Multimodal and Multidimensional Artificial Intelligence Technology in Obesity

Abstract

Although the prevalence of obesity is increasing worldwide, related treatment remains a complex challenge that requires multidimensional approaches. Recent advancements in artificial intelligence (AI) have led to the development of multimodal methods capable of integrating diverse types of data. These AI approaches utilize both multimodal data integration and multidimensional feature representations, enabling personalized, data-driven strategies for obesity management. AI can support obesity management through applications such as risk prediction, clinical decision support systems, large language models, and digital therapeutics. Several studies have shown that these AI-based weight loss programs can achieve significant weight reduction and behavioral changes. These AI systems can induce behavioral modifications through continuous personalized feedback and improve accessibility for people in underserved areas. However, these AI technologies must address issues such as data privacy and security, transparency and accountability, and consider the potential widening health disparities between individuals who have access to AI technology and those who do not, as well as strategies for sustained user engagement. Conducting long-term clinical trials and evaluations of cost-effectiveness across diverse, large-scale populations would facilitate the effective application of AI in obesity management, ultimately contributing to improvements in public health.

INTRODUCTION

Obesity is a chronic disease marked by abnormal and excessive fat accumulation, adversely affecting health and increasing the risk of comorbidities such as hypertension, diabetes, and cardiovascular disease.^1-3 Over the past 50 years, the global prevalence of obesity has increased,⁴ and as of 2022, more than one billion people are estimated to be living with obesity.⁵ This growing trend significantly burdens public health, underscoring the urgent need for effective obesity management.

In recognition of this need, numerous professional societies have established guidelines that emphasize lifestyle interventions, including diet, physical activity, and behavior therapy, as the primary strategies for obesity management.^6-8 However, real-world implementation of these strategies remains difficult. Many patients fail to recognize obesity as a chronic, relapsing disease, and barriers such as misinformation, financial constraints, and environmental factors hinder effective weight loss. Moreover, physicians often lack both sufficient consultation time and specialized training in obesity treatment and counseling, resulting in suboptimal management.⁹ Furthermore, obesity is a complex disease influenced by various factors, including genetic, environmental, and behavioral components. Although initial weight loss may be achieved, long-term weight reduction remains challenging.¹⁰ For these reasons, effective management of obesity continues to be a significant challenge.

Recent advancements in artificial intelligence (AI) have sparked growing interest in its application to obesity management. As technology continues to evolve, various machine learning models have been developed. More recently, multimodal AI, which utilizes multiple types of data, has been introduced, expanding both the applicability and accuracy of AI. AI has the potential to predict the risk of obesity and obesity-related diseases, as well as to enable personalized interventions. In addition, AI can continuously monitor patients, provide real-time feedback and motivation, and offer significant benefits in terms of cost-effectiveness and accessibility. These advantages highlight AI’s potential to address many of the limitations associated with conventional obesity treatment methods. This review aims to provide a comprehensive overview of the potential applications and effectiveness of multimodal, multidimensional AI in obesity management, while identifying current challenges and discussing future directions.

MULTIMODAL AND MULTIDIMENSIONAL AI

Obesity is a complex and multifactorial disease,¹¹ necessitating a multidimensional approach that considers multiple features within a single data domain (e.g., diet, activity, and psychology). In parallel, AI systems should also incorporate multimodal approaches, integrating various data types such as images, sensor data, and clinical notes. A multidimensional approach entails strategies that address the diverse aspects of obesity, including physical activity, dietary behavior, psychological and environmental factors, pharmacotherapy, surgical interventions, and long-term behavioral support.^10,12 Implementation of such strategies has been shown to effectively promote weight reduction.^10,13,14 Accordingly, AI systems designed for obesity care should reflect this complexity by integrating multidimensional perspectives into their structure and functionality (Fig. 1).

Figure 1.

Overview of multimodal artificial intelligence (AI) integration of diverse data types to enhance prediction, intervention, and outcomes in obesity management. MRI, magnetic resonance imaging; CT, computed tomography; EHR, electronic health record.

In addition to adopting a multidimensional perspective, it is essential to employ analytic methods capable of handling the complexity and heterogeneity of health data relevant to obesity. Traditional machine learning models typically rely on unimodal inputs, from a single source or format of data. This limitation reduces the ability of such models to capture the full scope of an individual’s clinical profile.¹⁵ In contrast, multimodal AI integrates diverse data types (e.g., text, images, and audio), while multidimensional AI processes multiple variables within a single modality. This distinction enhances the analytical capacity to address the multifaceted nature of obesity.^16,17

Multimodal AI can incorporate a diverse range of data modalities, including medical images, temporal data, video, audio, text, omics data, and formats that support any-to-any modality translation. Medical images include X-ray, computed tomography scan, magnetic resonance imaging (MRI), and ultrasound. Temporal data encompass electronic health records and outputs from wearable devices. Video and audio data can capture patient movement and breath sounds, respectively. Text data may consist of clinical notes and scientific literature. Omics data include genomic, epigenomic, transcriptomic, and proteomic information. Recent advances in AI architecture now enable flexible combinations of inputs and outputs across these modalities.^16,18-20 Efficient integration and analysis of such heterogeneous data sources are essential for capturing the full clinical and biological complexity of obesity, ultimately enhancing diagnostic accuracy, risk prediction, and the development of personalized treatment strategies.

