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Counting electric sheep: Dreaming of AI in sleep medicine
“Artificial intelligence (AI) in healthcare refers to the use of machine learning (ML), deep learning, natural language processing, and computer vision to process and analyze large amounts of health care data.”
The preceding line is a direct quote from ChatGPT when prompted with the question “What is AI in health care?” (OpenAI, 2022). AI has rapidly infiltrated our lives. From using facial recognition software to unlock our cellphones to scrolling through targeted media suggested by streaming services, our daily existence is interwoven with algorithms. With the recent introduction of GPT-3 (the model that powers ChatGPT) in late 2022 and its even more capable successor, GPT-4, in March 2023, AI will continue to dominate our everyday environment in even more complex and meaningful ways.
For sleep medicine, the initial applications of AI in this field have been innovative and promising. (OSA) (Bandyopadhyay A, et al. Sleep Breath. 2023;27[1]:39). Pépin and colleagues (JAMA Netw Open. 2020;3[1]:e1919657) combined ML with mandibular movement to diagnose OSA with a reasonable agreement to polysomnography as a novel home-based alternative for diagnosis. AI has also been used to predict adherence to positive airway pressure (PAP) therapy in OSA (Scioscia G, et al. Inform Health Soc Care. 2022;47[3]:274) and as a digital intervention tool accessed via a smartphone app for people with insomnia (Philip P, et al, J Med Internet Res. 2020;22[12]:e24268). The data-rich field of sleep medicine is primed for further advancements through AI, albeit with a few hurdles and regulations to overcome before becoming mainstream.
Future promise
Sleep medicine is uniquely positioned to develop robust AI algorithms because of its vast data trove. Using AI, scientists can efficiently analyze the raw data from polysomnography, consumer sleep technology (CST), and nightly remote monitoring (from PAP devices) to substantially improve comprehension and management of sleep disorders.
AI can redefine OSA through analysis of the big data available, rather than solely relying on the apnea-hypopnea index. In addition, novel variables such as facial structure; snoring index; temperature trends; and sleep environment, position, and timing using a camera-based contactless technology may be incorporated to enhance the diagnostic accuracy for OSA or better describe sleep quality. AI algorithms can also be embedded into the electronic health record (EHR) to facilitate screening for sleep disorders using patient characteristics, thus accelerating the recognition and evaluation of possible sleep disorders.
New ways of collecting data may deliver deeper insights into sleep health, as well. CST such as wearables, nearables, and phone applications are improving with each iteration, resulting in more data about sleep for millions of people over thousands of nights.
AI can help achieve precision medicine by integrating multimodal data to establish endotypes and phenotypes of various sleep disorders. Delineating endotypes and phenotypes allows for personalized treatment recommendations, which may improve patient adherence and health outcomes.
Treatment personalization can also be achieved through AI by predicting compliance to various therapies and responses, as well as by discovering alternative forms of delivery to accomplish desired health outcomes. For example, to predict PAP compliance, we can record a patient encounter and use natural language processing to analyze their opinion of their treatment, extracting relevant keywords and combining such processing with other available data, such as environmental factors, sleep schedule, medical history, and other information extracted from the EHR. As another example, AI can determine the optimal time for cancer therapy by predicting a patient’s circadian timing (Hesse J, et al. Cancers (Basel). 2020;12[11]:3103). Circadian timing of drug delivery may be relevant in other specialties including cardiovascular disease, endocrine disorders, and psychiatric conditions due to its associations with sleep. Integration of the various “-omics” (eg, proteomics, genomics, and transcriptomics) with physiologic, behavioral, and environmental data can offer opportunities for drug discovery and possible prediction of sleep disorders and sleep-related morbidity. Although generative pretrained transformers are currently used to predict text (ie, ChatGPT), it is theoretically possible to also apply this technique to identify patients at risk for future sleep disorders from an earlier age.
Challenges to an AI renaissance
Despite making strides in numerous specialties such as radiology, ophthalmology, pathology, oncology, and dermatology, AI has not yet gained mainstream usage. Why isn’t AI as ubiquitous and heavily entrenched in health care as it is in other industries? According to the National Academy of Medicine’s AI in Healthcare: The Hope, The Hype, The Promise, The Peril, there are several realities to address before we fully embrace the AI revolution (Matheny M, et al. 2019).
