REFERENCES
1. Cheng, Y.; Zhang, C.; Zhang, Z.; et al. Exploring large language model-based intelligent agents: definitions, methods, and prospects. arXiv 2024, arXiv:2401.03428. Available online: https://doi.org/10.48550/arXiv.2401.03428 (accessed 11 August 2026).
2. Wang, L.; Ma, C.; Feng, X.; et al. A survey on large language model based autonomous agents. Front. Comput. Sci. 2024, 18, 186345.
4. Yao, S.; Zhao, J.; Yu, D.; et al. ReAct: Synergizing reasoning and acting in language models. In International Conference on Learning Representations. 2023. https://openreview.net/forum?id=WE_vluYUL-X. (accessed 2026-08-11).
5. Shinn, N.; Cassano, F.; Gopinath, A.; Narasimhan, K.; Yao, S. Reflexion: language agents with verbal reinforcement learning. In Advances in Neural Information Processing Systems. 2023, 36, 8634-52.
6. Wu, D.; Wang, H.; Yu, W.; Zhang, Y.; Chang, K. W.; Yu, D. LongMemEval: Benchmarking chat assistants on long-term interactive memory. In International Conference on Learning Representations. 2025. https://proceedings.iclr.cc/paper_files/paper/2025/file/d813d324dbf0598bbdc9c8e79740ed01-Paper-Conference.pdf. (accessed 2026-08-11).
7. Tan, H.; Zhang, Z.; Ma, C.; Chen, X.; Dai, Q.; Dong, Z. MemBench: towards more comprehensive evaluation on the memory of LLM-based agents. In Findings of the Association for Computational Linguistics: ACL 2025, Vienna, Austria, June, 2025; Association for Computational Linguistics: Stroudsburg, PA, USA, 2025; pp 19336-52.
8. Wang, Z. Z.; Mao, J.; Fried, D.; Neubig, G. Agent workflow memory. In Proceedings of the 42nd International Conference on Machine Learning. 2025, 267, 63897-911. https://proceedings.mlr.press/v267/wang25bx.html. (accessed 2026-08-11).
9. Liu, F.; Niu, Y.; Zhang, Q.; et al. A foundational architecture for AI agents in healthcare. Cell. Rep. Med. 2025, 6, 102374.
10. Ferber, D.; Hilgers, L.; Höper, C.; et al. Towards autonomous medical artificial intelligence agents. Nature 2026, 655, 1282-91.
11. Liévin, V.; Palepu, A.; Weng, W. H.; et al. Towards conversational artificial intelligence for disease management. Nature 2026, 655, 1292-9.
12. Gorenshtein, A.; Omar, M.; Glicksberg, B. S.; Nadkarni, G. N.; Klang, E. AI agents in clinical medicine: a systematic review. medRxiv. 2025, Epub ahead of print.
13. Massara, P.; Kirkland, J.; Pagani, I.; et al. Applying artificial intelligence and machine learning in precision nutrition. Nat. Commun. 2026, 17, 5880.
14. Karunanayake, N. Next-generation agentic AI for transforming healthcare. Informatics and Health. 2025, 2, 73-83.
15. Zeevi, D.; Korem, T.; Zmora, N.; et al. Personalized nutrition by prediction of glycemic responses. Cell 2015, 163, 1079-94.
16. Chiu, Y. H.; Wen, L. What are we estimating? Revisiting standard nutritional models through the Target Trial Framework. Am. J. Epidemiol. 2026, kwag053.
17. Zheng, G.; Chen, L.; Ran, S.; et al. A hypothetical intervention analysis for the effects of healthy dietary patterns on reducing major chronic diseases and mortality associated with air pollutant mixtures. BMC. Med. 2025, 23, 655.
18. Berry, S. E.; Valdes, A. M.; Drew, D. A.; et al. Human postprandial responses to food and potential for precision nutrition. Nat. Med. 2020, 26, 964-73.
19. Brügger, V.; Kowatsch, T.; Jovanova, M. Predicting postprandial glucose excursions to personalize dietary interventions for type-2 diabetes management. Sci. Rep. 2025, 15, 25920.
20. San, O.; Rasheed, A.; Bozdemir, E.; Deng, J. The evolution of digital twins from reactive to agentic systems. Nat. Comput. Sci. 2026, 6, 6-10.
