Whether it is clinical workflows or operations AI is transforming the healthcare industry by increasing efficiency, and maximising real-time data analysis. The healthcare AI market is projected to reach $31.02 billion by 2025 from the currently valued at $2.5 billion. Used in preventative medicine, new drug discovery and enhancing patient outcomes through precision medicine, AI is also helping healthcare providers with digital transformation and connecting diverse data sources for better patient care.
The healthcare industry has shown significant interest in adopting AI. Yet, there are several challenges that are currently impeding widespread implementation:
Data Quality and Privacy
The healthcare industry generates a large amount of sensitive and confidential data, and ensuring the privacy and security of this data is a major concern when implementing AI systems. In addition, the quality of the data used to train AI models is critical to the accuracy of the results. Inaccuracy may lead to grave repercussions and calling off of any more AI initiatives by regulatory bodies.
Interoperability
AI systems are only as good as the data they receive, and healthcare data is often siloed and difficult to access, making it challenging to integrate AI systems into existing workflows.
This was in fact planned to be so to protect the patient-doctor privacy privilege. Therefore data security is crucial while making such data available for analysis by third parties.
Regulation
Healthcare is a highly regulated industry, and AI systems must comply with strict rules and regulations around patient data privacy, data security, and the use of medical devices.
Compliance with regulations and standards such as HIPAA, European Union’s General Data Protection Regulation (GDPR) and India’s Personal Data Protection Bill (PDPB) apply to AI use in healthcare.
Lack of Standardisation in Healthcare AI
AI in healthcare is still a relatively new field, and there is currently a lack of standardisation around the development and deployment of AI systems. This can make it difficult for healthcare providers to determine which AI systems are best suited for their needs.
Training and sensitisation for healthcare professionals and data analysts is much needed.
Bias and Fairness
A big drawback is that AI algorithms are only as unbiased as the data they are trained on, and if the data contains biases, the AI system may produce biased results. This is a significant concern in healthcare, where biases in AI systems can lead to unequal treatment and harm to patients.
Resistance to Change
Healthcare is a traditionally conservative industry, and many healthcare providers may be resistant to adopting new technologies, particularly those that have the potential to disrupt established processes and workflows.
Many healthcare organisations have massive legacy systems in place and revamping means considering huge cost factors — a big challenge.
Conclusion
Despite these challenges, the potential benefits of AI in healthcare are significant, and many in the industry are working to overcome these obstacles and drive wider adoption of AI technologies. Many such technologies are already disrupting the industry as we read this.
Originally published at https://www.amalgam.blog on February 10, 2023.