First, AI algorithms should be trained on quality data that are representative of the population. Interoperability between health care systems and standardization across platforms is required to access large volumes of quality data. The current framework for data gathering is limited due to regulations, patient privacy concerns, and organizational preferences. The challenges to data acquisition and standardization of information will continue to snarl progress unless there are legislative remedies.
Furthermore, datasets should be diverse enough to avoid introducing bias into the AI algorithm. If the dataset is limited and health inequities (eg, societal bias and social determinants of health) are excluded from the training set, then the outcome will perpetuate further explicit and implicit biases.
The Food and Drug Administration (FDA) reviews and authorizes AI/ML-enabled devices. Its current regulatory structure treats AI as a static process and does not allow for exercise of its intrinsic ability to continuously learn from additional data, thereby preventing it from becoming more accurate and evolving with the population over time. A more flexible approach is needed.
Lastly, recent advanced AI algorithms including deep learning and neural network methodology function like a “black box.” The models are not explainable or transparent. Without clear comprehension of its methods, acceptance in clinical practice will be guarded and further risk of inherent biases may ensue.
A path forward
But these challenges, like any, can be overcome. Research in the area of differential privacy and the adoption of recent data-sharing standards (eg, HL7 FHIR) can facilitate access to training data (Saripalle R, et al. J Biomed Inform. 2019;94:103188). Regulators are also open to incorporating feedback from the AI research community and industry in favor of innovation in this frenetic domain. The FDA developed the AI/ML Software as a Medical Device Action Plan in response to stakeholder feedback for oversight (FDA, 2021). Specifically, the “Good Machine Learning Practice” will be developed to describe AI/ML best practices (eg, data management, training, interpretability, evaluation, and documentation) to guide product development and standardization.
Sleep medicine has significantly progressed over the last several decades. Rather than maintain the status quo, AI can help fill the existing knowledge gaps, augment clinical practice, and streamline operations by analyzing and processing data at a volume and efficiency beyond human capacity. Fallibility is inevitable in machines and humans; however, like humans, machines can improve with continued training and exposure.
We asked ChatGPT about the future of AI in sleep medicine. It states that AI could have a “significant impact” on sleep disorders diagnosis, treatment, prevention, and sleep tracking and monitoring. Only time will tell if its claims are accurate.
Dr. Tan is Clinical Associate Professor with the Division of Sleep Medicine at the Stanford University School of Medicine. Dr. Bhargava is Clinical Professor with the Division of Pediatric Pulmonary, Asthma, and Sleep Medicine at the Stanford University School of Medicine.
“Artificial intelligence (AI) in healthcare refers to the use of machine learning (ML), deep learning, natural language processing, and computer vision to process and analyze large amounts of health care data.”
The preceding line is a direct quote from ChatGPT when prompted with the question “What is AI in health care?” (OpenAI, 2022). AI has rapidly infiltrated our lives. From using facial recognition software to unlock our cellphones to scrolling through targeted media suggested by streaming services, our daily existence is interwoven with algorithms. With the recent introduction of GPT-3 (the model that powers ChatGPT) in late 2022 and its even more capable successor, GPT-4, in March 2023, AI will continue to dominate our everyday environment in even more complex and meaningful ways.
For sleep medicine, the initial applications of AI in this field have been innovative and promising. (OSA) (Bandyopadhyay A, et al. Sleep Breath. 2023;27[1]:39). Pépin and colleagues (JAMA Netw Open. 2020;3[1]:e1919657) combined ML with mandibular movement to diagnose OSA with a reasonable agreement to polysomnography as a novel home-based alternative for diagnosis. AI has also been used to predict adherence to positive airway pressure (PAP) therapy in OSA (Scioscia G, et al. Inform Health Soc Care. 2022;47[3]:274) and as a digital intervention tool accessed via a smartphone app for people with insomnia (Philip P, et al, J Med Internet Res. 2020;22[12]:e24268). The data-rich field of sleep medicine is primed for further advancements through AI, albeit with a few hurdles and regulations to overcome before becoming mainstream.
