Wearable Technology in Clinical Practice for Depressive Disorder

Seasonal, weekly, and individual variations in long-term use of wearable medical devices for diabetes management

Wearable medical-grade devices are transforming the standard of care for prevalent chronic conditions like diabetes. Yet, adoption and long-term use remain a challenge for many people. In this study, we investigate patterns of consistent versus disrupted use of continuous glucose monitors (CGMs) through analysis of more than 118,000 days of data, with over 22 million blood glucose samples, from 108 young adults with type 1 diabetes (average: 3 years of CGM data per person). In this population, we found more consistent CGM use at the start and end of the year (e.g., January, December), and more disrupted CGM use in the middle of the year/warmer months (i.e., May to July). We also found more consistent CGM use on weekdays (Monday to Thursday) and during waking hours (6AM - 6PM), but more disrupted CGM use on weekends (Friday to Sunday) and during evening/night hours (7PM - 5AM). Only 52.7% of participants (57 out of 108) had consistent and sustained CGM use over the years (i.e., over 70% daily wear time for more than 70% of their data duration). From semi-structured interviews, we unpack factors contributing to sustained CGM use (e.g., easier and better blood glucose management) and factors contributing to disrupted CGM use (e.g., changes in insurance coverage, issues with sensor adhesiveness/lifespan, and college/life transitions). We leverage insights from this study to elicit implications for next-generation technology and interventions that can circumvent seasonal and other factors that disrupt sustained use of wearable medical devices for the goal of improving health outcomes.

Exploring depression in adults over a decade: a review of longitudinal studies

Depression, as a prevalent global mental health disorder, stands as one of the main causes of disability worldwide, imposing significant individual, societal, and economic burdens. While its heterogeneous nature is well recognized, growing evidence highlights the importance of understanding depression trajectories, which describe the long-term course and variability of depressive symptoms over time. These trajectories are shaped by a complex interplay of biological, psychological, and social factors. However, despite extensive research on depression's prevalence and risk factors, a comprehensive synthesis of trajectory patterns, their determinants, and their long-term implications remains limited. This review systematically examines the existing literature on depression trajectories in adults, identifying key influences such as age, gender, socioeconomic status, early life experiences, social support, physical health, lifestyle factors, and external stressors, including pandemics. By integrating findings from longitudinal and epidemiological studies, this review provides novel insights into the bidirectional relationship between depression and chronic health conditions, underscoring the need for a holistic, trajectory-based approach to mental health care. The findings have important implications for clinical practice, public health, and future research. Recognizing distinct trajectory patterns may facilitate earlier identification of high-risk individuals, inform the development of personalized interventions, and optimize the allocation of mental health resources. Furthermore, by elucidating the complex interconnections between depression and broader health determinants, this review establishes a foundation for advancing targeted, evidence-based interventions aimed at reducing the long-term burden of depression, particularly among vulnerable populations.

Applying AI in the Context of the Association Between Device-Based Assessment of Physical Activity and Mental Health: Systematic Review

BACKGROUND

Wearable technology is used by consumers worldwide for continuous activity monitoring in daily life but more recently also for classifying or predicting mental health parameters like stress or depression levels. Previous studies identified, based on traditional approaches, that physical activity is a relevant factor in the prevention or management of mental health. However, upcoming artificial intelligence methods have not yet been fully established in the research field of physical activity and mental health.

OBJECTIVE

This systematic review aims to provide a comprehensive overview of studies that integrated passive monitoring of physical activity data measured via wearable technology in machine learning algorithms for the detection, prediction, or classification of mental health states and traits.

METHODS

We conducted a review of studies processing wearable data to gain insights into mental health parameters. Eligibility criteria were (1) the study uses wearables or smartphones to acquire physical behavior and optionally other sensor measurement data, (2) the study must use machine learning to process the acquired data, and (3) the study had to be published in a peer-reviewed English language journal. Studies were identified via a systematic search in 5 electronic databases.

RESULTS

Of 11,057 unique search results, 49 published papers between 2016 and 2023 were included. Most studies examined the connection between wearable sensor data and stress (n=15, 31%) or depression (n=14, 29%). In total, 71% (n=35) of the studies had less than 100 participants, and 47% (n=23) had less than 14 days of data recording. More than half of the studies (n=27, 55%) used step count as movement measurement, and 44% (n=21) used raw accelerometer values. The quality of the studies was assessed, scoring between 0 and 18 points in 9 categories (maximum 2 points per category). On average, studies were rated 6.47 (SD 3.1) points.

