Eyeworld

I ARTIFICIAL INTELLIGENCE N FOCUS 46 | EYEWORLD | AUGUST 2020 Contact Ho: achomd@gmail.com Joseph: aejoseph@eyeboston.com Lim: jennylim@uic.edu Stoller: gstoller@ocli.net results are applicable across diverse populations. "The dynamic nature of an AI system is that machine learning makes it difficult to predict at what point the algorithm should be reviewed," he said. There is also a risk for false negatives. Impact on clinical trials "I think we're going through a learning phase about how AI can be used in clinical trials," Dr. Stoller said. He thinks AI could help identi- fy patients most likely to develop disease or progress and could impact dosing regimens used in clinical trials. "I think there's a great deal of promise," he said. "It's not something that's been widely adopted yet in the ophthalmic community, but I think we're in the early phases of seeing that paradigm shift and embracing the technology." AI could pinpoint groups at higher risk of progression, identifying subgroups that would be best suited for preventive therapies, Dr. Lim said. In addition, AI could analyze the images for treatment response instead of relying on human graders. Lastly, AI may find patterns of disease that respond better to a treatment, she added. Dr. Joseph also thinks that AI could have an impact in clinical trials. "I think clinical trials are more rigorous in their controls and de- signs than regular clinical practice," he said. "I think it's a good opportunity for gathering and curating data and clinical images that would be useful in developing machine learning tools and accurate algorithms." Dr. Joseph added that he thinks researchers will realize when designing clinical trials that they may want to capture data in a fashion that will lend itself well to devel- oping AI tools, in addition to testing whatever medications or interventions that they're trying to study. be an essential component of the interpretation and implementation of AI analyses. Another risk is trying to obtain more information than what the dataset can yield, he added. "I think this is something that has been on the radar for awhile, but it takes time," Dr. Joseph said, adding that he expects a number of regulatory and ethical concerns for patient privacy and data protection. AI requires large amounts of patient data, he added, so there is the question of who owns the data. There are also technical limitations in terms of computa- tional power that's required. "We're producing or obtaining a large amount of clinical imaging, and that's what we want to use, but I think processing it takes man- power now and computational power later," Dr. Joseph said. "I think if you're looking further down the road, once we develop algorithms or tools, what kind of human oversights are we going to have?" He thinks some degree of hu- man oversight will be needed. Adding this new tool won't happen overnight, he said. "I think people think that this is not something that will be a replacement for clinical judgment, but it will certainly be an important tool in how we manage disease," Dr. Joseph said. Dr. Stoller said that one major challenge for AI will be acceptance from patients and doctors. "Many patients are willing to embrace high-tech devices, but I think they still might not trust computer diagnosis and would rather have their diagnosis made by an in-person visit," he said. With regard to physician acceptance, it's not always obvious how the computer algo- rithm reaches its conclusion, so the physicians are forced to trust the AI system without being able to evaluate the value of the metrics and data used by the computer program. Another challenge is managing the tech- nology itself. It's important to assess the quality and validation of the datasets to ensure the continued from page 45 Relevant disclosures Ho: None Joseph: None Lim: None Stoller: None

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