Health effects of intermittent fasting and the role of artificial intelligence technologies in optimizing its clinical translation

Chenghao Zhang; Lijun Chang; Yanqiu Huang; Yadan Xu; Wen Gu; Yang Yang; Hui Wang; Chenghao Zhang; Lijun Chang; Yanqiu Huang; Yadan Xu; Wen Gu; Yang Yang; Hui Wang

doi:10.3934/publichealth.2025061

AIMS Public Health

2025, Volume 12, Issue 4: 1190-1222. doi: 10.3934/publichealth.2025061

Previous Article Next Article

Review Special Issues

Health effects of intermittent fasting and the role of artificial intelligence technologies in optimizing its clinical translation

School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
† These two authors contributed equally.

Received: 29 October 2025 Revised: 09 December 2025 Accepted: 12 December 2025 Published: 18 December 2025

As a non-pharmacological approach, Intermittent Fasting (IF) exhibits the capacity to boost health and counteract chronic diseases by regulating the metabolism, strengthening the cellular resistance to stress, and reshaping the immune microenvironment. The rapid progress of Artificial Intelligence (AI) technologies has greatly advanced our comprehension of IF's diverse health benefits. This review outlines AI's role in enhancing the exploration of IF's function in governing systemic health, clarifies the association between IF and health outcomes, and specifies AI's function in analyzing IF's impacts, which cover metabolic processes, cellular stress responses, disease prevention, and the development of personalized dietary strategies. By leveraging AI to integrate various omics datasets, the mechanisms through which IF prevents chronic diseases can be uncovered. This review discusses the challenges that AI faces in researching diet-related health mechanisms and presents an outlook on future developments. AI offers innovative methods to investigate IF's effects on chronic disease prevention, which could lay the foundation for more efficient strategies to support healthier and longer lifespans.
- intermittent fasting,
- artificial intelligence,
- chronic disease prevention,
- nutrition,
- multi-omics
Citation: Chenghao Zhang, Lijun Chang, Yanqiu Huang, Yadan Xu, Wen Gu, Yang Yang, Hui Wang. Health effects of intermittent fasting and the role of artificial intelligence technologies in optimizing its clinical translation[J]. AIMS Public Health, 2025, 12(4): 1190-1222. doi: 10.3934/publichealth.2025061

Related Papers:

Abstract

As a non-pharmacological approach, Intermittent Fasting (IF) exhibits the capacity to boost health and counteract chronic diseases by regulating the metabolism, strengthening the cellular resistance to stress, and reshaping the immune microenvironment. The rapid progress of Artificial Intelligence (AI) technologies has greatly advanced our comprehension of IF's diverse health benefits. This review outlines AI's role in enhancing the exploration of IF's function in governing systemic health, clarifies the association between IF and health outcomes, and specifies AI's function in analyzing IF's impacts, which cover metabolic processes, cellular stress responses, disease prevention, and the development of personalized dietary strategies. By leveraging AI to integrate various omics datasets, the mechanisms through which IF prevents chronic diseases can be uncovered. This review discusses the challenges that AI faces in researching diet-related health mechanisms and presents an outlook on future developments. AI offers innovative methods to investigate IF's effects on chronic disease prevention, which could lay the foundation for more efficient strategies to support healthier and longer lifespans.

Acknowledgments

This study was supported by grants from the National Natural Science Foundation of China (82030099 and 82403381), the National Key R&D Program of China (2022YFD2101500), the Sailing Program of Shanghai Rising-Star Program (24YF2722700), the Innovative Research Team of High-Level Local Universities in Shanghai and Medical-Engineering Cross Foundation of Shanghai Jiao Tong University (YG2025LC14).

Authors' contributions

Chenghao Zhang and Lijun Chang conducted literature retrieval, data extraction, and draft the manuscript. Yanqiu Huang, Yadan Xu and Wen Gu participated in the organization of the review structure and key findings. Yang Yang and Hui Wang conceived the theme, supervised the work, obtained the fundings and finalized the manuscript.

