AI’s Role in Orthopedics: Advancements and Challenges

By Sumona Bose

January 8, 2024

Introduction

Artificial intelligence (AI) has rapidly evolved from a theoretical concept to a practical application with the help of affordable computational power and the availability of large data sets. In the healthcare field, AI has the potential to invigorate orthopedic treatment by addressing specific challenges such as image recognition, preoperative risk assessment, clinical decision-making, and analysis of massive data sets. A recent systematic review by Cabitza et al. highlighted the increasing number of initiatives that leverage AI to tackle orthopedic-specific problems. The review found that spine pathology, osteoarthritis (OA) detection and prediction, and imaging of bone and cartilage were the most studied topics. Machine learning (ML) techniques, particularly deep learning (DL) and support vector machines (SVMs), were the most frequently applied algorithms. Medical imaging data emerged as the most commonly used input source. This article will explore AI’s role in Orthopedics.

Figure 1: Remote patient monitoring. Data for health monitoring applications can be captured using a wide array of pervasive sensors that are worn on the body, implanted, or captured through ambient sensors, e.g., inertial motion sensors, electrocardiogram patches, smart watches, electroencephalograms, and prosthetics.

Discussing Challenges in AI Research

While the potential of AI in orthopedics is promising, there are challenges that need to be addressed. One of the main concerns is the accusation that AI in medicine provides no advantage over traditional statistics. However, this claim is unfounded as AI has proven to be effective in various medical applications. It is important to view traditional statistics and ML as lying on a spectrum rather than as distinct techniques.

To successfully implement AI in orthopedics, certain prerequisites need to be met. These include big accurate data sets, powerful computers, cloud computing, and open-source algorithmic development. However, there are additional challenges associated with AI research, such as privacy issues and biasing. If the data used to train AI models are biased, it can lead to systematic analytical mistakes. For example, if the training data predominantly consists of medical records of white men, the AI may make less accurate predictions for women, ethnic minorities, or other underrepresented groups.

Furthermore, AI lacks traits that are uniquely human, such as morality and intuition, making it prone to absurd mistakes. Adversarial attacks, which intentionally bias or force AI models to make errors, pose a significant threat to patient care and safety. These attacks can impact medical diagnosis, decision support, insurance claims, drug and device approvals, and clinical trials. AI’s role in Orthopedics highlight an important area in the medical industry where practical knowledge will meet theoretical insights.

Conclusion

Addressing these challenges requires careful consideration. Regulating AI too early may stifle innovation and result in inaccurate threat-based models and unwieldy regulatory structures. However, delaying regulation may leave healthcare systems vulnerable to adversarial attacks.

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