Introduction: AI in medicine

Healthcare is changing significantly because of new technologies like 3D printing, robots, and artificial intelligence (AI). AI is the computer’s ability to perform functions associated with human intelligence, such as learning and arriving at conclusions, which is especially helpful in finding patterns in enormous amounts of data. In medicine, AI is making a big difference by:^1,2,3

• Processing patient information and records 
• Speeding drug discovery 
• Identifying patient risk 
• Helping with the diagnosis of different types of diseases

Importance of early disease detection

If not properly treated, conditions like heart disease, diabetes and other chronic diseases can damage organs such as the eyes, brain, heart, and kidneys, causing serious problems that affect everyday life. In addition, chronic diseases are responsible for most of the deaths worldwide, and they cost a lot to treat.⁴ Therefore, early diagnosis is essential to slow down the progression of the disease and motivate high-risk patients to change their unhealthy lifestyles, which reduces complications and further improves their health and quality of life.^4,5

Because chronic diseases start without noticeable symptoms, it's hard for doctors to figure out who's at risk of developing them.⁴ On the other hand, AI scans plenty of data from mammographies, ultrasounds, magnetic resonance imaging (MRI), computed tomography scans, etc to find patterns and give us useful information quickly and accurately. Therefore, this technology can help identify several diseases, decrease errors in diagnosis, optimise treatment plans, and improve patients' results.^1,3

How can AI be used in early disease detection?

Machine learning algorithms

Machine Learning (ML) helps computers complete tasks like medical professionals. One important step in ML is data preprocessing, where data is cleaned up to reduce mistakes and make things more efficient. Then, important features are picked out and used to train the ML model to recognise patterns in medicine, which helps the model work better. Finally, the model is trained and adjusted to make sure it can make good decisions and predictions.²

Deep learning techniques

Deep Learning (DL) is the development of ML to analyse a large amount of data, to achieve that, simple concepts are put together in layers to create a complex structure. It's like building a tower with many blocks. DL uses an automated strategy to figure out which features are important by looking at millions of examples. DL has been around for a while, but its popularity has increased because we have more data and better computers.²

DL has done better than older methods in recognizing determined items in pictures and classifying a huge number of images, improving with each use. In healthcare, DL can analyze images from X-rays, MRIs, ultrasounds, computed tomography, microscopy, and scintigraphy to help diagnose diseases more accurately and efficiently, and even predict disease.²

Natural language processing

Natural language processing (NLP) is the term used by computers to understand human speech or text. In the past, NLP used strict rules to work, but with advancements in AI, NLP has significantly improved. Some AI methods can even spot patterns that humans might miss and go through large amounts of data faster. For example, electronic medical records store a large number of private patient information, but most of it is in text form and not structured, like doctor's notes, with a small amount of recorded structured data, such as patient demographics, vital signs, and lab tests. The amount of text for each patient is excessive so using computers to process it can help doctors work more productively.⁶

Examples of disease detection using AI 

Cancer detection

AI has a big impact on cancer detection by using data from various sources, like medical images and patient records, to predict diseases and help doctors make decisions.¹ Imaging is very useful in finding cancers like colon, breast, and lung cancer early as well as screening for cancer in people without symptoms which can save lives. However, using AI in cancer detection can be expensive and can lead to unnecessary treatments.⁷

AI algorithms are already being used to help detect breast cancer in screening tests and such algorithms are doing as well as or even better than radiologists in spotting cancer in mammograms. AI can also help radiologists sort through normal images, making their job easier and more accurate. This is helpful when there aren't enough radiologists available.⁷

For lung cancer, AI can help find suspicious nodules on scans and predict if they might be cancerous. Some AI systems are even better than radiologists at this. This is important because it can make lung cancer screening more cost-effective and help more people get screened.⁷

Diabetes detection

Diabetes is a major health issue that can lead to complications such as eye problems, which include blindness. AI can help prevent such complications by detecting it in an early stage. When analysing past and current patient history, some methods could predict blood sugar levels by using data such as:¹

• Body Mass Index (BMI)
• Stress
• Illness
• Medications 
• Amount of sleep 

Heart diseases

Tests like electrocardiogram (ECG) are often used to diagnose heart problems. It is a simple and cheap test that is available in many places, even where resources are limited. It's been used for a long time to help diagnose heart problems, but how well it works depends on the doctor's experience. ECG results have a lot of data that can be hard for doctors to fully interpret, but AI can help by better inspecting the data and finding crucial information that might not be obvious to humans. This can help detect specific heart problems more accurately.⁸

Neurological disorders

Machine learning algorithms can help accurately diagnose a disease by looking over various data, such as brain tissue images, electronic medical records and speech patterns, even in its early stage. Similarly, AI can also help detect signs of stroke and cerebrovascular disease by exploring medical images. Researchers have developed methods to quickly detect strokes in patients, leading to better monitoring and treatment. Additionally, AI systems can locate and estimate carotid plaque, which helps diagnose atherosclerosis-related conditions.¹

Challenges of AI in early disease detection

Although AI has made significant advances in disease diagnosis, it still faces several challenges:^1,3

• Ethical and legal frameworks: Implementing AI raises concerns about patient privacy, data protection, and liability
• Limited data size: Many studies struggle with small sample sizes, which affects the accuracy of AI models. Having more data to train on generally leads to better results
• High dimensionality: Some diseases have a vast number of features to consider, which can overwhelm AI models. Techniques to reduce the complexity of data help address this challenge
• Efficient feature selection: While some AI models perform well, they may not be computationally efficient. Finding methods to select the most relevant features improves prediction accuracy without unnecessary data processing
• Clinical implementation: While AI models show promise in research, they require validation in real clinical settings to be useful for healthcare practitioners
• Model generalizability: AI models are often tested on data from a single source, limiting their applicability to other settings. Validating models across multiple sites can improve their reliability
• Contextual bias: AI systems developed in high-income countries may not be suitable for low- and middle-income countries due to differences in healthcare contexts

Summary

The integration of AI into healthcare, particularly in disease detection, has shown significant promise in improving accuracy, efficiency, and patient outcomes.^1,2,3 Early disease detection is crucial for conditions like cancer, diabetes, heart disease, and neurological disorders, as timely intervention can prevent complications and improve treatment outcomes.^4,5 In this context, AI algorithms play a pivotal role in analysing vast amounts of medical data, including images and patient records, to identify patterns and assist in diagnosis.^1,3

In cancer detection, AI algorithms go through imaging data to detect tumours early.⁷ In diabetes, AI uses patient history data to predict blood sugar levels, which allows for early intervention to prevent complications like blindness.¹ For heart diseases, AI enhances the interpretation of tests like ECGs, leading to more accurate diagnosis.⁸ Similarly, in neurological disorders, AI algorithms look into brain tissue images, electronic medical records, and speech patterns to aid in early diagnosis.¹

Despite its potential, AI in early disease detection faces challenges such as ethical and legal concerns, limited data size, high dimensionality of diseases, and contextual bias, particularly in low- and middle-income countries where healthcare contexts may differ. Addressing these challenges requires a multifaceted approach, including the development of robust ethical and legal frameworks, the expansion of data resources, and the validation of AI models in diverse clinical settings. By overcoming these hurdles, AI has the potential to transform early disease detection and improve healthcare outcomes for patients worldwide.^1,3