Advances in Artificial Intelligence

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Artificial Intelligence (AI) is being used in almost every aspect of science and engineering. In AI tasks, computers perform tasks that have normally required human intelligence. AI has shown significant progress in speech recognition and natural language processing [1]

In 2017, JASON published a study that investigated how AI will shape the future of public health, community health, and healthcare delivery [5]. One finding from the study was that AI can perform clinical diagnostics on medical images equal to expert clinician diagnosis, on specific cases

Although there is an abundance of healthcare data available, it is often not easily available to create algorithms that aid in data-driven decisions. Health data is privacy protected therefore it makes sharing the data very difficult compared to other datasets. The lack of interoperability between electronic health records also poses an issue for the collection of data [6].

Medical Imaging / Diagnostics

The JASON study cited a study where automated retinal image analysis was conducted. The algorithm was trained using a dataset of over 100,000 images. The dataset was reviewed by 3-7 ophthalmologists. The results from the algorithm were comparable to results derived from manual assessment [7]. This study demonstrates the potential of increased AI driven outcomes if accessibility is granted to valuable datasets. If this work continues the technology could be used to support clinical decision making, reduce the costs of a manual ophthalmologist assessment, or extend medical services to underserved populations.

Another study that was performed involved a training set of over 125,000 dermatologists labeled images from 18 online repositories. The study used a convolution neural network algorithm to classify melanoma. The algorithm performed similarly to a dermatologist diagnosis [8].

Both studies demonstrate that an algorithm can perform levels similar to its training set.

Patient Data

There are currently may smartphone attachments and apps for monitoring one’s personal health. These devices empower individuals to monitor and understand their health; create large amounts of data that could be used for AI applications; and capture health data that can be shared with clinicians and researchers. If patients were to provide access to mobile health data, this could enhance the research community’s ability to build more insights into public health through AI.

To collect the data, participants must provide consent. One study was able to do this through the creation of an app that was available through the Apple Store. The app was created for individuals with Parkinson disease. The app explained the study, those who were eligible to participate were asked if they wanted to share their data. Of the 12,200 eligible participants, 78% agreed to share their data [2] [3]. This is one way to collect patient data in order to further AI techniques to study health data, but patients must consent and there should be a clear plan as to how the data will be used.

Challenges and the Future of AI

The JASON study found the following barriers to using AI in healthcare, the lack of acceptance of AI in a clinical setting; the obstacles to obtain data from personal networked devices; the lack of availability of quality training data to build AI applications; missing data streams in current data collection (such as environmental factors); challenges to building on the success of AI in other domains; and understanding the limitations of AI in healthcare [5].

A 2016 study of AI related healthcare startups are primarily based in the US, 75 of the 106 startups are in the US [4]. Regardless of the current challenges of using AI in healthcare, there are organizations working towards the goal of introducing more AI in healthcare.






5. JASON 2017, Artificial Intelligence for Health and Health Care. JSR-17-Task-002.

6. JASON 2013, A Robust Health Data Infrastructure. JSR-13-Task-007.

7. Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., et al. (2016). Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. Jama, 316(22), 2402.

8. Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancerwith deep neural networks. Nature, 542(7639), 115– 118.

Elizabeth Gamino