What are the challenges associated with using artificial intelligence in healthcare?
Have you heard of robotic nurses or robots that conduct medical conversations? These are not ideas from science fiction movies, but are a reality that we live today thanks to artificial intelligence.
The healthcare sector is undergoing a radical transformation thanks to artificial intelligence technology, and the market for software, devices, and services supported by artificial intelligence in the healthcare sector is expected to exceed $34 billion globally by 2025, but these exceptional developments come with a set of challenges.
Doctors, nurses, and other healthcare providers face many challenges when integrating AI into their work. Below are some of the most prominent challenges and possible solutions:
1- Impact on human labor:
There is growing concern that AI could replace healthcare professionals, and this concern may lead many medical institutions to refrain from adopting AI systems in their work, but this prevents them from benefiting from using this technology in the medical field.
The challenge is to balance using AI to perform routine tasks while retaining human experts to provide appropriate care to patients in complex cases.
This means that to effectively integrate AI systems into healthcare, medical institutions must balance the benefits of AI with the preservation of human expertise. This can also help reduce concerns about job loss.
2- Maintaining the privacy of patient data:
Obtaining patient consent for the use of their data in AI systems can be complex, especially when patients do not understand the underlying purpose. Being transparent with patients about the role of AI in their treatment is therefore crucial to increasing its adoption.
On the other hand, medical institutions must ensure that data privacy and security are maintained; the emergence of cyber threats poses significant risks to sensitive patient data. Therefore, balancing data accessibility with strong security measures is essential, and medical institutions must adopt strict cybersecurity practices to protect patient data.
3- Biases in training data and poor data quality:
Biases in training data can lead to inappropriate treatment suggestions or incorrect diagnoses. In addition, poor data quality such as incomplete or inaccurate data can lead to inaccurate analyses, ultimately putting patients at risk.
To solve this problem, the medical datasets used to train AI algorithms should be verified to eliminate biases and the problem of low-quality data, which in turn enhances the validity and accuracy of diagnosis and ensures the provision of appropriate healthcare to patients.
4- Lack of training:
Most medical students do not receive adequate training on how to use AI tools in their field of work, which makes it difficult to adopt AI in their work.
On the other hand, there are some doctors and healthcare providers who refuse to adopt any digital technologies in their field of work, as they prefer traditional in-person consultations between human doctors and relying on their expertise. This refusal is often due to the general lack of awareness of AI and its benefits in improving the medical sector.
To solve this problem, training and education for healthcare professionals should be enhanced, and this training can also be extended to universities; ensuring that medical students and other medical specialties are familiar with how to handle AI tools appropriate for their work, which in turn will enhance the adoption of this technology in most medical institutions.
5- Data collection and processing problems:
Medical organizations that rely on AI in their work may face problems with the systems’ ability to aggregate data from different sources, making it difficult to access and process information efficiently.
To solve this problem, a unified data environment must be created that can aggregate data from all different sources into a central system, often requiring a complete overhaul of the medical organization's data storage system.