Future promise
Sleep medicine is uniquely positioned to develop robust AI algorithms because of its vast data trove. Using AI, scientists can efficiently analyze the raw data from polysomnography, consumer sleep technology (CST), and nightly remote monitoring (from PAP devices) to substantially improve comprehension and management of sleep disorders.
AI can redefine OSA through analysis of the big data available, rather than solely relying on the apnea-hypopnea index. In addition, novel variables such as facial structure; snoring index; temperature trends; and sleep environment, position, and timing using a camera-based contactless technology may be incorporated to enhance the diagnostic accuracy for OSA or better describe sleep quality. AI algorithms can also be embedded into the electronic health record (EHR) to facilitate screening for sleep disorders using patient characteristics, thus accelerating the recognition and evaluation of possible sleep disorders.
New ways of collecting data may deliver deeper insights into sleep health, as well. CST such as wearables, nearables, and phone applications are improving with each iteration, resulting in more data about sleep for millions of people over thousands of nights.
AI can help achieve precision medicine by integrating multimodal data to establish endotypes and phenotypes of various sleep disorders. Delineating endotypes and phenotypes allows for personalized treatment recommendations, which may improve patient adherence and health outcomes.
Treatment personalization can also be achieved through AI by predicting compliance to various therapies and responses, as well as by discovering alternative forms of delivery to accomplish desired health outcomes. For example, to predict PAP compliance, we can record a patient encounter and use natural language processing to analyze their opinion of their treatment, extracting relevant keywords and combining such processing with other available data, such as environmental factors, sleep schedule, medical history, and other information extracted from the EHR. As another example, AI can determine the optimal time for cancer therapy by predicting a patient’s circadian timing (Hesse J, et al. Cancers (Basel). 2020;12[11]:3103). Circadian timing of drug delivery may be relevant in other specialties including cardiovascular disease, endocrine disorders, and psychiatric conditions due to its associations with sleep. Integration of the various “-omics” (eg, proteomics, genomics, and transcriptomics) with physiologic, behavioral, and environmental data can offer opportunities for drug discovery and possible prediction of sleep disorders and sleep-related morbidity. Although generative pretrained transformers are currently used to predict text (ie, ChatGPT), it is theoretically possible to also apply this technique to identify patients at risk for future sleep disorders from an earlier age.
Challenges to an AI renaissance
Despite making strides in numerous specialties such as radiology, ophthalmology, pathology, oncology, and dermatology, AI has not yet gained mainstream usage. Why isn’t AI as ubiquitous and heavily entrenched in health care as it is in other industries? According to the National Academy of Medicine’s AI in Healthcare: The Hope, The Hype, The Promise, The Peril, there are several realities to address before we fully embrace the AI revolution (Matheny M, et al. 2019).
First, AI algorithms should be trained on quality data that are representative of the population. Interoperability between health care systems and standardization across platforms is required to access large volumes of quality data. The current framework for data gathering is limited due to regulations, patient privacy concerns, and organizational preferences. The challenges to data acquisition and standardization of information will continue to snarl progress unless there are legislative remedies.
Furthermore, datasets should be diverse enough to avoid introducing bias into the AI algorithm. If the dataset is limited and health inequities (eg, societal bias and social determinants of health) are excluded from the training set, then the outcome will perpetuate further explicit and implicit biases.
The Food and Drug Administration (FDA) reviews and authorizes AI/ML-enabled devices. Its current regulatory structure treats AI as a static process and does not allow for exercise of its intrinsic ability to continuously learn from additional data, thereby preventing it from becoming more accurate and evolving with the population over time. A more flexible approach is needed.
Lastly, recent advanced AI algorithms including deep learning and neural network methodology function like a “black box.” The models are not explainable or transparent. Without clear comprehension of its methods, acceptance in clinical practice will be guarded and further risk of inherent biases may ensue.
