Top Challenges of AI in Healthcare: What Businesses Need to Resolve
According to Statista, the global market for Artificial Intelligence (AI) in healthcare will reach $188 billion by 2030. The reasons for this are evident. Healthcare providers are investing hugely in employing AI for multiple purposes, such as disease diagnosis and improving the quality of patient care. However, despite these benefits, the technology is sparking debate among industry experts as it is accompanied by some major pitfalls. In this article, we will discuss the top challenges of AI in healthcare to help you understand the current concerns amid the tech’s enormous potential.
Objectives of AI in Healthcare
Before understanding the challenges of AI in healthcare, let’s list the objectives of applying this technology to this sector.
1. Increasing Effectiveness of Diagnostic Processes
One reason to implement AI in healthcare is to improve diagnostic effectiveness. Large caseloads and a lack of medical history may increase the possibility of human error in healthcare settings. In comparison to clinicians, AI systems can detect and diagnose diseases quicker and with minimal risk of error.
2. Reducing Overall Healthcare Costs
AI can be employed to make diagnosis processes more efficient, which in turn, reduces the total cost of healthcare procedures. For example, consider a case when AI can scan through millions of diagnostic images to look for disease symptoms. It eliminates the need for expensive manual labor. Moreover, patients can receive faster and more effective care, which reduces the need for hospital admissions and long waiting periods.
3. Safer Surgeries
By offering effective and distinctive aid in surgery, AI is establishing a position for itself in healthcare robotics. AI’s ability to perform less invasive surgical procedures that might otherwise require open surgery provides a wide range of benefits to patients, such as reduced blood loss, decreased infection risk, and less pain after surgery. Moreover, due to AI’s capacity to offer more precision while operating on delicate organs and tissues, patients need smaller incisions and also experience reduced scarring and faster recovery times.
4. Easy Information Sharing
The ability of AI algorithms to analyze vast amounts of information quickly is the key to fulfilling the potential of this technology in precision medicine. For instance, currently, 11.3% of the American population has diabetes. The condition has to be treated and managed urgently, and AI can assist healthcare professionals in understanding the disease through data via a real-time glucose monitoring system.
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Applying AI in Healthcare
The 2021 AI100 study states that AI is currently being tested actively at medical facilities for diagnosis, symptom prediction, and research, including drug discovery. The following are some of its most promising use cases:
1. Diagnostic Assessment
AI can examine enormous amounts of data from Electronic Health Records (EHRs), radiography, CT scans, and magnetic resonance images. By comparing data across patients, finding patterns, and detecting associations, AI systems can help with early symptom predictions.
2. Virtual Health Assistants
Virtual health assistants (such as Sense.ly, AiCure) are responsible for performing a variety of tasks, such as answering routine patients’ calls and emails, managing medical information, protecting sensitive patient data, scheduling doctor appointments, and reminding patients of follow-up visits. It is one of the most helpful AI applications in healthcare that provides patients with a tailored experience in managing their health as well as addressing their queries.
3. Treatment of Rare Diseases
An AI-based clinical-stage biotech platform called BERG strives to map diseases to accelerate the discovery and development of cutting-edge breakthrough drugs and vaccines, revolutionizing the way healthcare is provided. It uses Research and Development (R&D), along with interrogative biology, that allows medical practitioners to produce robust products for patients fighting rare diseases.
4. Targeted Treatment
With the help of technologies such as Deep Learning and AI, BenevolentAI, a leading, clinical-stage AI-enabled drug discovery company, was able to deliver appropriate treatment to the required patients at the right time, resulting in targeted treatment of patients with useful insights. Currently, the company is working on obtaining licensing for its drugs and creating portable treatments for rare diseases.
5. Drug Discovery
AI uses neural networks to assess the bioactivity and properties of drug candidates. Researchers can identify the best drug targets to test for various diseases with the help of AI systems. It has proved indispensable in clinical trials in the selection of the right candidates and, as a result, the healthcare industry has witnessed an increased speed and lesser investment in drug discovery.
ALSO READ: Drug Development Process: How Long Does it Take to Develop a Drug?
Privacy Concerns With AI in Healthcare
When it comes to applying AI in the healthcare industry, privacy is a significant concern. Patient data consists of highly sensitive Personally Identifiable Information (PII) such as medical history, identity information, and payment information, which is protected by regulation guidelines provided by GDPR and HIPAA.
Healthcare AI adoption is hindered by the vast amount of data that the majority of AI systems demand which, in turn, increases the possibility of data leakages. For example, HIPAA Journal publishes reports on healthcare data breaches in the U.S. each month and, as per them, over 6 million records were breached as of October 2022.
Top Most Common Challenges of AI in Healthcare
Despite impressive possibilities, the real deployment of AI-enabled solutions in clinical practice is still limited. Besides privacy challenges, AI technology also presents other technical and methodological shortcomings. Here are the top challenges of AI in healthcare:
1. Lack of Quality Medical Data
Clinicians require high-quality datasets for the clinical and technical validation of AI models. However, due to the fragmentation of medical data across several EHRs and software platforms, collecting patient information and images to test AI algorithms becomes challenging. Another obstacle is that the medical data from one organization may not be compatible with other platforms due to interoperability problems. To increase the amount of data available for testing AI systems, the healthcare sector must concentrate on techniques for standardizing medical data.
2. Clinically Irrelevant Performance Metrics
The measures used to gauge an AI model’s success are not necessarily transferable to clinical settings. The discrepancy between the clinical efficacy demonstrated in the real world and the technical precision of AI tests is referred to as the AI chasm. To avoid this gap, developers and clinicians should collaborate to investigate how AI algorithms enhance patient care. To do this, they can assess AI models for accuracy using decision curve analysis. This method enables them to evaluate the clinical usefulness of a prediction model by comparing the datasets and estimating the chances of an AI model’s success in the real world.
3. Methodological Research Flaws
There are not enough established methodologies, prospective research, or peer-reviewed studies of AI in healthcare. The majority of studies have been retrospective and based on historical patient medical records. However, to realize the true value of AI diagnosis in real-world settings, physicians must study current patients over time, which means prospective research. And for reliable prospective research, doctors should monitor the health of their patients by combining physical examinations with telehealth visits and remote monitoring technologies (sensors and trackers).
Future of AI in Healthcare
Although sophisticated AI models aren’t yet ready for widespread deployment owing to data discrepancies, research flaws, and privacy protection concerns, these challenges can be resolved. AI offers exciting prospects to advance healthcare with diagnostics, early symptom predictions, and drug discovery. Moreover, current AI- and ML-based systems (AI voice assistants) are already supporting healthcare organizations in streamlining their workflow. Thus, the future of AI in healthcare looks promising. As a result, professionals who are interested in launching a successful career in this field should stay updated with recent advancements. As a starting point in this upskilling, choose from Emeritus’ wide selection of online healthcare courses spanning the latest industry topics and enroll in the right curriculum to advance your career.
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