The application of multimodal AI in obesity management has shown considerable promise. For example, data from wearable sensors can be used to monitor physical activity in real time, facilitating behavioral interventions aimed at improving health outcomes.²¹ In addition, multi-omics profiling enables the stratification of individuals based on genetic and metabolic characteristics, supporting the development of personalized treatment approaches.²² Medical imaging, including MRI, can be used to assess body composition²³ and quantify abdominal subcutaneous fat.²⁴ By integrating these diverse data sources, multimodal AI can significantly enhance the precision and effectiveness of obesity interventions. Compared to conventional unimodal approaches, multimodal models have shown superior performance in 91% of cases,¹⁵ with improvements ranging from 6% to 33% over single-source models.^25,26 Leveraging these capabilities allows researchers and clinicians to more effectively address the multifaceted nature of obesity, ultimately contributing to improved patient outcomes.

APPLICATIONS OF AI IN OBESITY MANAGEMENT

AI is increasingly utilized across various healthcare fields, with continuous advancements in its integration into obesity management. It can be utilized for predictive modeling, clinical decision support systems (CDSSs), chatbots powered by large language models (LLMs), and digital therapeutics (DTx) for obesity management (Table 1).

Table 1.

Applications of AI in obesity management

AI application	Description	Strengths	Limitations	Examples
Risk prediction	Uses demographic and biomarker data to assess obesity risk or predict treatment response	Enables early, personalized interventions; informs clinical decisions	Requires high-quality, diverse training datasets; potential lack of generalizability	Obesity prediction28; Obesity-related comorbidity prediction29,30; Bariatric surgery response models31,32; Pharmacotherapy prediction33; Weight loss predictors34,35
CDSS	Provides clinical recommendations based on patient data using rules or AI algorithms	Supports real-time decisions; integrates multi-omics and clinical data	Black box issue in non-knowledge-based systems; unclear legal accountability	Fat removal estimation38; Postoperative complication prediction42; XAI for insulin resistance diagnosis43; XAI for obesity-related comorbidity prediction29
LLMs	Text-based AI models that generate human-like responses for interactive coaching	Delivers continuous, empathetic support; highly accessible via chatbots	Risk of misinformation; limited ability to interpret non-textual input	ChatDiet45; ChatGPT meal planner46; CHARLIE47; GPT-4 exercise planner48; Paola49; SlimME50
DTx	Evidence-based software used for therapeutic purposes, often via mobile or web	Scalable and cost-effective; ideal for long-term behavioral interventions	Dependent on user engagement and digital literacy; regulatory issues remain	Omada health56; Liva healthcare57,58; Multimodal app interventions59

AI, artificial intelligence; CDSS, clinical decision support system; XAI, explainable artificial intelligence; LLM, large language model; DTx, digital therapeutics.

Prediction model

Accurate disease prediction and outcome forecasting are essential for effective prevention and treatment. AI can be used to predict obesity using data such as demographic information and biomarkers.²⁷ Additionally, it can assess the risk of obesity-related comorbidities, enabling early intervention and preventive strategies.^28,29 Furthermore, AI can predict individual responses to bariatric surgery^30,31 and pharmacological treatments,³² as well as forecast weight loss outcomes for individuals undergoing weight management interventions.^33-39 These predictive capabilities of AI contribute to clinical decision-making by enabling healthcare professionals to select the most effective treatment strategies for obesity management.

Clinical decision support system

A CDSS is a software tool designed to assist clinicians in decision-making by matching an individual patient’s characteristics with computerized clinical knowledge to provide personalized assessments and recommendations.⁴⁰ Knowledge-based CDSS generates outputs based on predefined rules, whereas non-knowledge-based CDSS produces results through machine learning techniques.^40,41 CDSS can predict clinical prognosis and aid physicians in making informed medical decisions. In the field of bariatric surgery, CDSS can be utilized to estimate the optimal fat removal volume in obese patients³⁷ and predict early postoperative complications associated with obesity procedures.⁴²

However, non-knowledge-based CDSS powered by AI face the ‘black box’ problem, in which the reasoning behind the generated outcomes remains unknown.⁴⁰ CDSS incorporating explainable artificial intelligence (XAI) is being developed to elucidate decision-making mechanisms through techniques such as Shapley additive explanations (SHAP).⁴³ XAI-based CDSS can be employed to predict obesity-related comorbidities and non-communicable diseases, including diabetes, cardiovascular disease, and heart disease.²⁹ Additionally, by integrating multi-omics and clinical data, XAI-based CDSS can facilitate early diagnosis of insulin resistance.⁴³ The implementation of such XAI-based CDSS ensures transparency and reliability, fostering greater trust in AI-assisted clinical decision-making.

