Advances in Artificial Intelligence
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].
Contents
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.
Generative AI
Another advance in artificial intelligence (AI) is generative AI. The key word is generative, which intimates that something novel is being generated. Generative AI technology relies on deep-learning algorithms to generate new content (text, audio, etc) from a wide variety of sources. These sources can be both unstructured (clinical notes, recordings, images (and structured data sets (numerical values). [9]
Some of the excitement behind this stems from the ability to create an aggregated summary from all the disparate sources of information present in a person’s medical record, in a short time frame. This could have the potential to save providers lots of time and effort procuring the right information, which they normally must do every time they see a patient. This allows for providers to focus most of their time on focus on the patient and tend to their care plans. Consequently, this can have an impact on reducing burnout as well, given that many clinicians indicate time sifting through the electronic medical record as a key factor in their fatigue. Another benefit is that generative AI allows for the transition to person-centered care. Many patients complain that clinicians spend more time behind a computer than working with them during encounters; generative AI champions believe this technology can flip the narrative. While there is a lot of positivity around this topic, generative AI’s output needs to be vetted for accuracy. Some type of proof reading is needed to ensure the final product is accurate and meets the standards of clinical documentation, for example.
Some industry focused applications of generative AI are making headwinds. For payors, member engagement and personalized outreach could be streamlined on the back end by reducing the administrative burden backlog. Additionally, prior authorization claims could be addressed in a speedier fashion. For health systems, automation of discharge summaries and streamlining administrative burdens there as well can have huge impact on care coordination, quality, and safety.
Ambient AI
Another advance in AI is called ambient AI. This differs from generative AI in that ambient AI focuses on passively documenting and capturing real time information for patients. Examples of this include ambient AI with regards to note-taking during a patient encounter in the ambulatory setting. Another example includes information being derived in real-time from wearables and remote patient monitoring (RPM) devices. The important aspect of this is that the technology focuses on capturing the essence and purpose of a patient interaction in real-time. With regards to wearables and RPM, ambient AI provides us with more data points on the chronic conditions of patients, for example, which allows clinicians to have greater insight and create more nuanced care plans. Within the realm of clinical documentation, it will significantly reduce the documentation burden on providers without disrupting their normal workflows and allow them to be more in tune with their patient care responsibilities. This technology has advanced significantly over the last few years where now the passive documentation and capture of conversation can happen in real time. [10]
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.
References
1. https://www.nytimes.com/2016/12/14/magazine/the-great-ai-awakening.html
2. https://itunes.apple.com/us/app/parkinson-mpower-study-app/id972191200?mt=8
3. https://techcrunch.com/2016/11/03/plushcare-nabs-8m-series-a-to-prove-telehealth-can-go-mainstream/
4. https://www.cbinsights.com/research/artificial-intelligence-startups-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. http://doi.org/10.1001/jama.2016.17216
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. http://doi.org/10.1038/nature21056
Elizabeth Gamino
10. https://www.mcpdigitalhealth.org/article/S2949-7612(23)00035-4/fulltext
Submitted by (Suvrat Chandra)