Conflicts of interest

Hui Wang is an editor-in-chief for AIMS Public Health, Yang Yang is editorial board members of AIMS Public Health, and Hui Wang is guest editor for its Special Issue. They did not engage in the editorial review or the decision to publish this article. The authors declare no conflicts of interest.

References

[1]	Liu H, Yin P, Qi J, et al. (2024) Burden of non-communicable diseases in China and its provinces, 1990–2021: Results from the Global Burden of Disease Study 2021. Chin Med J 137: 2325-2333. https://doi.org/10.1097/cm9.0000000000003270
[2]	WHONoncommunicable diseases (2025). [cited 2025 December 12]. Available from: https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases
[3]	Bukhman G, Mocumbi A, Wroe E, et al. (2023) The PEN-Plus partnership: Addressing severe chronic non-communicable diseases among the poorest billion. Lancet Diabetes Endo 11: 384-386. https://doi.org/10.1016/s2213-8587(23)00118-3
[4]	Ramani-Chander A, Thrift A, van Olmen J, et al. (2023) Prioritising and planning scale-up research projects targeting non-communicable diseases: A mixed-method study by the Global Alliance for Chronic Diseases upscaling working group. BMJ Global Health 8: e012804. https://doi.org/10.1136/bmjgh-2023-012804
[5]	Duan H, Pan J, Guo M, et al. (2022) Dietary strategies with anti-aging potential: Dietary patterns and supplements. Food Res Int 158: 111501. https://doi.org/10.1016/j.foodres.2022.111501
[6]	Tessier AJ, Wang F, Korat AA, et al. (2025) Optimal dietary patterns for healthy aging. Nat Med 31: 1644-1652. https://doi.org/10.1038/s41591-025-03570-5
[7]	Shang X, Liu J, Zhu Z, et al. (2023) Healthy dietary patterns and the risk of individual chronic diseases in community-dwelling adults. Nat Commun 14: 6704. https://doi.org/10.1038/s41467-023-42523-9
[8]	O'Hearn M, Lara-Castor L, Cudhea F, et al. (2023) Incident type 2 diabetes attributable to suboptimal diet in 184 countries. Nat Med 29: 982-995. https://doi.org/10.1038/s41591-023-02278-8
[9]	Glenn AJ, Li J, Lo K, et al. (2023) The portfolio diet and incident type 2 diabetes: Findings from the women's health initiative prospective cohort study. Diabetes Care 46: 28-37. https://doi.org/10.2337/dc22-1029
[10]	Pagidipati NJ, Taub PR, Ostfeld RJ, et al. (2024) Dietary patterns to promote cardiometabolic health. Nat Rev Cardiol 22: 38-46. https://doi.org/10.1038/s41569-024-01061-7
[11]	Lim GH, Neelakantan N, Lee YQ, et al. (2024) Dietary patterns and cardiovascular diseases in Asia: A systematic review and meta-analysis. Adv Nutr 15: 100249. https://doi.org/10.1016/j.advnut.2024.100249
[12]	Turpin W, Dong M, Sasson G, et al. (2022) Mediterranean-like dietary pattern associations with gut microbiome composition and subclinical gastrointestinal inflammation. Gastroenterology 163: 685-698. https://doi.org/10.1053/j.gastro.2022.05.037
[13]	Shen X, Tilves C, Kim H, et al. (2024) Plant-based diets and the gut microbiome: Findings from the Baltimore Longitudinal Study of Aging. Am J Clin Nutr 119: 628-638. https://doi.org/10.1016/j.ajcnut.2024.01.006
[14]	Peters BA, Xing J, Chen GC, et al. (2023) Healthy dietary patterns are associated with the gut microbiome in the Hispanic Community Health Study/Study of Latinos. Am J Clin Nutr 117: 540-552. https://doi.org/10.1016/j.ajcnut.2022.11.020
[15]	Li Y, Jia X, Li C, et al. (2024) The global incident gastrointestinal cancers attributable to suboptimal diets from 1990 to 2018. Gastroenterology 167: 1141-1151. https://doi.org/10.1053/j.gastro.2024.07.009
