Authors: Moh Noori and Ben Vitenson

Opportunities of AI in Healthcare?

“There are things that machines will never do well, and then others where they’ll be exceeding what any human can do … when you put the two together it’s a very powerful package.” Eric Topol, Cardiologist, and founder of the Scripps Research Translational Institute.

The healthcare industry is being revolutionized with the further introduction of machine learning and artificial intelligence. Artificial intelligence has proven to be capable of producing predictions and making decisions that humans alone could not produce. The combination of AI and machine learning predictions or contributions mixed with the knowledge of human physicians has strongly improved healthcare, many times decreasing costs to patients and increasing successful treatments. This success has led to an ever-growing market for AI in healthcare.

The market for artificial intelligence in 2021 was estimated to be worth around 11 billion dollars worldwide. It is forecasted that this industry will grow to be worth around 180 billion dollars by the year 2030. The industry is highly specialized, with most companies developing AI that pertains to specific medical practices. For example, ScriptChain Health focuses on preventative care and predictions for heart failure, hypertension, atrial fibrillation, and readmission using tabular and text based data. Most start-ups focus on training AI with specific intention towards certain conditions, like ScripChain Health has done for cardiovascular diseases. By specializing on certain conditions AI healthcare companies have maximized the quality and function of their AI and have been able to be more effective with implementation. The largest challenge to AI healthcare companies is the risky integration of their technologies and the conservatism of the healthcare industry. The healthcare industry in general is conservative and hesitant to fully integrate new technologies because of the implications they can have on patients’ well-being. Most hospitals view the risk of full AI implementation to be high but have been improving over the years. Nevertheless, as digital health is becoming more prominent and AI solutions are improving there is a gradual increase in the willingness of hospitals for AI implementation. ScriptChain Health has come up with a plan to be able to minimize the fear of using such technologies and to help physicians adapt to technologies to assist them in their everyday responsibilities of identifying and diagnosing high risk patients.

To minimize risk and maximize efficiency of their AI technology, ScriptChain Health uses federated learning systems to train their AI. Federated learning systems use multiple sets of shared data to collaboratively train a deep learning global model in a distributed way using alternative devices. Multiple parties download a model and share data independently to create a collaborative and iterative learning system that is fully encrypted. Federated learning is one of the methods used to improve AI technology in healthcare. Other common systems and methods are SATs, Graph Neural Networks, and cross validation as testing methods. Transformers encode input sequences in AI, making the AI self-attentive and able to place different significance levels on data. Graph Neural Networks are used to interpret graphs and create inferences based on the data in graphs. With the combination of transformers and Graph Neural Networks, AI can create self-attentive inferences based on data that is realistically weighed. Lastly, many AI systems include cross validation testing. Cross validation testing uses different data iterations to train and check AI predictions. Cross validation ensures the most accurate outcomes are produced and is essential with AI in healthcare. ScriptChain Health AI employs the methods described as well as others to produce useful and accurate predictions for heart disease. Reliable and efficient AI systems are producing more healthcare solutions and launching the AI healthcare industry. Today is the time to make sure that AI models are well rounded by being trained on diverse datasets to represent the U.S. and continue to be retrained in real time by adding more workers to our global model. With the help of healthcare institutions, we can ensure better patient outcomes through prevention and reduce the cost of care for all.

References:

Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future Healthc J. 2019 Jun;6(2):94-98. doi: 10.7861/futurehosp.6-2-94. PMID: 31363513; PMCID: PMC6616181.

Kulkov, I. (2021), “Next-generation business models for artificial intelligence start-ups in the healthcare industry”, International Journal of Entrepreneurial Behavior & Research, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/IJEBR-04-2021-0304

Stewart, Conor. “AI in Healthcare Market Size Worldwide 2030.” Statista, 28 Sept. 2022, https://www.statista.com/statistics/1334826/ai-in-healthcare-market-size-worldwide/#statisticContainer.