If you’ve been following the latest global developments, you already know how artificial intelligence (AI) is transforming multiple industries, and healthcare is no exception! You’ve likely seen AI-driven chatbots assisting patients, predictive analytics improving diagnostics, and automation streamlining workflows. But is AI truly solving healthcare’s biggest challenges or are we just scratching the surface?

At the Smart Health Transformation Forum during the Healthcare Information and Management Systems Society (HIMSS) Global Health Conference & Exhibition 2025, Matt Cybulsky – a healthcare leader for AI, go-to-market, and design at Ionian Consulting – explored this very question. He highlighted both the opportunities and limitations of AI in healthcare, urging professionals like you to think critically about its role in the industry.

The Demand for Healthcare is Real!

If you’ve noticed longer wait times for doctor appointments or heard about physician shortages, you’re not alone. These aren’t just temporary setbacks – they’re symptoms of a much larger crisis in healthcare.

The physician shortage in the U.S. is an ongoing trend, and the numbers are alarming. According to the Association of American Medical Colleges (AAMC), the U.S. could face a shortage of up to 86,000 physicians by 2036. This growing gap means longer delays in care, overworked healthcare providers, and more patients struggling to get the medical attention they need.

So, what’s causing this?

• Fewer students entering medical school due to high tuition and burnout concerns
• Declining interest in general practice residencies as specialists earn higher salaries
• An aging population requiring more frequent and complex care
• Advancing diagnostic capabilities revealing more medical conditions than ever before

If this trend continues, the consequences could be severe. That’s why AI is emerging as a game-changer in healthcare.

“We need to think about using these AI tools because of this demand imperative for rigorous outputs. It has to be better than humans. It has to have the scale and has to be reliable.” – Matt Cybulsky

Is AI the All-Encompassing Solution to Physician Shortages?

While AI in healthcare offers powerful solutions – automating administrative workflows, analyzing patient data, and improving diagnostics – it cannot replace the expertise, empathy, and decision-making of human professionals. This is where healthcare virtual assistants (VAs) come in.

As remote professionals, healthcare VAs are becoming an essential solution for healthcare practices. True, AI can streamline efficiency, but remote staff members are still critical for tasks like;

• Scheduling and patient coordination
• Medical billing and insurance verification
• Transcription and electronic health record (EHR) management
• Answering patient inquiries and follow-ups

This is where My Mountain Mover  plays a vital role. As the #1 provider of trained and HIPAA-compliant healthcare virtual assistants, My Mountain Mover helps doctors and clinics reduce administrative workloads, increase efficiency, and improve patient experiences – all while keeping costs manageable.

Why Combine AI with Virtual Assistants?

• Enhance operational efficiency without sacrificing the human touch
• Improve patient engagement and overall healthcare accessibility
• Reduce burnout among doctors and healthcare staff

Fewer burned-out providers mean more sustainable, long-term healthcare solutions! As My Mountain Mover CEO, Amanda Descuacido says;

“When you choose My Mountain Mover, you’re not only getting a VA, but you’re also getting an entire full-service support system. It’s a smart investment as it requires very little effort from you, but it comes with a huge, tremendous upside.”

AI’s Power in Forecasting & Data Utilization

What if you could predict patient needs before they happen? AI-powered analytics make this possible, offering unprecedented forecasting capabilities.

Cybulsky highlighted a real-world example where a healthcare company trained AI algorithms using multiple data sources, including:

• CMS claims data (Medicare and Medicaid)
• Transaction data (debit and credit card spending patterns)
• Demographic information (age, gender, ZIP code)

By analyzing these inputs, the AI successfully predicted:

• Length of hospital stays
• Revenue cost per inpatient
• Risk of inpatient admission after an ER visit

For hospitals and clinics, this means better resource allocation, improved patient flow, and reduced operational costs. Imagine how much smoother your own healthcare operations could run with AI-driven forecasting at the core.

“”That’s the power of AI. We have to change the way we approach using these tools conceptually in order for us to win.” – Matt Cybulsky

Balancing Between AI and Human Connection

While AI’s efficiency is undeniable, its integration into healthcare presents an ethical challenge;

How do we ensure it doesn’t replace the human touch?

Healthcare isn’t just about efficiency. It’s about the trust between a doctor and a patient. This is something AI cannot replicate, and as Cybulsky pointed out, “When it comes to the doctor and patient relationship, it is not just about efficiency but the emotional connection.” 

So, how do we strike the right balance?