CONCLUSIONS

The use of wearable technology for the detection, prediction, or classification of mental health states and traits is promising and offers a variety of applications across different settings and target groups. However, based on the current state of literature, the application of artificial intelligence cannot realize its full potential mostly due to a lack of methodological shortcomings and data availability. Future research endeavors may focus on the following suggestions to improve the quality of new applications in this context: first, by using raw data instead of already preprocessed data. Second, by using only relevant data based on empirical evidence. In particular, crafting optimal feature sets rather than using many individual detached features and consultation with in-field professionals. Third, by validating and replicating the existing approaches (ie, applying the model to unseen data). Fourth, depending on the research aim (ie, generalization vs personalization) maximizing the sample size or the duration over which data are collected.

Exploring actigraphy as a digital phenotyping measure: A study on differentiating psychomotor agitation and retardation in depression

INTRODUCTION

Psychomotor activity stands out as a crucial symptom in characterizing behaviors associated with depression. This study aims to explore the potential of actigraphy as a tool for digital phenotyping in characterizing symptoms of psychomotor agitation and retardation, which are clinically challenging dimensions to capture, in patients diagnosed with major depressive episode (MDE) according to DSM-5 criteria.

METHODS

We compared rest-activity circadian rhythm biomarkers measured by the Motion Watch 8 actigraphy between 58 (78.4%) patients with MDE and psychomotor retardation (PMR), and 16 (21.6%) patients with MDE and psychomotor agitation (PMA), according to DSM-5 criteria.

RESULTS

Actigraphy allowed to objectively report PMA through heightened activity over a 24-h period, while PMR manifests as reduced activity during the most active 10 h. Lower rest-activity rhythm (RAR) amplitude in PMR was accompanied by increased irregularities in intra- and inter-day rhythms. Interestingly, actigraphy emerges as an objective tool to measure the characteristics of the active and rest periods, free from the confounding effects of sleep disturbances. Indeed, no differences in sleep disturbances were identified between patients exhibiting psychomotor agitation and those displaying PMR.

CONCLUSION

Digital phenotyping through actigraphy may aid in distinguishing psychomotor retardation and psychomotor agitation allowing for a more precise characterization of the depression phenotype. When integrated with clinical assessment, measurements from actigraphy could offer additional insights into activity rhythms alongside subjective assessments and hold the potential to augment existing clinical decision-making processes in psychiatry.

The Association Between Cognitive Functioning and Depression Severity: A Multiwave Longitudinal Remote Assessment Study

Cognitive difficulties are prevalent in depression and are linked to various negative life outcomes such as psychosocial impairment, absenteeism, lower chance of recovery or remission, and overall poor quality of life. Thus, assessing cognitive functioning over time is key to expanding our understanding of depression. Recent methodological advances and the ubiquity of smartphones enable remote assessment of cognitive functioning through smartphone-based tasks and surveys. However, the association of smartphone-based assessments of cognitive functioning to depression severity remains underexplored. Using a dedicated mobile application for assessing cognitive functioning (THINC-it), we investigate within- and between-person associations between performance-based (attention, working memory, processing speed, attention switching) and self-report measures of cognitive functioning with depression severity in 475 participants from the RADAR-MDD (Remote Assessment of Disease and Relapse-Major Depressive Disorder) cohort study (t = 2036 observations over an average of 14 months of follow-up). At the between-person level, we found stronger negative associations between the self-reported cognitive functioning measure and depression severity (β = -0.649, p  < 0.001) than between the performance-based measures and depression severity (βs = -0.220 to -0.349, p s  < 0.001). At the within-person level, we found negative associations between depression severity and the self-reported measure (β = -0.223, p  < 0.001), processing speed (β = -0.026, p=0.032) and attention (β = -0.037, p=0.003). These findings suggest that although THINC-it could adequately and remotely detect poorer cognitive performance in people with higher depressive symptoms, it was not capable of tracking within-person change over time. Nonetheless, repeatedly measuring self-reports of cognitive functioning showed more potential in tracking within-person changes in depression severity, underscoring their relevance for patient monitoring.