[16]	Fadnes LT, Celis-Morales C, Økland JM, et al. (2023) Life expectancy can increase by up to 10 years following sustained shifts towards healthier diets in the United Kingdom. Nat Food 4: 961-965. https://doi.org/10.1038/s43016-023-00868-w
[17]	Madeo F, Carmona-Gutierrez D, Hofer SJ, et al. (2019) Caloric restriction mimetics against age-associated disease: Targets, mechanisms, and therapeutic potential. Cell Metab 29: 592-610. https://doi.org/10.1016/j.cmet.2019.01.018
[18]	Fanti M, Mishra A, Longo VD, et al. (2021) Time-restricted eating, intermittent fasting, and fasting-mimicking diets in weight loss. Curr Obes Rep 10: 70-80. https://doi.org/10.1007/s13679-021-00424-2
[19]	Margetis AT (2024) Caloric restriction for the management of malignant tumors – from animal studies towards clinical translation. Int J Vitam Nutr Res 94: 1-9. https://doi.org/10.1024/0300-9831/a000779
[20]	Varady KA, Cienfuegos S, Ezpeleta M, et al. (2022) Clinical application of intermittent fasting for weight loss: Progress and future directions. Nat Rev Endocrinol 18: 309-321. https://doi.org/10.1038/s41574-022-00638-x
[21]	Panteli D, Adib K, Buttigieg S, et al. (2025) Artificial intelligence in public health: Promises, challenges, and an agenda for policy makers and public health institutions. Lancet Public Health 10: e428-e432. https://doi.org/10.1016/s2468-2667(25)00036-2
[22]	Marra A, Morganti S, Pareja F, et al. (2025) Artificial intelligence entering the pathology arena in oncology: Current applications and future perspectives. Ann Oncol 36: 712-725. https://doi.org/10.1016/j.annonc.2025.03.006
[23]	Echle A, Rindtorff NT, Brinker TJ, et al. (2020) Deep learning in cancer pathology: A new generation of clinical biomarkers. Brit J Cancer 124: 686-696. https://doi.org/10.1038/s41416-020-01122-x
[24]	Monlezun DJ, MacKay K (2024) Artificial intelligence and health inequities in dietary interventions on atherosclerosis: A narrative review. Nutrients 16: 2601. https://doi.org/10.3390/nu16162601
[25]	Karakan T, Gundogdu A, Alagözlü H, et al. (2022) Artificial intelligence-based personalized diet: A pilot clinical study for irritable bowel syndrome. Gut Microbes 14: e2138672. https://doi.org/10.1080/19490976.2022.2138672
[26]	Patikorn C, Roubal K, Veettil SK, et al. (2021) Intermittent fasting and obesity-related health outcomes: An umbrella review of meta-analyses of randomized clinical Trials. JAMA Netw Open 4: e2139558. https://doi.org/10.1001/jamanetworkopen.2021.39558
[27]	Ciastek B, Kapłon K, Domaszewski P (2025) A comprehensive perspective on the biological effects of intermittent fasting and periodic short-term fasting: A promising strategy for optimizing metabolic health. Nutrients 17: 2061. https://doi.org/10.3390/nu17132061
[28]	Ezpeleta M, Cienfuegos S, Lin S, et al. (2024) Time-restricted eating: Watching the clock to treat obesity. Cell Metab 36: 301-314. https://doi.org/10.1016/j.cmet.2023.12.004
[29]	Wei M, Brandhorst S, Shelehchi M, et al. (2017) Fasting-mimicking diet and markers/risk factors for aging, diabetes, cancer, and cardiovascular disease. Sci Transl Med 9: eaai8700. https://doi.org/10.1126/scitranslmed.aai8700
[30]	Zhang X, Zou Q, Zhao B, et al. (2020) Effects of alternate-day fasting, time-restricted fasting and intermittent energy restriction DSS-induced on colitis and behavioral disorders. Redox Biol 32: 101535. https://doi.org/10.1016/j.redox.2020.101535
