Top 6 Challenges of AI in Healthcare & Overcoming them in 2024

Artificial intelligence (AI) has numerous applications in the healthcare industry, and it continues to grow with technology advancements. However, this field also has some limitations that hold AI back from being integrated into the current healthcare systems. We will explore how you can overcome these challenges to boost healthcare with AI.

Understand how and why AI comes up with specific results

AI models become more complicated to deliver better outcomes. This complexity causes AI to work in a “black-box,” where it becomes harder to understand how the model works. Healthcare workers often need to understand how and why AI comes up with specific results to act accordingly. The lack of reasoning raises reliability issues for both healthcare companies and patients.

There are Explainable AI (XAI) methods that can solve this issue and build confidence between humans and computers by justifying how they reach particular solutions. As more work is done in this area, it will be possible for healthcare workers to work with complex but explainable models. You can read our in-depth explainable AI (XAI) guide to learn more about this field.

On the other hand, the benefits of complex black-box models such as deep learning models are hard to ignore. Deep learning algorithms have applications in processes ranging from medical imaging to personalized healthcare and drug discovery. So, we recommend using what works best but testing and analyzing it carefully, which is our next point.

You can also check our article on how to reduce bias in AI systems.

Test AI more to prevent diagnostic errors

Diagnostic errors account for 60% of all medical errors and an estimated 40,000 to 80,000 deaths each year. Although AI can offer more accurate diagnostics, there is always a chance that it can make mistakes, which causes companies to hesitate about adopting AI in diagnosis.

IBM Watson for Oncology is a popular example in healthcare for an AI tool that gives erroneous advice. We have also witnessed unsuccessful AI tools built to diagnose Covid-19.

The most common problem in these examples is that these AI tools are trained on poor-quality data that does not accurately represent its underlying real-world mechanism. Healthcare organizations must test and verify that the training data is representative and the model generalizes well without underfitting or overfitting against the training data.

Utilize innovative ways of data annotation

Finding high-quality medical data is another major challenge in implementing AI in the healthcare sector. The sensitive nature and ethical constraints attached to medical data make it difficult to collect. Since annotating a single model can require about 10,000 images, this can make the processing time-consuming and expensive, even when automated.

New ways of medical image annotation are helping to overcome this challenge by extracting more data sets from one image and significantly reducing the amount of data needed for training a model.

See how it works

Invest in privacy-enhancing technologies

When it comes to the healthcare industry, privacy is a prominent issue. Patient data contains highly sensitive personally identifiable information (PII) (e.g., medical histories, identity information, payment information), which is protected by regulations such as GDPR and HIPAA. The large data requirements of most AI models and companies’ concerns over the possibility of data leakages reduce the adoption of healthcare AI technologies.

For example, the University of Washington accidentally shared almost 1 million people’s personal health information due to a database configuration error. HIPAA Journal publishes reports on healthcare data breaches in the US each month, and they report that there were over 700 data breaches in 2021, around an 11% increase from 2020.

Healthcare organizations must leverage privacy-enhancing technologies (PETs) to reap the benefits of AI while minimizing the risk of data breaches. There are traditional methods such as data masking that involve replacing sensitive information with false but realistic data. There are also emerging PETs that can help healthcare organizations ensure the security of their sensitive data without reducing the utility they can get from it. They include:

• Differential privacy
• Homomorphic encryption
• Secure multi-party computation
• Zero-knowledge proofs

Provide training to and increase engagement among healthcare workers

The top negative perception about the advent of AI among healthcare providers is its potential impact on employment. The technology will no doubt replace repetitive and routine jobs and create new job roles. This slows down the adoption of AI among healthcare organizations.

However, AI is still far from replacing most jobs since AI applications are generally successful in carrying out narrow tasks. Specialized jobs, on the other hand, are far more complex than narrowly defined tasks and require human expertise. 

AI tools can increase the efficiency of many specialized jobs in all industries as well as in the healthcare industry. For instance, some diagnostic procedures are complex, while others are labor-intensive and repetitive. AI tools can take over the latter tasks and free up clinicians’ time for more complex tasks.

To overcome this challenge, healthcare organizations should provide training to upskill their workers for AI and machine learning technologies and their applications. This will help organizations create a workforce that is confident in using emerging technologies and also help employees with their long-term careers.

Educate to reduce patient reluctance

People are resistant to change and are more accepting of familiar things, especially when it comes to healthcare. When new and known technology is presented to people it can create hesitations. Patient reluctance is another major challenge in implementing AI in healthcare.

For example, at the beginning of the Covid-19 pandemic, patients were not comfortable with online checkups. However, now many people prefer it. According to a recent American study, about 50% of patients prefer healthcare facilities to offer online or web-based checkups. 

At first glance, patients might find it scary to be operated by a robot. However, as they understand and learn the benefits, such as reduced post-procedure pain and other complications, the hesitations might fade away.

For more on AI in healthcare, feel free to read our other articles:

• 43 Healthtech AI vendors by area of focus & geography
• Digitizing Healthcare: Customer-centric Health Services
• Personalized Care: In-depth guide

You can also check our sortable/filterable lists of:

• AI Platforms
• AI Consultants
• AI Development Services

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