The Prognostic Significance of Sleep and Circadian Rhythm for Myocardial Infarction Outcomes: Case-Control Study

BACKGROUND

Myocardial infarction (MI) is a medical emergency resulting from coronary artery occlusion. Patients with acute MI often experience disturbed sleep and circadian rhythm. Most previous studies assessed the premorbid sleep and circadian rhythm of patients with MI and their correlations with cardiovascular disease. However, little is known about post-MI sleep and circadian rhythm and their impacts on prognosis. The use of actigraphy with different algorithms to evaluate sleep and circadian rhythm after acute MI has the potential for predicting outcomes and preventing future disease progression.

OBJECTIVE

We aimed to evaluate how sleep patterns and disrupted circadian rhythm affect the prognosis of MI, using actigraphy and heart rate variability (HRV). Nonparametric analysis of actigraphy data was performed to examine the circadian rhythm of patients.

METHODS

Patients with MI in the intensive care unit (ICU) were enrolled alongside age- and gender-matched healthy controls. Actigraphy was used to evaluate sleep and circadian rhythm, while HRV was monitored for 24 hours to assess autonomic nerve function. Nonparametric indicators were calculated to quantify the active-rest patterns, including interdaily stability, intradaily variability, the most active 10 consecutive hours (M10), the least active 5 consecutive hours (L5), the relative amplitude, and the actigraphic dichotomy index. Follow-ups were conducted at 3 and 6 months after discharge to evaluate prognosis, including the duration of current admission, the number and duration of readmission and ICU admission, and catheterization. Independent sample t tests and analysis of covariance were used to compare group differences. Pearson correlation tests were used to explore the correlations of the parameters of actigraphy and HRV with prognosis.

RESULTS

The study included 34 patients with MI (mean age 57.65, SD 9.03 years) and 17 age- and gender-matched controls. MI patients had significantly more wake after sleep onset, an increased number of awakenings, and a lower sleep efficiency than controls. Circadian rhythm analysis revealed significantly lower daytime activity in MI patients. Moreover, these patients had a lower relative amplitude and dichotomy index and a higher intradaily variability and midpoint of M10, suggesting less sleep and wake activity changes, more fragmentation of the rest-activity patterns, and a more delayed circadian rhythm. Furthermore, significant correlations were found between the parameters of circadian rhythm analysis, including nighttime activity, time of M10 and L5, and daytime and nighttime activitySD, and patient prognosis.

CONCLUSIONS

Patients with acute MI experienced significantly worse sleep and disturbed circadian rhythm compared with healthy controls. Our actigraphy-based analysis revealed a disturbed circadian rhythm, including reduced daytime activities, greater fluctuation in hourly activities, and a weak rest-activity rhythm, which were correlated with prognosis. The evaluation of sleep and circadian rhythm in patients with acute MI can serve as a valuable indicator for prognosis and should be further studied.

Transformer-Driven Affective State Recognition from Wearable Physiological Data in Everyday Contexts

The rapid advancement in wearable physiological measurement technology in recent years has brought affective computing closer to everyday life scenarios. Recognizing affective states in daily contexts holds significant potential for applications in human-computer interaction and psychiatry. Addressing the challenge of long-term, multi-modal physiological data in everyday settings, this study introduces a Transformer-based algorithm for affective state recognition, designed to fully exploit the temporal characteristics of signals and the interrelationships between different modalities. Utilizing the DAPPER dataset, which comprises continuous 5-day wrist-worn recordings of heart rate, skin conductance, and tri-axial acceleration from 88 subjects, our Transformer-based model achieved an average binary classification accuracy of 71.5% for self-reported positive or negative affective state sampled at random moments during daily data collection, and 60.29% and 61.55% for the five-class classification based on valence and arousal scores. The results of this study demonstrate the feasibility of applying affective state recognition based on wearable multi-modal physiological signals in everyday contexts.