Large language model

LLMs are AI models trained on vast amounts of text to generate human-like responses, and they can be applied to various clinical tasks.⁴⁴ In the context of obesity management, where lifestyle interventions play a crucial role, LLMs offer significant advantages. AI-powered chatbots can provide continuous monitoring, encouragement, and appropriate interventions for patients with obesity throughout the day. As a result, numerous AI-powered chatbots have been developed for obesity management. These chatbots can recommend personalized dietary plans and physical activity regimens while also providing motivational support. For instance, ChatDiet (University of California, Irvine) offers personalized and explainable food recommendations,⁴⁵ and ChatGPT (OpenAI) can be used to generate weekly meal plans tailored to individual needs.⁴⁶ Additionally, the chatbot CHARLIE⁴⁷ both provides dietary recommendations and suggests fitness plans customized to the user’s schedule. Furthermore, OpenAI GPT-4 can be utilized to create personalized exercise programs.⁴⁸

Many chatbots also function as virtual coaches, providing users with motivation and support. For example, Paola (IBM Watson), a virtual health coach, monitors dietary intake and physical activity levels while offering lifestyle interventions.⁴⁹ Additionally, SlimME (Taipei Medical University), a chatbot equipped with artificial empathy, can provide users with emotional support and personalized weight management.⁵⁰ By leveraging these diverse chatbot technologies, individuals can benefit from personalized dietary and exercise management while maintaining continuous motivation, ultimately supporting effective weight loss. Initially, early LLMs could only process text data as input, but the ability to integrate various modalities of data allows the potential to provide more effective monitoring and personalized management.⁵¹

Digital therapeutics

DTx are evidence-based interventions that leverage high-quality software programs to prevent, manage, and treat medical conditions.⁵² Various digital platforms, including mobile applications, wearable devices, web-based interventions, and virtual reality, serve as delivery mechanisms for these therapies.^53,54 DTx are primarily employed in the management of chronic diseases such as hypertension and diabetes,⁵⁵ as they function by facilitating behavioral modifications in patients. In addition to chronic disease management, DTx have been applied to obesity treatment, with numerous applications being developed to support this approach. For instance, Omada health⁵⁶ and Liva healthcare,^57,58 which are examples of DTx providing digital lifestyle coaching, can support weight loss by leveraging diverse formats such as text, audio, images, and video.⁵⁹ These flexible delivery formats also contribute to the broad applicability of DTx across various populations. DTx offer high scalability and accessibility and are particularly valuable in low-resource settings and for individuals with limited access to traditional healthcare services.⁶⁰

EXAMPLES OF SUCCESSFUL AI-BASED OBESITY MANAGEMENT

Examples of AI-based weight loss programs

Several studies have shown the effectiveness of AI-driven weight loss interventions. In a study involving 391 participants who received lifestyle interventions through SureMediks (Rasimo Systems LLC), a digital AI-powered lifestyle intervention platform, all participants experienced weight loss over a 24-week period, with an average reduction of 14% of their initial body weight.⁶¹ Furthermore, in a study assessing the effectiveness of the eating trigger-response inhibition program, a 12-week app-based weight management program, 230 participants showed improvements in overeating, snacking behaviors, self-regulation of eating habits, and physical activity levels.⁶² In another study involving 70 overweight and obese participants, use of an AI-driven health coach led to an average weight reduction of 2.38% of initial body weight and a 31% increase in the proportion of healthy meals consumed.⁶³ Similarly, among 294 adults with type 2 diabetes mellitus, use of an integrated digital health management platform with AI-based dietary guidance resulted in improved glycemic control and enhanced weight loss outcomes.⁶⁴ These findings underscore the potential of AI-based lifestyle interventions as effective tools for weight management and overall health improvement.

Implications and future directions

The encouraging results from AI-based weight loss interventions suggest meaningful potential for application beyond research settings. These tools can serve as scalable and cost-effective complements to traditional obesity treatment when incorporated into routine clinical practice.⁶⁵ By delivering real-time, personalized feedback based on individual behavior and progress, AI systems can support long-term lifestyle changes. In particular, LLM-based chatbots and multimodal platforms enable continuous engagement and motivation, which are essential for sustainable weight management.⁶⁶

Moreover, AI-powered interventions can improve accessibility by reaching individuals in remote or underserved areas through mobile apps, wearables, and virtual coaching platforms. This may help reduce health disparities by extending care to populations with limited access to obesity specialists. At the public health level, AI systems could be integrated into nationwide initiatives, school-based programs, or workplace wellness strategies to enhance population-level impact. Moving forward, efforts should focus on improving user experience, ensuring cultural adaptability, and validating effectiveness across diverse groups to support broader implementation and long-term success.