• AI should augment, not replace, physicians. Think of AI as a support system, handling administrative tasks and data analysis so doctors can focus on patient care.
• Ethical AI frameworks must be established to protect privacy and ensure responsible implementation.
• Healthcare professionals need AI training to effectively collaborate with these systems.

Overcoming the Data Challenge

Here’s something you may not realize: 80% of healthcare data is unstructured.

Cybulsky revealed this startling statistic, explaining that most of this data is inaccessible to AI algorithms. Without structured data, AI can’t learn from it—limiting its effectiveness in healthcare.

“If it’s not unstructured, it’s not inside of an AI algorithm. If it’s not inside of an AI algorithm, we are not learning from it.” – Matt Cybulsky

Structured vs. Unstructured Data in Healthcare

Data Type	% of Total Healthcare Data	AI Accessibility
Structured Data	20%	High
Unstructured Data	80%	Low

Bridging this gap requires:

• Advanced Natural Language Processing (NLP) to structure free-text medical notes.
• Standardized data-sharing protocols across healthcare systems.
• Investment in AI-driven data management solutions to unlock valuable insights.

Without addressing this issue, AI will never reach its full potential in healthcare.

The Future of AI in Healthcare

The momentum behind AI in healthcare is undeniable, but challenges remain. Fear, ethical concerns, and data limitations could slow progress—but they don’t have to.

“The future speculation of AI is real; I will not dispute that, but are we just getting started if 80% of our data is still in a place that we can’t access?” – Matt Cybulsky

So, Are We Ready?

AI’s role in healthcare is undeniable and it’s here to stay, but it is not a cure-all solution yet. Challenges like data accessibility, ethical concerns, and the balance between efficiency and human connection must be addressed. With proper integration, AI has the power to improve forecasting, optimize resource allocation, and bridge the growing gap in healthcare availability.

The real question isn’t just “Should we use AI in healthcare?” but rather “How do we use it responsibly and effectively?” The choices you and other healthcare leaders make today will shape the future of medicine for generations to come.

FAQ

1. How is AI transforming healthcare?

AI is revolutionizing healthcare by automating administrative tasks, improving diagnostics through predictive analytics, and optimizing patient care through advanced data processing. It helps reduce physician burnout, enhance operational efficiency, and forecast patient needs for better resource allocation.

2. Can AI solve the physician shortage crisis?

AI can assist with physician shortages by streamlining workflows, automating data analysis, and enhancing diagnostic accuracy. However, it cannot replace human expertise. The best solution combines AI with virtual assistants (VAs) who handle administrative and patient support tasks, allowing doctors to focus on patient care.

3. What role do virtual assistants (VAs) play in healthcare?

Healthcare VAs provide remote support for healthcare practices by managing scheduling, medical billing, EHR documentation, patient inquiries, and other administrative tasks. By combining AI automation with VAs, healthcare organizations can reduce workload and improve efficiency without sacrificing the human touch.

4. How does AI improve healthcare forecasting?

AI-powered analytics use large datasets—including Medicare/Medicaid claims, transaction records, and demographic information—to predict hospital stay durations, patient admission risks, and cost efficiencies. These insights help hospitals optimize staffing, reduce costs, and improve patient outcomes.

5. What challenges does AI face in healthcare adoption?

Key challenges include:

• Data Accessibility: 80% of healthcare data is unstructured and difficult for AI to process.
• Ethical Concerns: Ensuring AI is used responsibly without compromising patient trust.
• Human-AI Balance: AI should augment, not replace, healthcare professionals.
• Training & Integration: Healthcare providers need proper training to effectively collaborate with AI systems.

6. What is the difference between structured and unstructured healthcare data?

• Structured Data (20% of healthcare data): Organized information like lab results and billing codes, easily processed by AI.
• Unstructured Data (80% of healthcare data): Includes physician notes, scanned documents, and medical transcripts, which AI struggles to interpret without advanced NLP (Natural Language Processing) technologies.

7. How can healthcare organizations optimize AI integration?

To successfully implement AI, healthcare providers should:

• Establish ethical AI frameworks to protect patient privacy.
• Invest in Natural Language Processing (NLP) to process unstructured data.
• Train staff on AI tools to ensure effective collaboration.
• Use AI alongside medical VAs to maintain efficiency and patient engagement.

8. What does the future of AI in healthcare look like?

AI will continue evolving, with improvements in predictive analytics, personalized medicine, and real-time patient monitoring. However, its success depends on responsible implementation, data accessibility, and human collaboration to maintain high-quality, ethical patient care.