Depression Recognition Using Daily Wearable-Derived Physiological Data

The objective identification of depression using physiological data has emerged as a significant research focus within the field of psychiatry. The advancement of wearable physiological measurement devices has opened new avenues for the identification of individuals with depression in everyday-life contexts. Compared to other objective measurement methods, wearables offer the potential for continuous, unobtrusive monitoring, which can capture subtle physiological changes indicative of depressive states. The present study leverages multimodal wristband devices to collect data from fifty-eight participants clinically diagnosed with depression during their normal daytime activities over six hours. Data collected include pulse wave, skin conductance, and triaxial acceleration. For comparison, we also utilized data from fifty-eight matched healthy controls from a publicly available dataset, collected using the same devices over equivalent durations. Our aim was to identify depressive individuals through the analysis of multimodal physiological measurements derived from wearable devices in daily life scenarios. We extracted static features such as the mean, variance, skewness, and kurtosis of physiological indicators like heart rate, skin conductance, and acceleration, as well as autoregressive coefficients of these signals reflecting the temporal dynamics. Utilizing a Random Forest algorithm, we distinguished depressive and non-depressive individuals with varying classification accuracies on data aggregated over 6 h, 2 h, 30 min, and 5 min segments, as 90.0%, 84.7%, 80.1%, and 76.0%, respectively. Our results demonstrate the feasibility of using daily wearable-derived physiological data for depression recognition. The achieved classification accuracies suggest that this approach could be integrated into clinical settings for the early detection and monitoring of depressive symptoms. Future work will explore the potential of these methods for personalized interventions and real-time monitoring, offering a promising avenue for enhancing mental health care through the integration of wearable technology.

Wearable bioelectronics based on emerging nanomaterials for telehealth applications

Nanomaterial-driven, soft wearable bioelectronics are transforming telemedicine by offering skin comfort, biocompatibility, and the capability for continuous remote monitoring of physiological signals. The devices, enabled by advanced zero-dimensional (0D), one-dimensional (1D), and two-dimensional (2D) nanomaterials, have achieved new levels in electrical stability and reliability, allowing them to perform effectively even under dynamic physical conditions. Despite their promise, significant challenges remain in the fabrication, integration, and practical deployment of nanoscale materials and devices. Critical challenges include ensuring the durability and stability of nanomaterial-based bioelectronics for extended wear and developing efficient integration strategies to support multifunctional sensing modalities. Telemedicine has revolutionized healthcare by enabling remote health monitoring. The integration of nanomaterials within wearable devices is a central factor driving this breakthrough, as these materials enhance sensor sensitivity, durability, and multifunctionality. These wearable sensors leverage various operating principles tailored to specific applications, such as intraocular pressure monitoring, electrophysiological signal recording, and biochemical marker tracking.

Digital Health for Asthma Management: Electronic Medication Monitoring for Adherence as a Case Example

Digital health is an umbrella term for components of health care using computer platforms, software, connectivity, and sensors to augment the recording, documentation, and communication of clinical information. The functions of digital health may be viewed in three domains: (1) the repository for patient information, (2) monitoring devices, and (3) communication tools. Monitoring devices have provided robust information as diagnostic and prognostic tools in office and hospital settings. In this review, as a case study, we will discuss the research and our direct clinical experience of electronic medication monitoring technology and the potential benefits to patient care, and the opportunities and perils encountered in using this approach for patients with moderate to severe asthma, including issues related to patient uptake and concerns for bias, impacts on the provider-patient relationship, and discussions regarding monitoring of rescue medication use in exacerbations. Additionally, although there is evidence for improvements in various aspects of patient care afforded by electronic medication monitoring, these devices have not yet seen widespread uptake in clinical settings, and we will discuss the steps needed to address these barriers and keep these important devices available for patient use in the future.

Exercise and depression symptoms in chronic kidney disease patients: an updated systematic review and meta-analysis

OBJECTIVES

To investigate whether exercise intervention is associated with reducing depressive symptoms in chronic kidney disease (CKD) patients.

METHODS

Medline (PubMed), Web of Science, Embase, and the Cochrane Central Register of Controlled Trials (CENTRAL) from inception to February 28, 2024. Randomized controlled trials comparing exercise intervention with usual care or stretching sessions for depression symptoms. Independent data extraction was conducted, and the quality of studies was assessed. A meta-analysis was carried out by using random effects models to calculate standardized mean difference (SMD) with a 95% confidence interval (95% CI) between groups.

RESULTS

23 trials with 1561 CKD patients were identified. Exercise interventions are associated with a significant reduction in depression symptoms among CKD patients, with a moderate average SMD of -0.726 (95% CI: -1.056, -0.396; t=-4.57; p < 0.001). Significant heterogeneity was observed (tau2 = 0.408 [95%CI: 0.227, 1.179], I2 = 79.9% [95% CI: 70.5%, 86.3%]). The funnel plot shows potential publication bias. Subgroup analyses showed that the beneficial effects of exercise on depression remained constant across all subgroups. The evidence is deemed as 'very low' certainty.

CONCLUSIONS

Our systematic review and meta-analysis showed that exercise intervention was associated with significantly alleviating depression symptoms (certainty of evidence: very low). While the very low certainty of the evidence highlights a need for further research.

