5 Challenges of AI-powered Telemedicine and Their Bold Fixes

Challenge No. 1: Telemedicine AI is Hard to Trust 

Can you leave your patients completely in the hands of AI models? Many healthcare providers are hesitant because these models are not 100% transparent. It is even difficult for data engineers and scientists to understand how these models generate outputs. This is called a black box problem. 

Then, there are limitations in their accuracy, especially when they are trained on less diverse patient demographics. Results? Misdiagnoses or incorrect treatment recommendations. 

Solutions:

Despite AI's transparency being a challenge, overcoming it is easy with certain practices. 

• Explainable AI (XAI): Use systems built with explainable AI. Explainable AI is a set of practices and methods that help comprehend how AI systems generate certain outputs or decisions. This will build trust and confidence between AI systems and human physicians. 
• Model Training and Testing: Static data can cause accuracy issues in AI-based telemedicine tools. To solve this challenge, ensure the systems are always fed fresh and diverse data. Frequent testing will further help spot any gaps in the systems. 
• AI as a Complementary Tool: Use AI in telemedicine as a support, not a replacement. A human physician should always validate the final decisions. 

Challenge No. 2: AI-Based Telemedicine Tools Could Lead to Privacy Breaches

AI's capability to work with extensive data is undoubtedly impressive, but it raises data security concerns in the healthcare sector. With the rise in telemedicine platforms and patient demand, sensitive patient data will keep growing. The challenge is how AI will handle that data and, most importantly, process it securely . 

Solutions:

• Advanced Security Measures: Cybersecurity experts help you protect sensitive data with end-to-end encryption across all data exchanges. Multi-factor authentication will further ensure that patient information remains secure. 
• Data Minimization: Train your AI systems and chatbots to collect only the necessary data for telemedicine consultations. This will reduce the attack surface, impacting only limited information in case of breach. 
• Clear Data Policies: It is essential to communicate with patients how their data will be collected, used, and stored. Healthcare providers with clear data policies build more trust with their patients and faster adoption of AI systems as patients don't see AI as a shadowy figure in their health journey.

Challenge No. 3: The Unseen Barriers of Legacy Systems

Legacy systems are the roots of the healthcare sector, and using AI-based telemedicine systems means replacing them. Many are afraid of this because those systems were not designed to incorporate AI, resulting in interoperability issues. 

Fragmented medical health records across various systems and a lack of standardization make it even more challenging to share and integrate data between old and new technology. Costly upgrades add more strain. 

Solutions:

• Modular AI Solutions: Rather than replacing entire systems, invest in modular AI solutions. These are easy to integrate with existing infrastructure and save money on costly system-wide upgrades.
• Cloud-Based Platforms: Shift to cloud-based solutions wherever possible. Cloud platforms eliminate the challenge of interoperability and let you scale without adding new systems on-premises. They are also cost-effective when it comes to upgrading your IT infrastructure. 
• Incremental Updates: Instead of doing it all at once, go step by step. Start with areas where you think AI-based tools will bring the most immediate impact, such as patient scheduling or remote monitoring. 

Challenge No. 4: AI is Expensive and Will Burden Already Tight Budgets

AI is here to stay. That means the faster you adopt it, the better you can compete in the industry. However, it comes with a huge cost. To develop small-scale AI models, you need a budget of a minimum of $5000. Bigger models are more expensive. 

This cost is not a burden for large healthcare providers, but those with tight budgets may feel that AI is an expensive technology. In addition to developing AI healthcare solutions, additional maintenance, model training, and optimization expenses exist. All these only add stress to small-scale healthcare providers. 

Solution:

AIaaS: Use AI as a service rather than paying for AI software upfront. This new model of implementing AI in business is a flexible solution for those who lack resources. An AIaaS solution provider charges only for services you use. From maintenance to support, they take care of everything. 

Challenge No. 5: The Evolving Regulatory Landscape

Telemedicine, by nature, pushes the boundaries of healthcare delivery beyond the hospital's walls. No matter how far it goes, it will always be under regulatory standards. Adding AI to telemedicine will add layers of complexity, making it difficult to keep up with the standards. How?

AI-driven tools will be under the purview of the FDA as other diagnostic tools. From testing to approval, they must meet the same standards to be used as medical devices. Then, there are consent-related challenges. 

Solutions :

• Work with Experts: Choose regulatory consultants specializing in your domain and AI. This will free you from the headache of ensuring compliance with your AI-driven telemedicine solutions. 
• Compliance Monitoring Tools: Use compliance monitoring solutions to monitor your AI systems continuously. They warn you about potential issues before your system breaches any compliance.
• Consent Management: Invest in consent management tools to align with patients' consent preferences in real time. 

Conclusion

Embracing new technology is always challenging and creates doubts, but its benefits are unmatchable. AI is that technology. It brings healthcare services closer to patients who cannot visit hospitals or need urgent care at home. Video consultations, store-and-forward messaging systems, and healthcare applications make telehealth possible. 

Telemedicine AI is not limited to remote consultations, it is expanding to tele-ICU, telecardiology, teleoncology, and other areas. It is not even an option now. However, security, integration, and ethics-related challenges may make you hesitant. There is always a way out. Solutions mentioned in our blog will help you address them head-on. 

Several healthcare leaders, such as Cleveland Clinic, are paving the way for the smooth adoption of AI in telehealth, showing that good things are lying on the other side.