e.g. mhealth
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Authors’ Reply: Foundation Models for Generative AI in Time-Series Forecasting
In our work, we intended to refer to FMs as models that have been trained on extremely large and typically unlabeled datasets, encompassing both models that are inherently capable of generative tasks and models that can be adapted for generative forecasting tasks (eg, clinical language models).
J Med Internet Res 2025;27:e79772
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Foundation Models for Generative AI in Time-Series Forecasting
The first list features models for time-series forecasting, while the second includes large language models trained on electronic health records (referred to as clinical language models [CLa Ms]) that take text as input and may produce text as output.
J Med Internet Res 2025;27:e76964
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For instance, Google Trends (GT) data, which measure the relative volume of searches on a specific topic or term, have shown promising results as a complementary tool to classical surveillance methods [6], in forecasting influenza spread and hospitalizations [14-16], for modelling COVID-19 spread [17,18], and for forecasting asthma admissions [19].
J Med Internet Res 2025;27:e51804
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Generative AI Models in Time-Varying Biomedical Data: Scoping Review
In this section, we introduce a set of traditional, ML, DL, and Gen AI models that are referenced later in the paper and list existing models used in time-series forecasting in Figure 3, as well as showing a timeline for when these models were first introduced in Figure 4 and a comparison among these methods using common metrics in Table 1.
Existing models for time-series forecasting.
J Med Internet Res 2025;27:e59792
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Reference 23: Forecasting influenza activity using self-adaptive AI model and multi-source data in Chongqing Reference 26: An experimental review on deep learning architectures for time series forecasting Reference 39: Forecasting seasonal influenza-like illness in South Korea after 2 and 30 weeks using GoogleforecastingForecasting the Incidence of Mumps Based on the Baidu Index and Environmental Data in Yunnan, China:
J Med Internet Res 2025;27:e66072
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Others have also found that forecasting performance metrics varied between early and declining mpox outbreak phases [32]. This underscores the need for nowcasting methods that will reliably perform well as epidemics grow, peak, and decline.
Stratifying by race or ethnicity improved performance, and the highest average scores were observed for White patients. Performance at shorter windows was lowest for hindcasts of Hispanic or Latino patients, possibly due to a lower interview success rate.
Online J Public Health Inform 2025;17:e56495
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As widely known, effective health care workforce planning drives the establishment of resilient and sustainable health care systems [16], with workforce demand forecasting playing a crucial role in health care workforce planning [17].
Asian Pac Isl Nurs J 2024;8:e59484
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A Roadmap for Using Causal Inference and Machine Learning to Personalize Asthma Medication Selection
Forecasting which patient will respond well to which ICS is the first step toward creating this tool, but no prior study has predicted ICS response, forming a gap.
JMIR Med Inform 2024;12:e56572
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Recent literature has also shown its potential use for forecasting COVID-19. Various univariate models have been used to predict the number of cases, deaths, and hospitalizations due to COVID-19 [10-14]. However, one of the criticisms of this approach is its inability to capture the interdependency of these parameters and hence, multivariate time series methods have been used to fill this gap [15,16].
JMIR Form Res 2023;7:e46357
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Forecasting Artificial Intelligence Trends in Health Care: Systematic International Patent Analysis
Interestingly, most of the single words with a high relative increase are linked to modern technologies within health care (“device,” “forecasting,” “inference,” and “classifying”). Similarly, some of the increasingly used word pairs are “computer aided” and “learning algorithms.”
By using the patent database filter option “Applicant toplist,” a list in descending order of the number of patent applications per applicant was generated.
JMIR AI 2023;2:e47283
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