Increasing volumes of healthcare data and growing complexities in diagnoses and treatment are escalating the need for Artificial Intelligence (AI) in the healthcare sector. Benjamin Freedman, MD, Chief of Radiology at Memorial Healthcare System, deliberated on this development during an Aidoc-hosted webinar, asserting that this is the ‘right time’ for AI adoption in healthcare.
As per Freedman, several variables influence the adoption of AI in healthcare, including quality, speed, and safety. Today’s healthcare system is data-driven, and AI has the potential to efficiently handle, analyze, and interpret patient data and electronic health records. It can assist doctors in decision making, improving patient treatment outcomes, reducing false positives and errors, recognizing severe conditions earlier, and helping anticipate potential health issues. Overall, adopting AI can accelerate diagnosis and enhance patient care quality.
An ever-increasing patient population and an ageing demographic necessitate improved efficiencies in disease prediction and patient monitoring, which AI can provide. AI solutions enable healthcare providers to offer personalized, value-based care, delivering better patient experiences, and improving public health.
Implementation of AI in healthcare also presents the scope for cost-effectiveness and increased productivity. AI algorithms can reduce the time spent on routine, manual tasks, thereby allowing doctors to dedicate more time in patient care. As for the financial aspect, AI has the potential to save billions by detecting diseases earlier, managing chronic conditions effectively, and even avoiding unnecessary hospitalizations.
However, Freedman highlighted the need for a robust framework for the application of AI in healthcare, including defining use cases and understanding the technology’s limitations. AI should be trained and validated on a diverse range of data to ensure its applicability across patient populations. It’s also crucial to establish ethical guidelines to maintain transparency and to protect patient privacy.
Freedman explained the steps Memorial Healthcare System has taken in this direction. It has started developing a multidisciplinary AI strategy with radiology as its starting point. They have assembled an AI Committee with representatives from different departments and are investing in the training and development of their workforce. They aim to become a learning healthcare system by integrating AI into routine medical practice and augmenting doctors’ capabilities.
Freedman acknowledged that introducing AI doesn’t imply replacing doctors but enhancing their abilities to make better clinical decisions. It’s not about machines becoming doctors; it’s about creating a perfect blend of human intelligence and machine learning to improve healthcare delivery.
Exemplifying this, a pilot program at Memorial Healthcare System used AI to interpret CT results and identify patients potentially at risk from intracranial haemorrhage- leading to quicker diagnoses and improved patient outcomes.
Freedman concluded by underscoring the importance of continual evaluation of AI performance and reviewing feedback to refine the algorithms, thus ensuring they remain efficient and beneficial for patients. Additionally, fostering partnerships with technology providers like Aidoc would be instrumental in driving the growth and development of AI in healthcare.
In sum, current healthcare challenges necessitate the adoption of AI, with the time being ripe for this technology to augment healthcare delivery. However, this also calls for careful planning and execution, ensuring AI applications are ethical, practical, and beneficial for patients. The Memorial Healthcare System’s steps suggest a way forward, converting healthcare from a reactive discipline to a proactive one that uses AI’s advantages.
