Sergey Mashchenko is the CEO of Light IT Global, an international IT company specializing in HealthTech, FinTech and EdTech.
By the end of 2024, artificial intelligence (AI) and machine learning (ML) had established themselves as the main transformative forces behind recent technological advancements in healthcare. A report by Silicon Valley Bank states that in 2024, the amount of VC investment in health AI in the U.S. was expected to reach $11.1 billion, the highest number since 2021.
In my experience, the main driver behind the AI investment and adoption craze is the measurable value technology offers healthcare providers. A 2023 National Bureau of Economic Research study indicates that integrating AI can save the U.S. healthcare system up to $360 billion annually. A 2023 survey by the AMA shows that physicians see AI as a way to reduce the administrative burden of documentation (54%) and improve workflow efficiency (69%).
But do these positive changes reflect on the quality of care, and do patients benefit from AI and ML-powered solutions? In this article, I share my take on the transformative potential of AI and ML in the modern care delivery process.
Simple Solutions For Complex Matters
Healthcare systems are known to be intricate and overcomplicated due to their multifunctionality. They require constant attention and a significant portion of manual labor, inevitably reducing staff efficiency and patient outcomes.
Introducing AI and ML-based tools proves their usefulness in helping solve that problem. Some of the most common AI and ML applications in healthcare include:
Automation
AI and ML-enabled tools can help with time-consuming tasks such as scheduling, billing, document flow management and digital communication. In our work, we’ve seen how advanced AI and ML functionality can have a significant positive impact on the practitioners’ workflow. Enhanced data analytics and automated report generation help medical professionals spend less time working with documentation, easily keep track of all the patients and swiftly manage patients’ EHRs. As a result, healthcare processes become less demanding for the provider and more patient-centric.
Personalization
AI can be used for individual treatment plan generation and drug interaction checks, as well as precise dosage calculations for each patient.
For example, the introduction of ML helped us implement a complex calculation algorithm created by medical professionals into a custom hormonal dosage calculator. The system automatically analyzes millions of diverse parameters related to the patient (e.g., age, gender, existing conditions and medical history) and generates the dosage appropriate for each individual. All that’s required from a medical professional is the relevant data input.
Diagnostic Support
AI and ML-based solutions can provide quick data analysis, precise anomaly detection, detailed risk assessment and evidence-based recommendations.
Security And Compliance
In healthcare, maintaining patients’ confidentiality and protecting their data are paramount. AI can help with real-time security monitoring, unauthorized access prevention, compliance-related activities and more.
For example, one of the requirements for a medical platform and custom hormonal dosage calculator we developed was full HIPAA compliance. ML enabled us to incorporate extra security measures into the system, including data encryption, SSL/HTTPS traffic protection, two-factor authentication, a profile autolock after five failed login attempts and more.
User Experience Improvement
Although AI will never replace physicians, it can offer simple interactions via chatbots and virtual assistants, 24/7 accessibility to digital care and greater flexibility with telemedicine solutions.
Here’s where the transformative potential of AI and ML kicks in—all these improvements can be achieved by a healthcare business both separately and as a balanced whole. It all depends on the provider’s needs and their strategic vision.
Some businesses aim to resolve one specific problem (e.g., automating claims processing or reducing the time PCPs spend filling in documentation). Others are looking for a custom, all-in-one solution. As the CEO of an international IT company, I can assure you that both requests are executable if the IT vendor knows their way around AI and ML. There’s no need to entirely replace existing business practices with new AI and ML-based ones (unless that’s what you want to do). Subtle, more prolonged transitions are as effective and have far fewer risks.
How To Become A Real-Life Success Story
Here are my recommendations to help healthcare organizations willing to adopt AI and ML-powered solutions succeed.
Prioritize cooperation.
Keep in mind that AI and ML integration requires efforts not only from you as a business owner and the IT department but also from lawyers and healthcare professionals. Every step (from ideation to post-launch solution monitoring) should be supervised by tech, legal and medical experts in equal measures.
Set up and track KPIs.
For each project, key performance indicators will be different, but it’s vital to define them in advance and control whether or not AI and ML incorporation helps you achieve the improvements you were hoping for.
Invest in training.
Staff’s inability to use AI and ML-based tools effectively is one of the biggest adoption challenges. Dedicate time to team training to prevent mistakes and a decrease in motivation.
Take time with development.
Both startups and well-established healthcare market players may feel the urge to integrate AI and ML as quickly as possible before it’s too late. However, a thorough analysis and the ability to accurately determine areas that require AI and ML support are essential for the success of the implementation.
Conclusion
I firmly believe that the modern healthcare industry can greatly benefit from the AI boom we’re experiencing. Yet, staying mindful of the technological changes you’re introducing to your business is more important than the potential short-term rewards of a hit-and-run strategy.
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