PROSPERO REGISTRATION NUMBER

CRD42021248450.

Accurately predicting mood episodes in mood disorder patients using wearable sleep and circadian rhythm features

Wearable devices enable passive collection of sleep, heart rate, and step-count data, offering potential for mood episode prediction in mood disorder patients. However, current models often require various data types, limiting real-world application. Here, we develop models that predict future episodes using only sleep-wake data, easily gathered through smartphones and wearables when trained on an individual's sleep-wake history and past mood episodes. Using mathematical modeling to longitudinal data from 168 patients (587 days average clinical follow-up, 267 days wearable data), we derived 36 sleep and circadian rhythm features. These features enabled accurate next-day predictions for depressive, manic, and hypomanic episodes (AUCs: 0.80, 0.98, 0.95). Notably, daily circadian phase shifts were the most significant predictors: delays linked to depressive episodes, advances to manic episodes. This prospective observational cohort study (ClinicalTrials.gov: NCT03088657, 2017-3-23) shows sleep-wake data, combined with prior mood episode history, can effectively predict mood episodes, enhancing mood disorder management.

Autonomic assessment at the intersection of psychosocial and gastrointestinal health

BACKGROUND

Wearable technology is increasingly used in clinical practice and research to monitor functional gastrointestinal symptoms and mental health.

AIMS

This article explores the potential of wearable sensors to enhance the understanding of the autonomic nervous system (ANS), particularly its role in linking psychological and gastrointestinal function. The ANS, facilitates brain-gut communication and is responsive to psychosocial conditions. It is implicated in disorders related to psychological stress and gut-brain interaction. Wearable technology enables tracking of the ANS in daily life, offering complementary and alternative methods from traditional lab-based measures. This review places focus on autonomic metrics such as respiratory sinus arrhythmia, vagal efficiency, and electrodermal activity as well as self-reports of autonomic symptoms.

DISCUSSION

Potential applications include use of wearable sensors for tracking autonomic activity in disorder of gut-brain interaction such as cyclic vomiting syndrome, in which ANS dysregulation may be triggered by psychosocial factors. Considerations for data interpretation and contextualization are addressed, acknowledging challenges such as situational confounders of ANS activity and accuracy of wearable devices.

Beyond Step Count: Are We Ready to Use Digital Phenotyping to Make Actionable Individual Predictions in Psychiatry?

Some models for mental disorders or behaviors (eg, suicide) have been successfully developed, allowing predictions at the population level. However, current demographic and clinical variables are neither sensitive nor specific enough for making individual actionable clinical predictions. A major hope of the "Decade of the Brain" was that biological measures (biomarkers) would solve these issues and lead to precision psychiatry. However, as models are based on sociodemographic and clinical data, even when these biomarkers differ significantly between groups of patients and control participants, they are still neither sensitive nor specific enough to be applied to individual patients. Technological advances over the past decade offer a promising approach based on new measures that may be essential for understanding mental disorders and predicting their trajectories. Several new tools allow us to continuously monitor objective behavioral measures (eg, hours of sleep) and densely sample subjective measures (eg, mood). The promise of this approach, referred to as digital phenotyping, was recognized almost a decade ago, with its potential impact on psychiatry being compared to the impact of the microscope on biological sciences. However, despite the intuitive belief that collecting densely sampled data (big data) improves clinical outcomes, recent clinical trials have not shown that incorporating digital phenotyping improves clinical outcomes. This viewpoint provides a stepwise development and implementation approach, similar to the one that has been successful in the prediction and prevention of cardiovascular disease, to achieve clinically actionable predictions in psychiatry.

Digital biomarkers for non-motor symptoms in Parkinson's disease: the state of the art

Digital biomarkers that remotely monitor symptoms have the potential to revolutionize outcome assessments in future disease-modifying trials in Parkinson's disease (PD), by allowing objective and recurrent measurement of symptoms and signs collected in the participant's own living environment. This biomarker field is developing rapidly for assessing the motor features of PD, but the non-motor domain lags behind. Here, we systematically review and assess digital biomarkers under development for measuring non-motor symptoms of PD. We also consider relevant developments outside the PD field. We focus on technological readiness level and evaluate whether the identified digital non-motor biomarkers have potential for measuring disease progression, covering the spectrum from prodromal to advanced disease stages. Furthermore, we provide perspectives for future deployment of these biomarkers in trials. We found that various wearables show high promise for measuring autonomic function, constipation and sleep characteristics, including REM sleep behavior disorder. Biomarkers for neuropsychiatric symptoms are less well-developed, but show increasing accuracy in non-PD populations. Most biomarkers have not been validated for specific use in PD, and their sensitivity to capture disease progression remains untested for prodromal PD where the need for digital progression biomarkers is greatest. External validation in real-world environments and large longitudinal cohorts remains necessary for integrating non-motor biomarkers into research, and ultimately also into daily clinical practice.

