How AI is Revolutionizing Rheumatoid Arthritis Treatment

Credit: Business Standard

The role of artificial intelligence (AI) in disease management is rapidly evolving, and its potential to revolutionize the management of chronic conditions like rheumatoid arthritis (RA) is significant. AI can analyze vast amounts of patient data to identify patterns and predict disease progression, which allows for more personalized and timely interventions.

For RA, this means that AI can help in early detection, monitoring disease activity and optimizing treatment plans tailored to individual patient needs. By integrating AI-driven insights, healthcare providers can offer more precise and proactive care, ultimately improving patient outcomes and quality of life.

However, this emerging advancement does not come without challenges, from issues with accuracy to ethical considerations and regulatory concerns.

AI in Predictive Analytics and Personalized Care

One exciting evolving facet of AI involves predictive analytics. By using data from electronic health records, genotyping or other research studies, clinicians can integrate this information into models that predict the disease course.

For example, these models could predict whether a disease will become life-threatening or damage the joints in RA, forecast flares in patients who seem stable or determine which drug might work best for a particular patient.

“While we might not achieve a perfect system, incremental gains in using data to understand individual disease courses are very likely,” says S Louis Bridges, MD, PhD, physician-in-chief, chair of the Department of Medicine, chief of the Division of Rheumatology at Hospital for Special Surgery.

Another aspect is the ability to incorporate patient-reported outcomes and wearables that record movement. These tools can help predict treatment responses and complications more accurately.

A recent study evaluated the feasibility of collecting and analyzing data from electronic health records (EHR) to determine the efficacy of pharmacotherapeutic interventions and treatment outcomes. Results revealed the AI methodologies were successful, with the main advantage being its seamless integration with existing workflows.

Similar research has shown machine learning methods were able to predict a patient’s response to methotrexate with significantly high accuracy by using demographic information and clinical data, including medical records and laboratory assessments.

AI tools like ChatGPT are being increasingly utilized by rheumatologists to enhance patient care and communication. These tools can provide instant access to a wealth of medical information and support decision-making by offering evidence-based recommendations. For patient communication, AI can assist in answering common questions rapidly, providing education about RA, and offering emotional support through virtual consultations.

“By streamlining routine tasks and improving access to information, AI allows rheumatologists to spend more time on personalized patient care and complex clinical decision-making,” said Steve Siegel, president of ROMTech, a leader in advanced orthopedic rehab technology.

Despite these advancements, a continued and changing challenge lies in the accuracy of these predictions. Bridges recalled researching a gene associated with joint damage among Black patients. He asked ChatGPT if this gene was expressed in neutrophils, and it provided three citations that turned out to be entirely fictitious. This highlights the current limitations of using AI for diagnostic purposes without expert interpretation.

Similarly, results from a survey of doctors showed significant heterogeneity in acting in a variety of complicated clinical situations, both among the doctors surveyed as well as in comparison with ChatGPT. Both clinicians and AI did, however, agree on the acceptance and integration of biosimilar treatment options.

While AI is improving and many models are being trained on extensive medical literature, we are not yet at the point where AI can replace expert judgment. However, AI can assist experts by quickly identifying normal cases and flagging potential issues for further review, like radiologists reviewing chest X-rays. The field is evolving rapidly, and it is constantly improving.

AI in Healthcare Operations and Enhancing Accessibility

Recent studies have shown promising results in using AI to manage RA. For example, machine learning algorithms have been developed to predict flares in patients with RA by analyzing clinical and laboratory data. These predictive models can help rheumatologists adjust treatment plans before a flare occurs, thereby reducing the severity and frequency of these episodes.

Additionally, AI has been used to analyze medical images, such as MRI and ultrasound, to detect early signs of joint damage that may not be visible to the naked eye. These advancements enable earlier and more accurate diagnosis, leading to better long-term management of RA.

Despite these promising findings, integrating AI into existing healthcare systems involves several key steps. First, Siegel explained, it's crucial to ensure that AI tools are integrated with current electronic health record (EHR) systems, allowing seamless data sharing and analysis. Training healthcare providers on the use of AI technologies and demonstrating their benefits is also essential for widespread adoption.

