AI-driven innovations in medicine: devices, data, and diagnosis Premium

India poised to lead AI-based medical diagnostics with ethical data collection, innovative medical devices, and government support for R&D.

The medical field has always been a beacon of innovation, and the integration of artificial intelligence (AI) has supercharged advancements, particularly through the convergence of devices, data, and diagnosis. India, with its unique strengths, is poised to lead the development of AI-based diagnostic models by leveraging its vast and diverse population to provide high-quality, annotated medical data.

Medical diagnosis often hinges on pattern recognition, whether in chest X-rays, MRIs, or CT scans. Each disease or condition exhibits distinct patterns that clinicians identify to draw conclusions. This process forms the backbone of objective diagnosis. AI has the potential to revolutionise this step by analysing vast amounts of anonymised patient data to recognise these patterns with unparalleled accuracy.

However, the success of AI models depends on the quality and ethical sourcing of data. Ethical data excludes personally identifiable information (PII) such as names or addresses, while still retaining critical demographic details like age and gender, which are often essential for accurate analysis. Additionally, the data must be meticulously annotated. For example, in an X-ray, patterns indicating diseases like pneumonia, emphysema, cystic fibrosis, or cancer need to be clearly marked. With millions of such annotated data points, AI models can be trained to deliver precise and reliable diagnostics.

The key challenge is data bias. What this means is that in the example of chest X-Ray, if AI can identify around 25 types of patterns (diseases/illnesses), then we need data for these 25 types of patterns in almost equal numbers. Too much data of one pattern and too little data for another can create a learning bias. Getting sufficient data quantity of rare illness patterns is always a challenge. This can impact on the specificity and sensitivity (false positive and false negative results) of the AI model. Thus, getting accuracy beyond 97% is always a challenge with AI models in medical area.

While AI can now generate synthetic data to supplement real-world datasets, even synthetic data creation requires a robust foundation of real, high-quality data. This underscores the importance of ethical data collection and annotation as the cornerstone of AI-driven medical innovation.

The field of medical devices is also undergoing a transformative evolution, driven by advancements in AI. A striking example of this innovation is the stethoscope, a tool first invented in 1816 by French physician René Laennec. Originally designed to allow doctors to listen to heart and lung sounds, the stethoscope has now evolved into a sophisticated digital device that is gaining widespread adoption worldwide.

Modern digital stethoscopes not only amplify sound but also record and store it, creating a rich repository of data that can be used to train AI systems. Traditionally, physicians have relied on their expertise to identify patterns in these sounds, diagnosing conditions such as heart murmurs or aortic stenosis. However, with the integration of AI, this diagnostic process is being revolutionised. By training AI models on annotated sound data, healthcare providers can achieve greater accuracy, efficiency, and consistency in diagnosing cardiovascular and respiratory conditions.