Alexey Bashkirov, a private investor and founder of the Donum Foundation — an initiative dedicated to supporting medical education — shares his insights on the evolving role of artificial intelligence (AI) in healthcare. With AI-driven technologies gaining momentum across industries, healthcare has emerged as one of the most promising areas for transformation.
AI in Healthcare Today: Where We Stand
According to Bashkirov, AI is already making meaningful contributions in HealthTech by:
- Simplifying and interpreting complex medical data
- Supporting real-world evidence research
- Enhancing patient care through CRM systems and digital tools
However, despite notable progress, Bashkirov points to a persistent gap between the high expectations of investors and the actual financial performance of many digital health ventures. He cites the 2023 bankruptcy of Babylon Health — a company that raised billions to reinvent primary care — as a cautionary tale.
The Long-Term Promise of AI in Medicine
Bashkirov remains optimistic about AI’s long-term potential in healthcare. Consider these projections:
- Generative AI in healthcare is expected to grow at an annual rate of 85%
- AI-driven neural networks could contribute to the development of up to 30% of new drugs
- McKinsey estimates suggest generative AI might improve clinical trial success rates by 10%, while reducing costs and timelines by 20%
Still, Bashkirov urges caution when it comes to over-reliance on AI for drug discovery, citing the human body’s extraordinary complexity as a key challenge.
Why Data Is AI’s Greatest Strength
In Bashkirov’s view, the most immediate and valuable use of AI lies in data management. Tools like IBM’s watsonxshowcase the capabilities of generative AI to:
- Automate repetitive workflows
- Manage large datasets
- Improve operational efficiency across departments, including customer support and HR
In clinical settings, AI can analyze vast volumes of medical data, recognize patterns, and support diagnostic accuracy. One milestone came in 2023 when the FDA approved an AI tool capable of autonomously diagnosing diabetic eye disease.
The Road to Widespread Adoption: A Slow but Steady Climb
AlexeyBashkirov invokes Amara’s Law, which says we tend to overestimate the short-term effects of technology while underestimating its long-term impact. Much like the early days of the internet, he believes AI in healthcare will see many false starts before reaching its true potential — likely over the course of several years.
Challenges Slowing Down AI Implementation
Adopting AI in healthcare isn’t as straightforward as in fintech or government services. HealthTech demands deep sector expertise and significant capital, which makes private investors hesitant. The last major wave of funding — in telemedicine — peaked over five years ago, and many investors remain cautious due to unclear ROI and scalability.
Public Sector’s Role in Accelerating AI in Healthcare
Bashkirov emphasizes the importance of government support in driving AI adoption. In some countries, for example, key enablers include:
- Centralized outpatient care systems
- Modern urban hospital infrastructure
- A Ministry of Health-led push for digitization
- Unified platforms for patient data
He also notes that major tech companies like Yandex and Sberbank are well-positioned to lead large-scale HealthTech initiatives.
Large Language Models: A Game-Changer in Healthcare
Mr. Bashkirov sees significant promise in large language models (LLMs), especially in:
- AI-powered patient interaction tools
- Structuring unorganized medical data
- Real-time voice transcription and analysis during consultations
If deployed effectively, he believes these tools could help attract more private investment into AI-powered healthcare solutions.
Final Thoughts: Unlocking AI’s Potential in Medicine
Alexey Bashkirov concludes that while AI will undoubtedly revolutionize healthcare, the path forward requires a balanced approach — combining public sector vision, private innovation, and patience. Success, he argues, lies in long-term thinking and collaborative execution across the healthcare ecosystem.
