might have missed
• Provide exact measurements to support and enhance visual evaluations
• Ensure consistent and accurate results, regardless of fatigue or workload

4. Data fragmentation and privacy concerns

Medical imaging data is scattered across many hospitals and stored in different formats. Strict privacy regulations (like HIPAA and GDPR) restrict the sharing of patient data. This fragmentation makes it difficult to develop accurate AI models, which need large and diverse datasets.

Generalised approach

AI processes data at each hospital without sharing private patient information. Only the AI’s updates are shared, keeping data safe while learning from many sources. Supporting this secure approach, ELEKS has earned HITRUST e1 Certification, proving its ability to protect healthcare data and deliver compliant solutions like Compliance Automation Platform (eCAP), trusted by U.S. healthcare providers.

5. Challenges in AI implementation

AI models may produce false positives and hallucinations or exhibit bias, which can compromise the reliability of their outputs. As a result, their performance must be carefully verified and validated with subject matter experts (SMEs) to interpret results and check if the model's decisions align with clinical or domain-specific standards.

As AI becomes more integrated into diagnostic imaging, it helps address key challenges in the field. However, this sensitivity also brings a challenge—the technology may detect subtle changes that doctors don’t think are serious. For instance, AI systems might flag tiny lesions in a scan that a radiologist will typically ignore. These findings, sometimes referred to as "incidentalomas," can lead to overdiagnosis and unnecessary follow-up tests, biopsies, or treatments, increasing patient anxiety and burdening healthcare systems.

Conclusion

Despite some challenges, AI in healthcare shows promising results in medical imaging and a strong potential to transform the field. It is important to have teamwork between doctors, AI developers, and regulators to tackle ethical and practical issues while making the most of what AI can offer.

AI also helps improve the speed and accuracy of diagnoses, treatments, personalisation, and how care is delivered overall. It also makes results clearer and easier to understand and supports better communication among healthcare teams.

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