How can AI help improve accessibility to healthcare?
Innovation in artificial intelligence (AI) and machine learning (ML) in healthcare has grown exponentially in recent years. Around 80% of doctors believe that AI in healthcare is useful and it is already being used in exam rooms by triaging hospital and emergency department traffic, analysing patient risk scores and identifying potential new therapies by simulating chemistry with computers. Even though there have been tremendous advancements in healthcare and AI, challenges around accessibility and affordability remain. With this in mind, how can AI help improve accessibility to healthcare?
Addressing affordability issues in healthcare
According to the U.S. Census Bureau, during the onset of COVID-19 in 2020 28 million American citizens didn’t have health insurance at any point during the year. Although many did, there are still some treatments and checkups that aren’t covered by insurance, such as follow up breast cancer screenings and mental health services, making them expensive for individuals. This is where AI has the ability to provide quality healthcare options at a lower cost, improving accessibility to all.
For example, Vara is an AI-powered mammography screening platform offering a software screening service that can be installed on existing machines without requiring hospitals or healthcare companies to invest in substantial new equipment. In clinical trials in Germany, founder and CEO Jonas Muff, claimed that Vara found roughly 40% of all cancers that were missed by the radiologists. Promising innovations like this one could provide low-cost healthcare options, helping to improve efficiency and accessibility to some services that would otherwise be expensive.
Personalising treatments and diagnosis
Similarly to breast cancer screenings, mental health care and treatments aren’t usually covered by insurance in the US. Although these treatments are available on the NHS in the UK, there are still very long wait times making these types of treatments difficult to access for those who can’t afford to see a private practitioner. This is where AI could help to bridge the gap.
For example, Paradromics aims to develop a data interface that directly interacts with neural signals from the brain using AI and machine learning. Although the technology isn’t yet commercially available, Paradromics’ goals include applications that focus on detecting and treating intractable mental illnesses. The devices would be surgically implanted to function and would likely be used therapeutically once a condition has been diagnosed. Innovations like this one can help improve accessibility to treatments that are otherwise expensive or have long wait times.
Using data to improve accessibility
Data is everywhere in today’s world and in healthcare patient data can be the difference between life and death. AI and ML can help to organise this data and obtain better insights and improve accessibility. For example, AI could help to increase vaccine equity by extracting information from social media, clustering it into user-defined groups and presenting key insights across the population. Organisations can then improve their understanding of public perception of infections and vaccines and tailor educational efforts to improve vaccine rates.
When implemented and used correctly, AI and ML have the potential to lower the cost for some treatments and services, provide personalised treatments and diagnosis and analyse patient data efficiently, increasing accessibility across the board. Already, AI-driven health solutions have proven more efficient and have become more effective. However, the challenge remains in scaling up these technologies and building trust around AI. Once these issues are addressed AI and ML will have the power to significantly improve accessibility to healthcare globally.
In a fast-evolving technology landscape like digital health, innovators need to ensure they stay ahead of the curve and keep track of their competitors. Minesoft’s global patent database, PatBase, allows organisations to gain a bird’s eye view of a technology landscape, spot areas of innovation and keep track of competitors patent portfolios in order to make strategic business decisions. With innovativeness at the forefront of what we do, using AI and ML is key to improving patent searchability… watch out for upcoming announcements!