21. Mosquera-Lopez, C.; Jacobs, P. G. Digital twins and artificial intelligence in metabolic disease research. Trends. Endocrinol. Metab. 2024, 35, 549-57.
22. Lutsker, G.; Sapir, G.; Shilo, S.; et al. A foundation model for continuous glucose monitoring data. Nature 2026, 650, 978-86.
23. Feuerriegel, S.; Frauen, D.; Melnychuk, V.; et al. Causal machine learning for predicting treatment outcomes. Nat. Med. 2024, 30, 958-68.
24. Prosperi, M.; Guo, Y.; Sperrin, M.; et al. Causal inference and counterfactual prediction in machine learning for actionable healthcare. Nat. Mach. Intell. 2020, 2, 369-75.
25. Tennant, P. W. G.; Murray, E. J.; Arnold, K. F.; et al. Use of directed acyclic graphs (DAGs) to identify confounders in applied health research: review and recommendations. Int. J. Epidemiol. 2021, 50, 620-32.
26. Nguyen, T.; Collins, G. S.; Landais, P.; Le Manach, Y. Counterfactual clinical prediction models could help to infer individualized treatment effects in randomized controlled trials - an illustration with the International Stroke Trial. J. Clin. Epidemiol. 2020, 125, 47-56.
27. Feeney, T.; Hartwig, F. P.; Davies, N. M. How to use directed acyclic graphs: guide for clinical researchers. BMJ 2025, 388, e078226.
28. Naumova, E. N. Causal AI for public health research and policy: a journey back to the future. J. Public. Health. Policy. 2025, 46, 1-7.
29. Wedlund, L.; Kvedar, J. Simulated trials: in silico approach adds depth and nuance to the RCT gold-standard. NPJ. Digit. Med. 2021, 4, 121.
30. Pathmanathan, P.; Aycock, K.; Badal, A.; et al. Credibility assessment of in silico clinical trials for medical devices. PLoS. Comput. Biol. 2024, 20, e1012289.
31. Wang, Z.; Gao, C.; Glass, L. M.; Sun, J. Artificial intelligence for in silico clinical trials: a review. arXiv 2022, arXiv:2209.09023. Available online: https://doi.org/10.48550/arXiv.2209.09023 (accessed 11 August 2026).
32. Mohsen, F.; Al-Absi, H. R. H.; Yousri, N. A.; El Hajj, N.; Shah, Z. A scoping review of artificial intelligence-based methods for diabetes risk prediction. NPJ. Digit. Med. 2023, 6, 197.
33. Rao, S.; Li, Y.; Mamouei, M.; et al. Refined selection of individuals for preventive cardiovascular disease treatment with a transformer-based risk model. Lancet. Digit. Health. 2025, 7, 100873.
34. Vasudevan, S.; Saha, A.; Tarver, M. E.; Patel, B. Digital biomarkers: convergence of digital health technologies and biomarkers. NPJ. Digit. Med. 2022, 5, 36.
35. Coravos, A.; Khozin, S.; Mandl, K. D. Developing and adopting safe and effective digital biomarkers to improve patient outcomes. NPJ. Digit. Med. 2019, 2, 14.
36. Lieberwirth, J. K.; Mittermaier, M.; Stern, A. D. Challenges and potential of using digital biomarkers in healthcare and clinical trials. Commun. Med. 2026, 6, 151.
37. Aboagye, N. Y.; Hinchliffe, C.; Del Din, S.; Ng, W. F.; Baker, K. F.; Baker, M. R. Systematic review: digital biomarkers of fatigue in chronic diseases. NPJ. Digit. Med. 2025, 8, 602.
38. Gardano, M.; Nocera, A.; Raimondi, M.; Senigagliesi, L.; Gambi, E. A narrative review on key values indicators of millimeter wave radars for ambient assisted living. Electronics 2025, 14, 2664.
39. Gong, E.; Baptista, S.; Russell, A.; et al. My diabetes coach, a mobile App-based interactive conversational agent to support type 2 diabetes self-management: randomized effectiveness-implementation trial. J. Med. Internet. Res. 2020, 22, e20322.
40. Schneider, D.; Liedtke, W.; Klausen, A. D.; et al. Indecision on the use of artificial intelligence in healthcare - a qualitative study of patient perspectives on trust, responsibility and self-determination using AI-CDSS. Digit. Health. 2025, 11, 20552076251339522.