CHALLENGES

Despite the potential benefits of AI-driven interventions in obesity management, several critical challenges must be addressed to ensure their ethical, legal, and practical applicability. First, data privacy and security remain major concerns. AI systems in healthcare require access to sensitive patient information, including medical history and lifestyle data. Security vulnerabilities in servers or cloud platforms may lead to data breaches, potentially exposing confidential patient information.^67,68 Additionally, healthcare institutions and companies may fail to obtain proper patient consent during AI development or may lack clarity in the intended use of collected data.⁶⁹ Addressing these issues requires the establishment of robust security frameworks, careful attention to patient data de-identification, transparency in data usage, and strict adherence to legal and ethical regulations.

Second, transparency and accountability represent critical challenges. Since AI functions as a ‘black box’ with opaque decision-making processes, concerns regarding transparency may arise when these systems are applied in clinical practice. Ensuring transparency is essential for both clinicians and patients to trust AI-driven decisions. While XAI techniques such as SHAP provide partial transparency, they do not fully resolve concerns regarding interpretability.⁷⁰ Furthermore, legal accountability in AI-driven decision-making remains unresolved. This is primarily due to uncertainty over whether responsibility should lie with AI developers, healthcare institutions, or individual clinicians overseeing AI-based interventions.⁶⁸ Clarifying this ambiguity in liability is essential to ensure the responsible and ethical deployment of AI in clinical settings.

Third, AI has the potential to exacerbate health disparities. The health gap may widen between individuals with greater access to AI technologies and those with limited access.⁷¹ Additionally, AI models may exhibit inherent biases if algorithms learn from datasets that disproportionately represent certain demographics, potentially leading to misdiagnosis in marginalized populations.^72,73 Ensuring fairness and inclusivity in AI development requires rigorous validation, continuous monitoring for bias, and adherence to ethical guidelines that prioritize patient safety and equitable care.

Fourth is the accessibility issue. AI-based medical solutions must be designed to ensure equitable access, including the elderly and individuals lacking technological literacy. Older adults and individuals with limited digital literacy often experience difficulty navigating AI-based health tools, which may reduce intervention efficacy in these groups.^74,75 To promote widespread accessibility, comprehensive user education and the development of intuitive, user-friendly interfaces tailored to diverse populations are essential.

Fifth, the effectiveness of AI-based lifestyle interventions heavily depends on participant adherence.^76-78 Lifestyle modifications rely significantly on individual motivation, particularly in the absence of direct supervision. Variability in adherence may lead to significant differences in intervention effectiveness, with engaged users receiving greater benefit than disengaged ones. Therefore, successful implementation requires carefully calibrated notification systems and strategies to enhance engagement and compliance.

Sixth, development of AI-driven obesity management interventions remains in its early stages, with a lack of large-scale clinical trials validating their long-term efficacy. Evidence regarding optimal AI intervention levels, the most effective intervention strategies, and the sustainability of AI-based weight management programs is limited. Further research is needed to determine best practices for AI integration in obesity treatment, including the extent to which AI-based guidance should be personalized and how engagement strategies can enhance adherence to AI-driven recommendations. Moreover, the effectiveness of such AI-based interventions must be validated across diverse populations. Economic evaluations of these AI-driven approaches are also necessary to enable practical implementation. Without well-designed, long-term studies across diverse populations and assessments of real-world applicability, the widespread implementation of these interventions remains uncertain.

Addressing these limitations is essential for effective development and practical implementation of AI-based obesity management. AI has the potential to reduce healthcare costs, improve access to weight management resources, and support healthcare professionals by providing predictive insights and data-driven recommendations. Successful integration of AI into clinical practice will require continuous attention to security, ethical integrity, equitable access, and sustained user engagement.

CONCLUSION

While the global prevalence of obesity has been increasing over the past 50 years, effective treatment remains a challenge. Multimodal and multidimensional AI approaches present promising new strategies for obesity management by enabling personalized, data-driven interventions. AI-based tools such as prediction models, CDSSs, LLMs, and DTx can support various aspects of obesity care. Moreover, recent studies suggest that AI-driven approaches may lead to substantial weight loss and meaningful behavioral changes. Nevertheless, these AI technologies must address concerns regarding data privacy, security, explainability, and legal challenges. Furthermore, factors such as equitable access and sustained user engagement should be carefully considered to ensure successful implementation of AI-based interventions. If these challenges are effectively addressed, technologies are further refined, and large-scale, long-term clinical trials along with evaluations of cost-effectiveness are conducted across diverse populations, AI may play a pivotal role in obesity treatment and public health strategies.