A path forward
But these challenges, like any, can be overcome. Research in the area of differential privacy and the adoption of recent data-sharing standards (eg, HL7 FHIR) can facilitate access to training data (Saripalle R, et al. J Biomed Inform. 2019;94:103188). Regulators are also open to incorporating feedback from the AI research community and industry in favor of innovation in this frenetic domain. The FDA developed the AI/ML Software as a Medical Device Action Plan in response to stakeholder feedback for oversight (FDA, 2021). Specifically, the “Good Machine Learning Practice” will be developed to describe AI/ML best practices (eg, data management, training, interpretability, evaluation, and documentation) to guide product development and standardization.
Sleep medicine has significantly progressed over the last several decades. Rather than maintain the status quo, AI can help fill the existing knowledge gaps, augment clinical practice, and streamline operations by analyzing and processing data at a volume and efficiency beyond human capacity. Fallibility is inevitable in machines and humans; however, like humans, machines can improve with continued training and exposure.
We asked ChatGPT about the future of AI in sleep medicine. It states that AI could have a “significant impact” on sleep disorders diagnosis, treatment, prevention, and sleep tracking and monitoring. Only time will tell if its claims are accurate.
Dr. Tan is Clinical Associate Professor with the Division of Sleep Medicine at the Stanford University School of Medicine. Dr. Bhargava is Clinical Professor with the Division of Pediatric Pulmonary, Asthma, and Sleep Medicine at the Stanford University School of Medicine.
“Artificial intelligence (AI) in healthcare refers to the use of machine learning (ML), deep learning, natural language processing, and computer vision to process and analyze large amounts of health care data.”
The preceding line is a direct quote from ChatGPT when prompted with the question “What is AI in health care?” (OpenAI, 2022). AI has rapidly infiltrated our lives. From using facial recognition software to unlock our cellphones to scrolling through targeted media suggested by streaming services, our daily existence is interwoven with algorithms. With the recent introduction of GPT-3 (the model that powers ChatGPT) in late 2022 and its even more capable successor, GPT-4, in March 2023, AI will continue to dominate our everyday environment in even more complex and meaningful ways.
For sleep medicine, the initial applications of AI in this field have been innovative and promising. (OSA) (Bandyopadhyay A, et al. Sleep Breath. 2023;27[1]:39). Pépin and colleagues (JAMA Netw Open. 2020;3[1]:e1919657) combined ML with mandibular movement to diagnose OSA with a reasonable agreement to polysomnography as a novel home-based alternative for diagnosis. AI has also been used to predict adherence to positive airway pressure (PAP) therapy in OSA (Scioscia G, et al. Inform Health Soc Care. 2022;47[3]:274) and as a digital intervention tool accessed via a smartphone app for people with insomnia (Philip P, et al, J Med Internet Res. 2020;22[12]:e24268). The data-rich field of sleep medicine is primed for further advancements through AI, albeit with a few hurdles and regulations to overcome before becoming mainstream.
Future promise
Sleep medicine is uniquely positioned to develop robust AI algorithms because of its vast data trove. Using AI, scientists can efficiently analyze the raw data from polysomnography, consumer sleep technology (CST), and nightly remote monitoring (from PAP devices) to substantially improve comprehension and management of sleep disorders.
AI can redefine OSA through analysis of the big data available, rather than solely relying on the apnea-hypopnea index. In addition, novel variables such as facial structure; snoring index; temperature trends; and sleep environment, position, and timing using a camera-based contactless technology may be incorporated to enhance the diagnostic accuracy for OSA or better describe sleep quality. AI algorithms can also be embedded into the electronic health record (EHR) to facilitate screening for sleep disorders using patient characteristics, thus accelerating the recognition and evaluation of possible sleep disorders.
New ways of collecting data may deliver deeper insights into sleep health, as well. CST such as wearables, nearables, and phone applications are improving with each iteration, resulting in more data about sleep for millions of people over thousands of nights.
AI can help achieve precision medicine by integrating multimodal data to establish endotypes and phenotypes of various sleep disorders. Delineating endotypes and phenotypes allows for personalized treatment recommendations, which may improve patient adherence and health outcomes.
