By Vatsal Ghiya, CEO and Co-Founder, Shaip
Twitter: @weareshAIp
Machine learning and artificial intelligence are changing the face of healthcare. Patient populations are growing larger, and our health is becoming more complex. Providers, researchers, and payers alike are looking for strategies to increase the quality of care and reduce costs simultaneously.
According to marketsandmarkets, the AI market in healthcare is expected to grow at a compound annual growth rate of 47.6% from 2023 to 2028, reaching a value of $102.7 billion in 2028.
This rapid growth is driven by the increasing amount of healthcare data generated daily. Artificial intelligence (AI) algorithms have the ability to analyze data in order to detect patterns and provide forecasts. Despite this growth, several challenges still need to be overcome before AI can be fully integrated into healthcare.
4 challenges of AI in healthcare 2023
Data is the fuel that powers AI applications. It can be used to train algorithms, which then help make better decisions for doctors and patients alike. To use data effectively, though, there are several challenges of AI in healthcare 2023 that must be addressed:
1. Privacy and Regulatory Compliance
Patients want their health data to be used for their benefit, not sold or used against them. This is particularly true in the US, where there is a long history of distrust of medical professionals and medical science.
AI systems require significant patient data to train and improve their algorithms, which can be sensitive and confidential. This data includes:
- Medical data
- Diagnoses
- Treatment plans
- Clinical data
Patient data, including personally identifiable information (PII) and protected health information (PHI), must be protected under the law. There are regulators in place that are also looking at privacy concerns.
For example, the European Union’s (EU) General Data Protection Regulation (GDPR), implemented throughout the EU since May 2018, requires companies to gain explicit consent before processing personal information. Companies must also notify regulators within 72 hours if they suspect a breach and allow users to access their data free of charge.
Healthcare organizations must ensure that they comply with privacy regulations such as the Health Insurance Portability and Accountability Act (HIPAA) to avoid the risk of data breaches or misuse of sensitive patient data.
2. Data Availability and Collection
In some cases, insufficient data is available to train and test AI algorithms. Furthermore, the available data may be stored in multiple locations and various formats, making it difficult to access and use.
Healthcare organizations must overcome these challenges through the following steps:
- Creating a centralized repository for data
- Standardizing the format
- Collecting data from various sources.
This process requires a significant investment in resources, time, and expertise.
Data scientists must work with medical professionals to understand their needs, collect data, and then translate it into an actionable format for them. This takes time and resources, but it’s worth it because you’ll be able to build better patient products.
3. AI Bias
For AI algorithms to work effectively, they need access to large datasets with diverse patient populations, medical histories, and disease patterns.
The effectiveness of AI algorithms is directly dependent on the quality of the data used to train them. Therefore, if the data used to train an AI algorithm is biased, the algorithm will also be biased. The training data must also be high quality, accurate, and relevant to the problem.
There’s are four common biases found in medical AI model trained for healthcare:
- Racial biases can occur when the dataset does not cover all racial classes, leading to inaccurate outcomes.
- Gender biases can result in incorrect diagnoses if the algorithm does not incorporate gender differences
- Socioeconomic biases can stem from clinicians’ biases, which transfer to the AI model, leading to output inequalities.
- Linguistic biases can occur when AI models using audio data for diagnoses are not trained with a diverse range of accents, disadvantaging those with non-Canadian English accents, for example.
Healthcare training data is often siloed and fragmented across healthcare systems and electronic health records (EHRs). This makes it difficult to collect and share the data.
To deal with these biases, we need to ensure that voice recognition datasets in healthcare and other datasets are diverse and representative of society. This means ensuring an equal distribution of gender, race, and age across the entire population being analyzed.
4. Lack of Understanding
There’s a significant gap between the technology itself and its application. Many executives lack knowledge about how AI can be applied to their organization’s problems and how it will impact their business internally and externally.
For example, some may think that using AI means automating everything, but that’s not the case—it’s simply a tool that can be used to make better decisions faster by leveraging the power of big data and machine learning algorithms.
Here’s what organizations can do to overcome the lack of understanding of healthcare AI:
- Invest in education and training programs for their staff. This can include workshops, webinars, and seminars focusing on AI basics and its potential applications in healthcare.
- Partner with technology companies to develop pilot programs that allow staff to experiment with AI applications in a safe and controlled environment.
- Create interdisciplinary teams that bring together healthcare professionals and AI experts to collaborate on projects and develop a shared understanding of AI’s capabilities and limitations.
- Prioritize transparency and open communication with patients and the public about using healthcare AI to build trust and understanding.
Conclusion
All in all, the healthcare field is full of patients and doctors who are motivated to make once again make a difference in the lives of people around the world.
Access to large data sets is one-way artificial intelligence will continue to prove itself as the future of medicine.
It’s up to researchers and developers alike to take advantage of these unique datasets to improve our understanding of clinical trials and patient care as we move toward an increasingly connected future for everyone.