Alexey Bashkirov, a private investor and founder of the Donum charitable initiative in medical education, shares his perspective on the future of artificial intelligence (AI) in medicine and HealthTech. According to him, interest in AI-driven technologies is rapidly growing across various industries, and healthcare is no exception.
The Current Role of AI in HealthTech
Today, AI-powered solutions are already being integrated into HealthTech to help doctors:
- Process and analyze medical data
- Conduct real-world evidence research
- Utilize CRM systems and other tools to improve healthcare quality
However, despite significant advancements, Bashkirov notes that the expectations of both professionals and investors often fail to align with the actual economic returns from digitalization. He cites the case of Babylon Health, a company that, despite receiving billions in investment, went bankrupt in 2023 after attempting a radical transformation of primary healthcare.
The Potential of AI in Medicine
Despite these challenges, Bashkirov believes AI has reached a level where it can make a significant impact on HealthTech. Industry forecasts suggest:
- The Generative AI market in healthcare is expected to grow by 85% annually
- Up to one-third of new drugs could be developed using AI-powered neural networks
- McKinsey estimates that generative AI could increase the success rate of clinical trials by 10%, while also reducing their cost and duration by 20%
However, Bashkirov remains cautious about an imminent breakthrough in AI-driven drug discovery. He argues that the complexity of the human body makes it premature to rely solely on AI for pharmaceutical research at this stage.
AI’s Strength Lies in Data Processing
Where AI shows the most promise, according to Bashkirov, is in data management and automation. For example, IBM watsonx leverages generative AI to:
- Automate internal processes
- Manage large databases
- Improve efficiency in customer service and HR operations
In healthcare, AI excels at processing large datasets, recognizing patterns, and assisting in diagnostics, but it is not yet capable of creating fundamentally new medical approaches. A recent milestone in this area was the FDA’s 2023 approval of an AI-driven system for diagnosing diabetes-related eye diseases without physician involvement.
Long-Term AI Adoption in Medicine
Bashkirov references Amara’s Law, which states that people tend to overestimate the short-term impact of new technologies while underestimating their long-term influence. He compares AI’s trajectory to that of the internet, initially overhyped but ultimately transformative.
He predicts that in the coming years, more AI startups in healthcare will fail than succeed, and the true impact of AI on medicine will become evident only after several years.
Challenges in AI Integration into Healthcare
Assessing the real potential of AI in healthcare is a complex task. Although digital transformation is advancing in areas such as:
- Government services
- Tax administration
- Financial technology innovations
HealthTech requires significant investment and specialized expertise. Private investors remain cautious due to uncertainty around economic returns, and the last major investment wave in HealthTech occurred over five years ago, primarily focused on telemedicine.
The Role of the Government in AI Adoption
Despite this, Bashkirov believes that government initiatives will be the main driver of AI adoption in medicine. Key factors supporting this shift include:
- Centralized outpatient care
- Well-equipped hospitals in major cities
- Ongoing efforts by the Russian Ministry of Health to digitalize healthcare
- The development of unified patient data systems
According to Bashkirov, leading technology companies like Yandex and Sber could play a crucial role, as they possess the resources and expertise needed to implement large-scale HealthTech projects.
The Promise of Large Language Models in Medicine
Bashkirov sees large language models (LLMs) as particularly promising in healthcare. Some of their potential applications include:
- Virtual assistants for patient interaction
- Structuring and analyzing unstructured medical data
- Real-time speech analysis
If these technologies prove effective, private investments in AI-driven healthcare solutions could increase.
Conclusion
Artificial intelligence holds tremendous potential to transform medicine, but its full-scale adoption will require time and a multi-faceted approach. According to Bashkirov, both government programs and private investment will play key roles in shaping the future of AI in healthcare.