WORCESTER MEDICINE
AI in Healthcare: Balancing Innovation with Regulation Vinit Gilvaz, MD Zeba Hashmath, MD
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ver the past few decades, computers have revolutionized the practice of medicine. They allow easy access to patient records, streamline provider communication, track prescriptions, and have enhanced our radiodiagnostic capabilities. One of the greatest impacts of computers on healthcare has resulted from the use of electronic medical records (EMR) to document and store patient information. They have taken us Art created using Mid Journey away from clipboards and paper charts and given us access to troves of patient data - all in one place. However, the widespread implementation of EMRs did not emerge from a technological breakthrough but rather, a legislative move. The Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009 incentivized healthcare centers nationwide to adopt EMRs, by rewarding compliance and penalizing non-adherence. As a result, EMR-adoption in the United States soared, with over 95% adoption in most regions by 2015. Although it is arguable whether this legislative policy achieved its specific objective of decreasing healthcare spending, its influence on medical practice across the country is undeniable. Several parallel advancements in medicine fueled by research in multiomics and advanced imaging have translated to more testing on the clinical front, allowing for nuanced data to be collected on individual patients. These advancements, coupled with the rise of smart sensors (activity trackers, continuous glucose monitors, long-term cardiac monitors, etc.), have resulted in an explosion of patient data. Currently, we amass several exabytes (billion gigabytes) of healthcare data annually, a figure that is projected to grow exponentially over the coming years. While intended to enhance patient care, this vast pool of data often leaves healthcare professionals feeling overburdened and overwhelmed. As Dr. Atul Gawande rightly put it, “The volume and complexity of what we know has exceeded our individual ability to deliver its benefits correctly, safely, or reliably. Knowledge has both saved us and burdened us.” Herein lies the potential of artificial intelligence (AI). AI-based tools can utilize these large data sets to identify patterns and generate clinically meaningful insights. AI is an umbrella term for computer algorithms capable of mimicking human intellect. It includes approaches such as machine learning (ML) and deep learning. ML models, as the name implies, learn from examples (trained) rather than being guided by predefined rules. They can improve and adapt in light of new information. Quite like humans, they ‘learn’ as they ‘experience’ new data. Deep learning is a subset of ML that employs artificial neural networks (ANNs), which are multi-layered computational structures inspired by the neural connections in the brain. Most of the
Winter 2023
A.I. In Medicine
recent breakthroughs in AI have been largely attributed to deep learning [1]. The more data an ML or deep learning model is trained on, the better it performs, making it the ideal tool to process the abundance of patient data we accumulate on a daily basis. From precisely detecting sepsis patients at risk of clinical deterioration [2] to forecasting 30-day readmissions in heart failure patients [3], there are countless examples of AI models accurately predicting patient outcomes. Advancements in computer vision have enabled automatic detection of pathology from radiographic images, often more accurately than trained radiologists [1]. Lately, a lot of attention has been focused on ‘foundation models,’ which are AI models trained on vast amounts of unlabeled data. Large language models (LLMs), such as OpenAI’s Chat GPT and Google’s PaLM, are prime examples. They can provide detailed answers to complex medical questions and enable natural interactions with computers. Recent iterations of these models have shown promise in answering USMLE (United States Medical Licensing Exam)-style questions with high accuracy and are currently being evaluated for their potential as clinical decision support systems [4]. Needless to say, the possibilities of AI in medicine are endless. However, despite all the progress made thus far, we are yet to see widespread implementation of AI tools in healthcare. There are several reasons for this, one of them being the technical challenges associated with AI models. Poor interpretability of large-scale models and the risk of ‘data hallucination’ with some of the newer LLMs are among the many technical hurdles that must be overcome. Another significant barrier to the broad adoption of AI in healthcare is the inability of regulation to match the pace of innovation. Like with EMR adoption, we need a combination of congressional guidance and enforceable regulation to ensure safe and effective adoption across the country. This is easier said than done, especially when trying to keep pace with a fastmoving field like AI. Big Tech has always been known to “move fast and break things” and aims to disrupt industries they venture into. Our regulatory bodies, on the other hand, have been known for their slow and measured approach. This has delayed widespread commercial implementation and deprived patients of the potential benefits of AI. In the United States, the Food and Drug Administration (FDA) oversees the safety and effectiveness of drugs, medical devices, and food products. In 2021, the FDA issued the ‘AI/ML-based Software as a Medical Device (SaMD) Action Plan’, supporting the development of methods to evaluate AI algorithms in healthcare. These, however, only
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