How AI is improving diagnostics and health outcomes, transforming healthcare

The healthcare sector is poised for a revolution, with artificial intelligence (AI) playing a pivotal role in transforming how we diagnose and treat diseases.

AI’s ability to process vast amounts of data quickly and accurately is reshaping the landscape, offering new ways to improve health outcomes globally.

However, building effective and accurate solutions that are culturally sensitive and inclusive requires collaboration across the plethora of needed skill sets. The result is better and faster access to health solutions.

AI’s potential in healthcare is vast, ranging from predictive analytics and personalized treatment plans to improving diagnostic accuracy. The sheer volume of medical data generated daily – from patient records, clinical trials, medical imaging and wearable devices – can be overwhelming for human practitioners.

As AI can analyze and learn from this data, patterns and correlations that may go unnoticed by human eyes can be detected. This leads to earlier and more accurate diagnoses, which is crucial in improving patient outcomes.

One of the most promising areas of AI in healthcare is in diagnostics. Traditional diagnostic methods often rely on the subjective interpretation of medical professionals, which can sometimes result in variability in outcomes. AI can reduce this variability by providing consistent, data-driven insights, leading to more reliable diagnoses.

Collaboration in AI development is essential for creating effective, ethical and impactful solutions to the world’s most pressing challenges, whether that includes AI engineers, data scientists and domain experts. “Such kind of collaboration across people from different sectors and regions help build more ethical and trustworthy AI solutions,” says Rudradeb Mitra, CEO of Omdena, a collaborative AI platform.

In the healthcare sector, Omdena applies AI to diagnostic processes, particularly in underserved communities. These models assist medical professionals in identifying diseases more accurately – such as tuberculosis and diabetic retinopathy – and at earlier stages, thereby increasing the chances of successful treatment.

These projects involve training AI algorithms on large datasets of medical images. Taking a collaborative approach ensures that the AI models are robust, culturally sensitive and adaptable to various healthcare settings.

The World Economic Forum is also driving digital healthcare transformation by fostering a global, multi-stakeholder approach to accelerate the responsible integration of digital health solutions into healthcare systems worldwide.

By collaborating with governments, businesses and civil society, the Forum focuses on improving healthcare access, outcomes and equity through innovative technologies while addressing challenges such as data privacy, cybersecurity and ethical use of AI in healthcare.

By integrating AI models into healthcare systems, particularly in regions with limited access to specialized medical professionals, we can democratize healthcare and ensure that more people have access to early and accurate diagnoses.

Early diagnosis is often the key to successful treatment. Diseases like cancer, for example, have a significantly higher survival rate when detected early. When breast cancer is detected at stage one, the five-year survival rate is over 90%. Colorectal cancer yields a 14% five-year survival rate when diagnosed in later stages but can be around 90% with early diagnosis. Therefore, the impact of AI-driven diagnostics on health outcomes cannot be overstated.

“This not only improves patient outcomes but also makes healthcare more accessible to all and reduces the burden on healthcare systems by preventing the progression of diseases to more advanced stages, which are often more difficult and expensive to treat,” says Mitra.

For example, Omdena's AI-powered app in Liberia predicts malaria outbreaks and identifies high-risk areas, enabling health officials to take proactive measures, particularly for vulnerable groups such as children and pregnant women.

AI is also transforming oncology by enabling personalized cancer treatments through genomic analysis, identifying mutations and tailoring therapies to a patient's unique cancer profile. This approach improves treatment efficacy, reduces side effects and accelerates drug discovery.

AI has also been used in the early detection and prediction of cardiovascular diseases. Machine learning is used to analyse ECGs, medical imaging, and patient data. With studies having shown impressive accuracy rates, including of heart disease classification of 93%, AI is improving diagnostics and offering noninvasive methods for assessing cardiovascular risks, potentially saving lives.

As AI continues to evolve, its role in healthcare will only become more critical. The ability to predict, diagnose and treat diseases with greater accuracy and efficiency will lead to healthier populations and more sustainable healthcare systems.

We at Omdena have seen how important collaboration and innovation are in realizing AI’s full potential in healthcare.

However, the mission is not just to build AI models; it’s about creating solutions that are inclusive, equitable and capable of making a real difference in people’s lives.

By leveraging the power of AI for diagnostics, we can improve health outcomes and contribute to a future where healthcare is more accessible and effective for everyone, particularly in the communities that need it the most.