Putting the usability of wearable technology in forensic psychiatry to the test: a randomized crossover trial

Introduction

Forensic psychiatric patients receive treatment to address their violent and aggressive behavior with the aim of facilitating their safe reintegration into society. On average, these treatments are effective, but the magnitude of effect sizes tends to be small, even when considering more recent advancements in digital mental health innovations. Recent research indicates that wearable technology has positive effects on the physical and mental health of the general population, and may thus also be of use in forensic psychiatry, both for patients and staff members. Several applications and use cases of wearable technology hold promise, particularly for patients with mild intellectual disability or borderline intellectual functioning, as these devices are thought to be user-friendly and provide continuous daily feedback.

Method

In the current randomized crossover trial, we addressed several limitations from previous research and compared the (continuous) usability and acceptance of four selected wearable devices. Each device was worn for one week by staff members and patients, amounting to a total of four weeks. Two of the devices were general purpose fitness trackers, while the other two devices used custom made applications designed for bio-cueing and for providing insights into physiological reactivity to daily stressors and events.

Results

Our findings indicated significant differences in usability, acceptance and continuous use between devices. The highest usability scores were obtained for the two fitness trackers (Fitbit and Garmin) compared to the two devices employing custom made applications (Sense-IT and E4 dashboard). The results showed similar outcomes for patients and staff members.

Discussion

None of the devices obtained usability scores that would justify recommendation for future use considering international standards; a finding that raises concerns about the adaptation and uptake of wearable technology in the context of forensic psychiatry. We suggest that improvements in gamification and motivational aspects of wearable technology might be helpful to tackle several challenges related to wearable technology.

The Future of Telemedicine for Obstructive Sleep Apnea Treatment: A Narrative Review

Obstructive sleep apnea is a common type of sleep-disordered breathing associated with multiple comorbidities. Nearly a billion people are estimated to have obstructive sleep apnea, which carries a substantial economic burden, but under-diagnosis is still a problem. Continuous positive airway pressure (CPAP) is the first-line treatment for OSAS. Telemedicine-based interventions (TM) have been evaluated to improve access to diagnosis, increase CPAP adherence, and contribute to easing the follow-up process, allowing healthcare facilities to provide patient-centered care. This narrative review summarizes the evidence available regarding the potential future of telemedicine in the management pathway of OSA. The potential of home sleep studies to improve OSA diagnosis and the importance of remote monitoring for tracking treatment adherence and failure and to contribute to developing patient engagement tools will be presented. Further studies are needed to explore the impact of shifting from teleconsultations to collaborative care models where patients are placed at the center of their care.

What does artificial intelligence mean in rheumatology?

Intelligence is the ability of humans to learn from experiences to ascribe conscious weights and unconscious biases to modulate their outputs from given inputs. Transferring this ability to computers is artificial intelligence (AI). The ability of computers to understand data in an intelligent manner is machine learning. When such learning is with images and videos, which involves deeper layers of artificial neural networks, it is described as deep learning. Large language models are the latest development in AI which incorporate self-learning into deep learning through transformers. AI in Rheumatology has immense potential to revolutionize healthcare and research. Machine learning could aid clinical diagnosis and decision-making, and deep learning could extend this to analyze images of radiology or positron emission tomography scans or histopathology images to aid a clinician's diagnosis. Analysis of routinely obtained patient data or continuously collected information from wearables could predict disease flares. Analysis of high-volume genomics, transcriptomics, proteomics, or metabolomics data from patients could help identify novel markers of disease prognosis. AI might identify newer therapeutic targets based on in-silico modelling of omics data. AI could help automate medical administrative work such as inputting information into electronic health records or transcribing clinic notes. AI could help automate patient education and counselling. Beyond the clinic, AI has the potential to aid medical education. The ever-expanding capabilities of AI models bring along with them considerable ethical challenges, particularly related to risks of misuse. Nevertheless, the widespread use of AI in Rheumatology is inevitable and a progress with great potential.