Table 2.

Barriers and possible solutions to incorporating AI in clinical practice.

	Possible Solutions
Challenges of validating AI-enabled algorithms
Variability in reported information about AI-enabled algorithms	Use predefined dataset description forms including detailed information and justification about the training dataset such as: Number of individuals in the dataset Inclusion/exclusion criteria Relevant physiological descriptors such as age, sex, and race Relevant clinical descriptors such as clinical condition, comorbidities, and medications Inclusion criteria of recruited population (ie, information about where and why the data were collected): routine clinical diagnosis of a specific sleep disorder, as part of a research study, a population-based sample, etc Source of testing dataset, specifically whether it comprises the same population as the training dataset or not Performance metrics details utilized to compare performance with standard of care Access to code used for the study and the final weights of the model and its characteristics
Lack of generalizability	Create external heterogenous validation resources that can compare AI algorithm performance with standard of care Need for prospective studies comparing algorithms with standard of care
Overfitting AI-enabled algorithm	Separate validation data from training data Leave-one-institute-out cross-validation Identify common information sources that may be present in validation and training data
Need for reproducibility and repeatability	Test the AI-enabled algorithm on multiple datasets that were built for similar intended use and indication. Of note, certain algorithms, such as those utilizing deep learning, should not be tested on the same training dataset; the training, validation, and test datasets always need to be clearly distinguished.
Researcher-clinician-industry collaboration	Involvement of all key stakeholders throughout the process of building and validating the AI-enabled algorithms through strategic meetings along the timeline
Challenges of implementing AI-enabled technology in practice
Wide variety of AI-enabled products to choose from	Key points for consideration: Will it make an impact on clinical care? Will it make an impact on productivity? Will the returns be sustained or diminish with time? Does it apply to my patients?
Lack of knowledge about AI algorithms	Increase in awareness and education regarding how AI algorithms learn and what are the various biases associated with its use
Lack of knowledge about when to use AI algorithms	Need for prospective studies assessing clinical noninferiority in a similar population and investigating how the AI–human team performs.
Lack of ease in operability	User-friendly interface, improved findability and accessibility
Physiological explanation of the working model	An explanation of why the sleep data show high risk (eg, dementia, where AI says it is because of decreased delta-to-theta power ratio during N3, eg, narcolepsy, where AI says it is because of an SOREMP during nocturnal sleep and presence of mixed REM-wake states)
Difficulty in utilizing AI as a tool complementing human skills	AI algorithm highlights the epochs that were difficult to predict or shows the features used to make predictions for clinician oversight, using natural human languages such as ChatGPT (OpenAI, San Francisco, California, USA)51
Patient privacy and workflow integration concerns	Educate users on how to maintain privacy in the era of cloud-based systems Invest in secure access portals Dedicated IT team providing logistical support
Continuous updates of AI algorithms can create differences in clinical outcome	Aftermarket surveillance by the manufacturing company and the need to revalidate algorithms after significant updates
Patient access to AI algorithm-generated clinical data	Need for shared decision making with clinician taking the lead to integrate data from medical-grade and consumer-grade devices and formulating treatment plans with patient input

AI = artificial intelligence, IT = internet technology, REM = rapid eye movement, SOREMP = sleep-onset REM period.