AI should be used to complement, not replace, traditional RA management approaches by providing additional insights and efficiencies, he said. For example, AI technology can help identify patients who may benefit from more aggressive treatment or closer monitoring, thereby enhancing the overall management strategy. A collaboration can also provide guidance and support throughout the transformation process. Ultimately, the goal is to leverage AI to enhance patient care, improve operational efficiency, and drive better health outcomes while maintaining the highest standards of ethics and patient privacy.

“By integrating AI thoughtfully, we can enhance the speed, precision, efficiency and effectiveness of RA care,” Siegel said.

Challenges and Limitations of AI in Disease Management

Data quality and availability remain significant obstacles due to privacy regulations and inconsistent data collection, says Vanessa Justice, director of Account Executives at BlueCloud. Additionally, AI models, especially deep learning, often lack interpretability, making their decision-making processes difficult to understand and trust. Integration with existing healthcare systems is another hurdle, requiring substantial resources and time.

Another significant ethical consideration is avoiding bias. For example, developing a model for treating a disease using only data from White patients would be unethical if applied to Black patients. Ensuring privacy is also crucial; using AI inappropriately and releasing patient information would be unethical. It’s important to integrate diverse data and protect patient privacy to maintain ethical standards. Bias in AI systems, stemming from inequalities in the training data, can also lead to unfair treatment outcomes, further complicating its use in delivering equitable healthcare.

“AI must be used responsibly to ensure fair and ethical treatment across all demographics,” said Bridges.

Addressing these potential biases in AI models can ensure fair treatment for all patients. Another key factor, Justice says, is to maintain transparency about how these systems make decisions and ensure accountability for any outcomes.

To mitigate these challenges, Justice suggests focusing on human oversight and “ensuring that AI systems are used to support, not replace, human clinicians, maintaining the human element in healthcare decision-making” can help to integrate AI into disease management while considering these complex ethical factors.

Additionally, utilizing robust data privacy measures to protect sensitive patient information while implementing strict cybersecurity protocols can safeguard against data breeches and unauthorized access to data. Consent is key in this scenario, and Justice encourages doctors to inform their patients about how their data will be used prior to using AI-driven tools.

The Future of AI in Rheumatology

Many patients, especially in rural areas, lack access to specialists. AI can help bridge this gap by providing support where specialists are unavailable, ensuring fairer access to care.

Nabeel Kaukab, CEO and co-founder of Jaan Health, a chronic care management and proactive care company catering to healthcare providers, believes AI’s biggest long-term impact will be on the delivery of care, as one of the major challenges in healthcare, especially in rheumatology, is the shortage of specialists.

“We can't suddenly increase the number of rheumatologists overnight, or arguably even within a generation,” he said. “Therefore, we are going to have to figure out ways for the average rheumatologist to become far more efficient in what they do to be able to see more patients. Technology can make this possible by either taking out some of the routine parts of their job, which require time, effort and human intervention.”

This will allow specialists to focus on more complex tasks, and could be crucial for managing chronic diseases like RA, where the majority of issues occur between patient visits.

According to Bridges, the goal is to achieve personalized medicine by using AI to analyze patient data and predict the best treatments. He believes AI will be able to help create personalized treatment plans by analyzing detailed data from affected tissues and predicting the best drug for an individual patient. Eventually, doctors may be able to use blood tests and AI algorithms to predict responses to different drugs, moving towards a precision medicine approach where treatments are tailored to each patient's unique characteristics.

AI is transforming the landscape of RA management through personalized treatment, enhanced accessibility and efficiency in healthcare systems. This includes scheduling, managing patient queries, triaging patients based on urgency and providing better access to care through efficient scheduling. These tools can help streamline logistics, improve patient outcomes and support healthcare providers in their daily tasks.

Despite ethical and logistical challenges, the future of AI in healthcare holds great promise, particularly in chronic disease management, and it is of the utmost importance to maintain ethical standards while integrating AI into healthcare.

“The potential of AI in healthcare is very exciting, but we must be cautious about ethics and privacy,” Bridges emphasized. “AI won't replace doctors; instead, it will augment their capabilities. We need to ensure AI is used responsibly, understanding its current limitations while recognizing its potential to transform healthcare.”