[31]	Mattson MP, Allison DB, Fontana L, et al. (2014) Meal frequency and timing in health and disease. PNAS 111: 16647-11653. https://doi.org/10.1073/pnas.1413965111
[32]	Ezpeleta M, Gabel K, Cienfuegos S, et al. (2023) Effect of alternate day fasting combined with aerobic exercise on non-alcoholic fatty liver disease: A randomized controlled trial. Cell Metab 35: 56-70.e3. https://doi.org/10.1016/j.cmet.2022.12.001
[33]	Kapogiannis D, Manolopoulos A, Mullins R, et al. (2024) Brain responses to intermittent fasting and the healthy living diet in older adults. Cell Metab 36: 1668-1678.e5. https://doi.org/10.1016/j.cmet.2024.05.017
[34]	Črešnovar T, Habe B, Mohorko N, et al. (2025) Early time-restricted eating with energy restriction has a better effect on body fat mass, diastolic blood pressure, metabolic age and fasting glucose compared to late time-restricted eating with energy restriction and/or energy restriction alone: A 3-month randomized clinical trial. Clin Nutr 49: 57-68. https://doi.org/10.1016/j.clnu.2025.04.001
[35]	Aktaş H, Atakan MM, Aktitiz S, et al. (2025) Six weeks of time-restricted eating improves basal fat oxidation and body composition but not fat oxidation during exercise in young males. Clin Nutr 50: 92-103. https://doi.org/10.1016/j.clnu.2025.04.022
[36]	Teong XT, Liu K, Vincent AD, et al. (2023) Intermittent fasting plus early time-restricted eating versus calorie restriction and standard care in adults at risk of type 2 diabetes: A randomized controlled trial. Nat Med 29: 963-972. https://doi.org/10.1038/s41591-023-02287-7
[37]	Ceperuelo-Mallafré V, Rodríguez-Peña MM, Badia J, et al. (2025) Dietary switch and intermittent fasting ameliorate the disrupted postprandial short-chain fatty acid response in diet-induced obese mice. eBioMedicine 117: 105827. https://doi.org/10.1016/j.ebiom.2025.105827
[38]	Bonham MP, Leung GKW, Rogers M, et al. (2025) Intermittent fasting for weight loss in night shift workers: A three-arm, superiority randomised clinical trial. eBioMedicine 117: 105803. https://doi.org/10.1016/j.ebiom.2025.105803
[39]	Azhar K, Ramirez-Obermayer A, Sourij C, et al. (2025) Sustained weight reduction following 12 weeks of intermittent fasting intervention in people with insulin-treated type 2 diabetes—Two-year follow-up of the randomised controlled InterFast-2 trial. Diabetes Obes Metab 27: 1605-1608. https://doi.org/10.1111/dom.16158
[40]	Patel S, Yan Z, Remedi MS (2024) Intermittent fasting protects β-cell identity and function in a type-2 diabetes model. Metabolism 153: 155813. https://doi.org/10.1016/j.metabol.2024.155813
[41]	Badreh F, Joukar S, Badavi M, et al. (2024) Fasting recovers age-related hypertension in the rats: reset of renal renin-angiotensin system components and klotho. BMC Nephrol 25: 470. https://doi.org/10.1186/s12882-024-03918-y
[42]	Xie K, Wang C, Scifo E, et al. (2025) Intermittent fasting boosts sexual behavior by limiting the central availability of tryptophan and serotonin. Cell Metab 37: 1189-1205.e7. https://doi.org/10.1016/j.cmet.2025.03.001
[43]	Zhao Z, Chen JL, Zhan H, et al. (2024) Noradrenergic projections from the locus coeruleus to the medial prefrontal cortex enhances stress coping behavior in mice following long-term intermittent fasting. Neuromol Med 26: 47. https://doi.org/10.1007/s12017-024-08818-w
[44]	Liow CH, Mohd Esa N, Yaacob A, et al. (2025) Effects of time-restricted feeding and weight-loaded swimming test on androgen levels and androgen receptor expression in orchiectomized male Wistar rats. Clin Nutr ESPEN 65: 36-42. https://doi.org/10.1016/j.clnesp.2024.11.001
