Artificial intelligence and machine learning systems are revolutionizing patient care in healthcare. In addition, the COVID-19 pandemic has played a vital role in accelerating the use and adoption of these systems in the healthcare industry.
The main reason for the accelerated adoption is because of the many benefits of AI and ML in improving patient care. So, how are these AI and ML solutions helping to revolutionize patient care? Let’s find out below.
The benefits of AI/ML in healthcare
There are many ways through which the healthcare industry benefit from AI and ML technology. Here are their top merits.
AI and ML help doctors accurately diagnose patients. Before the invention and adoption of technology in healthcare, there were several cases of misdiagnoses. However, AI and ML reduce these false diagnosis issues due to human error.
Mobile application development facilitates the creation of AI applications. These AI applications in various healthcare sectors, such as the oncology department, help to detect diseases like cancer.
Population health management
AI-driven tools can help healthcare organizations collect, evaluate, and compare large amounts of data, which can help create population-level patterns critical to health management. Healthcare organizations use this data to identify early risks of diseases in a given population and create preventive measures.
Portable medical devices
AI and ML in healthcare for elderly and chronically ill patients are promoting personalized care management through portable medical devices. These AI and ML-powered devices play a crucial role in monitoring an individual’s health without the need to visit a healthcare facility. Some of these portable medical devices include:
- Blood pressure monitors
- Portable ECG devices
Case studies of successful AI/ML implementations in healthcare
There are many cases of successful implementation of AI ML solutions in healthcare. Here are two examples of these successful stories:
Natural Language Processing (NLP)
NLP is a sub-sector of AI that focuses on human and computer interactions through natural language. Healthcare organizations use NLP for the following tasks:
- Clinical documentation
- Data mining research
- Clinical trial matching
- Computer-assisted coding
- Hierarchical condition categorization
- Speech recognition that helps in HER data entry
Remote patient monitoring
AI plays a critical role in remote patient monitoring (RPM) by enabling real-time patient monitoring. This real-time patient monitoring facilitates the early detection of potential health issues.
In addition, the AI-powered remote patient monitoring system can continuously monitor a patient’s medication adherence and vital signs. An example of these RPMs include biosensors that can track:
- Daily activities
- Sleep patterns
- Blood pressure
- Heart rate
Addressing ethical concerns in AI/ML for healthcare
Despite the many benefits of AI and ML in healthcare, they also raise several ethical concerns. For example, the use of these new technologies raises concerns about data breaches, inaccuracy of information sources, bias, security and transparency, and data privacy.
Healthcare is a very sensitive industry, and any mistakes can have a serious impact on patient outcomes. Therefore, the best way to address ethical concerns involves:
- Algorithmic transparency
- Patient privacy protection
- Elimination of cybersecurity vulnerabilities
- Informed consent for the use of patient data
- Proper AI and ML models training to avoid biases
Overcoming challenges in implementing AI/ML in healthcare
Even though AI and ML play a vital role in diagnostics and healthcare, there are challenges to their successful implementation. Below we’ll look at some of the challenges and how to overcome them.
The use of Explainable AI (XAI) techniques to solve reliability issues
Understanding how AI and ML models work is incredibly difficult to understand. In most cases, healthcare professionals need to understand why and how AI and ML models produce certain results in order to take action
This lack of reasoning and understanding creates trust issues that prevent effective AI/ML adoption in healthcare. The best way to overcome this issue is to use Explainable AI (XAI) techniques which help to build confidence between computers and humans. This technique enables one to understand how AI and ML systems determine specific solutions.
Maximum AI and ML Model testing to eliminate diagnostic errors
While AI promises more accurate diagnostics, mistakes can still be made. This possibility of error makes some healthcare organizations reluctant to adopt AI and ML solutions. The best way to overcome this challenge is for vendors and healthcare organizations to conduct more testing of AI and ML models.
Future possibilities for AI/ML in healthcare
The future of AI/ML in healthcare is incredibly promising, especially with the expected advancements in machine learning algorithms and technologies. AI and ML technologies will be more integrated into all healthcare processes.
This full integration will lead to better patient outcomes. Apart from better patient outcomes, the full integration of AI and ML will positively contribute to the creation of more career opportunities in healthcare.
AI and ML in healthcare for elderly adults and individuals with chronic diseases enhance patient care, encourage successful diagnoses, and fastens prediction of health outcomes. Has your healthcare organization adopted AI and ML use? What has been your experience?