Treatment personalization can also be achieved through AI by predicting compliance to various therapies and responses, as well as by discovering alternative forms of delivery to accomplish desired health outcomes. For example, to predict PAP compliance, we can record a patient encounter and use natural language processing to analyze their opinion of their treatment, extracting relevant keywords and combining such processing with other available data, such as environmental factors, sleep schedule, medical history, and other information extracted from the EHR. As another example, AI can determine the optimal time for cancer therapy by predicting a patient’s circadian timing (Hesse J, et al. Cancers (Basel). 2020;12[11]:3103). Circadian timing of drug delivery may be relevant in other specialties including cardiovascular disease, endocrine disorders, and psychiatric conditions due to its associations with sleep. Integration of the various “-omics” (eg, proteomics, genomics, and transcriptomics) with physiologic, behavioral, and environmental data can offer opportunities for drug discovery and possible prediction of sleep disorders and sleep-related morbidity. Although generative pretrained transformers are currently used to predict text (ie, ChatGPT), it is theoretically possible to also apply this technique to identify patients at risk for future sleep disorders from an earlier age.
Challenges to an AI renaissance
Despite making strides in numerous specialties such as radiology, ophthalmology, pathology, oncology, and dermatology, AI has not yet gained mainstream usage. Why isn’t AI as ubiquitous and heavily entrenched in health care as it is in other industries? According to the National Academy of Medicine’s AI in Healthcare: The Hope, The Hype, The Promise, The Peril, there are several realities to address before we fully embrace the AI revolution (Matheny M, et al. 2019).
First, AI algorithms should be trained on quality data that are representative of the population. Interoperability between health care systems and standardization across platforms is required to access large volumes of quality data. The current framework for data gathering is limited due to regulations, patient privacy concerns, and organizational preferences. The challenges to data acquisition and standardization of information will continue to snarl progress unless there are legislative remedies.
Furthermore, datasets should be diverse enough to avoid introducing bias into the AI algorithm. If the dataset is limited and health inequities (eg, societal bias and social determinants of health) are excluded from the training set, then the outcome will perpetuate further explicit and implicit biases.
The Food and Drug Administration (FDA) reviews and authorizes AI/ML-enabled devices. Its current regulatory structure treats AI as a static process and does not allow for exercise of its intrinsic ability to continuously learn from additional data, thereby preventing it from becoming more accurate and evolving with the population over time. A more flexible approach is needed.
Lastly, recent advanced AI algorithms including deep learning and neural network methodology function like a “black box.” The models are not explainable or transparent. Without clear comprehension of its methods, acceptance in clinical practice will be guarded and further risk of inherent biases may ensue.
A path forward
But these challenges, like any, can be overcome. Research in the area of differential privacy and the adoption of recent data-sharing standards (eg, HL7 FHIR) can facilitate access to training data (Saripalle R, et al. J Biomed Inform. 2019;94:103188). Regulators are also open to incorporating feedback from the AI research community and industry in favor of innovation in this frenetic domain. The FDA developed the AI/ML Software as a Medical Device Action Plan in response to stakeholder feedback for oversight (FDA, 2021). Specifically, the “Good Machine Learning Practice” will be developed to describe AI/ML best practices (eg, data management, training, interpretability, evaluation, and documentation) to guide product development and standardization.
Sleep medicine has significantly progressed over the last several decades. Rather than maintain the status quo, AI can help fill the existing knowledge gaps, augment clinical practice, and streamline operations by analyzing and processing data at a volume and efficiency beyond human capacity. Fallibility is inevitable in machines and humans; however, like humans, machines can improve with continued training and exposure.
We asked ChatGPT about the future of AI in sleep medicine. It states that AI could have a “significant impact” on sleep disorders diagnosis, treatment, prevention, and sleep tracking and monitoring. Only time will tell if its claims are accurate.
Dr. Tan is Clinical Associate Professor with the Division of Sleep Medicine at the Stanford University School of Medicine. Dr. Bhargava is Clinical Professor with the Division of Pediatric Pulmonary, Asthma, and Sleep Medicine at the Stanford University School of Medicine.