41. Hietbrink, E. A. G.; Middelweerd, A.; d’Hollosy, W.; Schrijver, L. K.; Laverman, G. D.; Vollenbroek-Hutten, M. M. R. Exploring the acceptance of just-in-time adaptive lifestyle support for people with type 2 diabetes: qualitative acceptability study. JMIR. Form. Res. 2025, 9, e65026.
42. Magnini, M.; Ciatto, G.; Cantürk, F.; Aydoğan, R.; Omicini, A. Symbolic knowledge extraction for explainable nutritional recommenders. Comput. Methods. Programs. Biomed. 2023, 235, 107536.
43. Onay, T.; Bekar, D.; Çoban, E.; Doğan, N.; Günşen, U. Artificial intelligence in clinical nutrition: a descriptive comparison of ChatGPT‐ and dietitian‐planned diets for chronic disease scenarios. J. Hum. Nutr. Diet. 2025, 38, e70135.
44. Aydin Cil, M.; Karatas, N.; Mustafaoglu, O.; Sevinc, C. Comparison of AI-generated renal diets by different large language models: a guideline-based evaluation with expert input. BMC. Nephrol. 2026, 27, 133.
45. Willett, W.; Rockström, J.; Loken, B.; et al. Food in the Anthropocene: the EAT-Lancet Commission on healthy diets from sustainable food systems. Lancet 2019, 393, 447-92.
46. van Dooren, C. A review of the use of linear programming to optimize diets, nutritiously, economically and environmentally. Front. Nutr. 2018, 5, 48.
47. Gebara, C. H.; Berthet, E.; Vandenabeele, M. I. D.; Jolliet, O.; Laurent, A. Diets can be consistent with planetary limits and health targets at the individual level. Nat. Food. 2025, 6, 466-77.
48. Zhu, R.; Wang, L.; Chen, H.; et al. The health and environmental impacts, safety, and affordability of the eat-lancet planetary health diet: a multidisciplinary systematic review and meta-analysis. Adv. Nutr. 2026, 17, 100666.
49. Shiratori, S.; Abeysekara, M. D. Relevance of mathematical optimization as a tool for diet modeling in the development of food-based dietary recommendations in sub-saharan africa: a scoping review. Adv. Nutr. 2025, 16, 100480.
50. Bashiri, B.; Kaleda, A.; Vilu, R. Sustainable diets, from design to implementation by multi-objective optimization-based methods and policy instruments. Front. Sustain. Food. Syst. 2025, 9, 1629739.
51. Hu, Y.; Zhao, F.; Zhang, M.; et al. Multi-objective optimization of national dietary guidelines: balancing nutrition, environment, and economy. Front. Nutr. 2026, 13, 1758724.
52. Wang, B.; Zheng, Y.; Han, X.; et al. A systematic literature review on integrating AI-powered smart glasses into digital health management for proactive healthcare solutions. NPJ. Digit. Med. 2025, 8, 410.
53. Guasch-Ferré, M.; Wittenbecher, C.; Palmnäs, M.; et al. Precision nutrition for cardiometabolic diseases. Nat. Med. 2025, 31, 1444-53.
54. Wu, X.; Oniani, D.; Shao, Z.; et al. A scoping review of artificial intelligence for precision nutrition. Adv. Nutr. 2025, 16, 100398.
55. Mehta, S.; Huey, S. L.; Fahim, S. M.; et al. Advances in artificial intelligence and precision nutrition approaches to improve maternal and child health in low resource settings. Nat. Commun. 2025, 16, 7673.
56. Chen, J.; Ren, Y.; Zhou, Y.; et al. A generative AI framework unifies human multi-omics to model aging, metabolic health, and intervention response. Cell. Metab. 2026, 38, 1229-44.e6.
57. Liu, H.; Shi, K.; Li, A.; et al. An AI agent for automated causal inference in epidemiology. medRxiv. 2026, Epub ahead of print.
58. Collins, G. S.; Moons, K. G. M.; Dhiman, P.; et al. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ 2024, 385, e078378.
59. Gallifant, J.; Afshar, M.; Ameen, S.; et al. The TRIPOD-LLM reporting guideline for studies using large language models. Nat. Med. 2025, 31, 60-9.