[45]	Liu Z, Zhao Z, Du H, et al. (2025) Intermittent fasting enhances motor coordination through myelin preservation in aged mice. Aging Cell 24: e14476. https://doi.org/10.1111/acel.14476
[46]	Whittaker DS, Akhmetova L, Carlin D, et al. (2023) Circadian modulation by time-restricted feeding rescues brain pathology and improves memory in mouse models of Alzheimer's disease. Cell Metab 35: 1704-1721.e6. https://doi.org/10.1016/j.cmet.2023.07.014
[47]	Gaugel J, Jähnert M, Neumann A, et al. (2025) Alternative splicing landscape in mouse skeletal muscle and adipose tissue: Effects of intermittent fasting and exercise. J Nutr Biochem 137: 109837. https://doi.org/10.1016/j.jnutbio.2024.109837
[48]	Qiu Z, Huang EYZ, Li Y, et al. (2024) Beneficial effects of time-restricted fasting on cardiovascular disease risk factors: A meta-analysis. BMC Cardiovasc Disord 24: 210. https://doi.org/10.1186/s12872-024-03863-6
[49]	Beveridge J, Montgomery A, Grossberg G (2025) Intermittent fasting and neurocognitive disorders: What the evidence shows. J Nutr Health Aging 29: 100480. https://doi.org/10.1016/j.jnha.2025.100480
[50]	Huang Z, Li Y, Park H, et al. (2023) Unveiling and harnessing the human gut microbiome in the rising burden of non-communicable diseases during urbanization. Gut Microbes 15: 2237645. https://doi.org/10.1080/19490976.2023.2237645
[51]	Hansen B, Sánchez-Castro M, Schintgen L, et al. (2025) The impact of fasting and caloric restriction on rheumatoid arthritis in humans: A narrative review. Clin Nutr 49: 178-186. https://doi.org/10.1016/j.clnu.2025.04.025
[52]	Yun F, Han X, Wang Z, et al. (2025) Intermittent fasting ameliorates resistant hypertension through modulation of gut microbiota. Pharmacol Res 219: 107864. https://doi.org/10.1016/j.phrs.2025.107864
[53]	Shi H, Zhang B, Abo-Hamzy T, et al. (2021) Restructuring the gut microbiota by intermittent fasting lowers blood pressure. Circ Res 128: 1240-1254. https://doi.org/10.1161/circresaha.120.318155
[54]	Nan K, Zhong Z, Yue Y, et al. (2025) Fasting-mimicking diet-enriched bifidobacterium pseudolongum suppresses colorectal cancer by inducing memory CD8+ T cells. Gut 74: 775-786. https://doi.org/10.1136/gutjnl-2024-333020
[55]	Jia M, Shi L, Zhao Y, et al. (2022) Intermittent fasting mitigates cognitive deficits in Alzheimer's disease via the gutbrain axis [Preprint]. bioRxiv : 2022.05.11.491466. https://doi.org/10.1101/2022.05.11.491466
[56]	Chadwick JS, Decourt C, Müller F, et al. (2025) Dietary-dependent sensitization of neuronal leptin signaling promotes neural repair after injury via cAMP and gene transcription. Neuron 113: 2839-2855.e8. https://doi.org/10.1016/j.neuron.2025.07.016
[57]	Song M, Zeng F, Huang L, et al. (2024) Energy restriction inhibits β-catenin ubiquitination to improve ischemic stroke injury via USP18/SKP2 axis. Metab Brain Dis 40: 68. https://doi.org/10.1007/s11011-024-01494-6
[58]	Chakraborty P, Dromard Y, André EM, et al. (2025) Meal scheduling corrects obesogenic diet induced-uncoupling of cortico-hippocampal activities supporting memory. eBioMedicine 117: 105783. https://doi.org/10.1016/j.ebiom.2025.105783
[59]	Gallage S, Ali A, Barragan Avila JE, et al. (2024) A 5:2 intermittent fasting regimen ameliorates NASH and fibrosis and blunts HCC development via hepatic PPARα and PCK1. Cell Metab 36: 1371-1393.e7. https://doi.org/10.1016/j.cmet.2024.04.015
