Updated on July 3, 2023 by Jordan McGlone
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Artificial intelligence (AI) has been making waves in various industries, from finance to retail, but its adoption in healthcare has been slow. Despite the potential benefits of artificial intelligence in improving patient outcomes and reducing healthcare costs, many healthcare organizations have been hesitant to fully embrace this technology. In this blog post, we’ll explore some of the reasons why AI adoption in healthcare has been slow.
How Artificial Intelligence Is Currently Being Used in Healthcare
Machine learning and AI have found various applications in healthcare, including precision medicine, drug discovery, medical diagnostics, and robotics. AI-powered software aids medical diagnostics by analyzing MRI and CT scans in real-time, enabling faster and more accurate diagnoses.
Diagnosis
Early diagnosis of diseases like cancer and Parkinson’s is facilitated by machine learning algorithms that analyze medical histories and biomarkers. In cancer diagnosis, for example, AI algorithms analyze medical images and genetic data to detect and classify tumors, aiding in early diagnosis accurately. Then, outcome prediction models can be made to anticipate disease progression, treatment responses, and survival rates by analyzing patient data.
Development
In the development of drugs, AI helps streamline the process by processing large data sets, refining drug targets, and identifying effective compounds. Artificial intelligence also enhances the evaluation of clinical trials, identifying positive outcomes in specific patient categories and revisiting past trials. It also helps identify genetic mutations, find the best drug candidates, and predict patient interactions for personalized treatments and improved trial analysis.
Treatment
Precision medicine utilizes AI to predict personalized treatment regimens based on individual patients’ medical histories, incorporating genomics, predictive modeling, and diagnostic decision-making. Machine learning algorithms enable targeted treatment options based on patient information and genetic structures, leading to personalized medicine.
Assisted surgery leverages robots with precision and enhanced vision, augmenting surgeons’ abilities. Robotics, particularly surgical robots, enhance surgeons’ capabilities in performing precise and minimally invasive procedures in areas such as gynecology, prostate, and head and neck surgeries. These advancements in AI have significant implications for improving healthcare outcomes.
Paperwork
AI automation helps reduce administrative burdens by handling tasks like medical data transfer, eligibility checks, managing medical records, and appointment booking. Virtual assistants powered by natural language processing gather patient data, provide information, update records, and assist with fulfilling requests.
Related article: Redefining Great Patient Care in 2023.
Challenges to Further Adoption of AI in Healthcare
Lack of Trust
One of the main reasons for the slow adoption of AI in healthcare is the lack of trust in the technology. Healthcare providers are understandably cautious when it comes to implementing new technologies that could potentially impact patient care. There is a fear that artificial intelligence could make mistakes or miss important information, leading to negative outcomes for patients. Additionally, there is a concern that AI could replace human healthcare providers, leading to job loss and a decrease in the quality of care.
There is a lack of understanding and education around artificial intelligence in healthcare. Many healthcare providers may not fully understand the capabilities and limitations of AI, which can lead to skepticism and resistance to adoption. There is a need for more education and training around AI in healthcare to help healthcare providers understand how it can be used to improve patient outcomes and reduce costs.
Complexity in Healthcare
Another reason for the slow adoption of AI in healthcare is the complexity of the healthcare system. Healthcare is a highly regulated industry, and implementing new technologies requires significant investment and time. Healthcare organizations must navigate these complexities while also ensuring that any new technology is effective and safe for patients.
Furthermore, there is a lack of standardization in healthcare data, which makes it difficult for AI systems to effectively analyze and interpret the data. Healthcare data is often stored in different formats and systems, making it challenging to integrate and analyze. This lack of standardization can lead to inaccurate or incomplete data, which can impact the effectiveness of AI systems.
Privacy & Security Concerns
There are also concerns around data privacy and security, as healthcare data is highly sensitive and must be protected. The data pertaining to patients contains confidential and personally identifiable information, including medical history, identity details, and payment information. These data are subject to strict regulations outlined by GDPR and HIPAA to ensure privacy and security. However, the adoption of healthcare AI faces challenges due to the large volume of data required by most machine learning systems, increasing the risk of potential data breaches or unauthorized access.
Unmatched Metrics
The criteria used to measure the success of AI models may not directly apply to real-world healthcare settings. This gap between technical precision and clinical effectiveness is known as the AI chasm. To bridge this gap, developers and clinicians need to collaborate and assess AI algorithms for their accuracy in improving patient care. One approach is to use decision curve analysis, which helps evaluate the clinical utility of prediction models by comparing datasets and estimating their real-world success rates.
Lack of Research
There is a lack of established methodologies, prospective research, and peer-reviewed studies on AI in healthcare. Most studies have relied on retrospective analysis of historical patient records. However, to fully understand the value of AI diagnosis in real-world scenarios, physicians must conduct prospective research by studying current patients over time. This requires combining physical examinations with telehealth visits and remote monitoring technologies, such as sensors and trackers, to ensure reliable data collection.
Related article: Are You Ready for the Smart Hospital Revolution?
Artificial intelligence has made significant strides in revolutionizing healthcare. It has proven beneficial in various areas, such as medical diagnostics, drug development, precision medicine, and assisted surgery. AI algorithms analyze medical data, enabling faster and more accurate diagnoses, predicting treatment outcomes, and personalizing medicine. Additionally, machine learning streamlines drug development processes, enhances clinical trial evaluation, and reduces administrative burdens through automation.
However, the adoption of AI in healthcare faces challenges. The lack of trust in the technology and the need for education and understanding hinder its widespread use. The complexity of the healthcare system and the lack of standardization in data pose additional barriers. Privacy and security concerns surrounding sensitive patient data also need to be addressed. Moreover, there is a gap between technical precision and clinical effectiveness, emphasizing the importance of collaborative assessment and research to ensure AI’s real-world impact.
The slow adoption of AI in healthcare can be attributed to a lack of trust in the technology, the complexity of the healthcare system, a lack of standardization in healthcare data, and a lack of understanding and education around artificial intelligence. While these challenges are significant, it’s important for healthcare organizations to continue exploring the potential benefits of AI and finding ways to overcome these obstacles. With the right approach, AI has the potential to revolutionize healthcare and improve patient outcomes.
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Resources
- Emeritus, Rupam Deb; “Top Challenges of AI in Healthcare: What Businesses Need to Resolve,” January 2023.
- The Motley Fool, Jeremy Bowman; “How AI Is Used in Healthcare,” May 2023.
- Vanguard X, “Machine Learning in Medicine: Impact of AI Technologies in Medical Care,” May 2023.