[60]	Di Biase S, Lee C, Brandhorst S, et al. (2016) Fasting-mimicking diet reduces HO-1 to promote T cell-mediated tumor cytotoxicity. Cancer Cell 30: 136-146. https://doi.org/10.1016/j.ccell.2016.06.005
[61]	Caffa I, Spagnolo V, Vernieri C, et al. (2020) Fasting-mimicking diet and hormone therapy induce breast cancer regression. Nature 583: 620-624. https://doi.org/10.1038/s41586-020-2502-7
[62]	Bou Malhab LJ, Madkour MI, Abdelrahim DN, et al. (2025) Dawn-to-dusk intermittent fasting is associated with overexpression of autophagy genes: A prospective study on overweight and obese cohort. Clin Nutr ESPEN 65: 209-217. https://doi.org/10.1016/j.clnesp.2024.11.002
[63]	Hofer SJ, Daskalaki I, Bergmann M, et al. (2024) Spermidine is essential for fasting-mediated autophagy and longevity. Nat Cell Biol 26: 1571-1584. https://doi.org/10.1038/s41556-024-01468-x
[64]	Alli-Shaik A, Arumugam TV, Liehn EA, et al. (2023) Multiomics analyses reveal dynamic bioenergetic pathways and functional remodeling of the heart during intermittent fasting. eLife 12: 89214. https://doi.org/10.7554/eLife.89214
[65]	Mohamed YA, Abouelmagd M, Elbialy A, et al. (2024) Effect of intermittent fasting on lipid biokinetics in obese and overweight patients with type 2 diabetes mellitus: Prospective observational study. Diabetol Metab Syndr 16: 4. https://doi.org/10.1186/s13098-023-01234-3
[66]	Qian J, Fang Y, Yuan N, et al. (2021) Innate immune remodeling by short-term intensive fasting. Aging Cell 20: 13507. https://doi.org/10.1111/acel.13507
[67]	Eraky SM, Ramadan NM, Atif HM, et al. (2025) The ameliorating effect of intermittent fasting on intestinal glucagon-like peptide 1 in rats fed a high-fat diet via the Farnesoid X receptor and the Melanocortin-4 receptor. Life Sci 361: 123327. https://doi.org/10.1016/j.lfs.2024.123327
[68]	Perez-Kast RC, Camacho-Morales A (2025) Fasting the brain for mental health. J Psychiatr Res 181: 215-224. https://doi.org/10.1016/j.jpsychires.2024.11.041
[69]	de Cabo R, Longo DL, Mattson MP (2019) Effects of intermittent fasting on health, aging, and disease. New England Journal of Medicine 381: 2541-2551. https://doi.org/10.1056/NEJMra1905136
[70]	Szegő ÉM, Höfs L, Antoniou A, et al. (2025) Intermittent fasting reduces alpha-synuclein pathology and functional decline in a mouse model of Parkinson's disease. Nat Commun 16: 4470. https://doi.org/10.1038/s41467-025-59249-5
[71]	Xin H, Huang R, Zhou M, et al. (2023) Daytime-restricted feeding enhances running endurance without prior exercise in mice. Nat Metab 5: 1236-1251. https://doi.org/10.1038/s42255-023-00826-7
[72]	Aon MA, Bernier M, Mitchell SJ, et al. (2020) Untangling determinants of enhanced health and lifespan through a multi-omics approach in mice. Cell Metab 32: 100-116.e4. https://doi.org/10.1016/j.cmet.2020.04.018
[73]	Deota S, Lin T, Chaix A, et al. (2023) Diurnal transcriptome landscape of a multi-tissue response to time-restricted feeding in mammals. Cell Metab 35: 150-165.e4. https://doi.org/10.1016/j.cmet.2022.12.006
[74]	Duregon E, Fernandez ME, Martinez Romero J, et al. (2023) Prolonged fasting times reap greater geroprotective effects when combined with caloric restriction in adult female mice. Cell Metabolism 35: 1179-1194.e5. https://doi.org/10.1016/j.cmet.2023.05.003
[75]	Hu FB (2023) Diet strategies for promoting healthy aging and longevity: An epidemiological perspective. J Intern Med 295: 508-531. https://doi.org/10.1111/joim.13728
[76]	Longo VD, Di Tano M, Mattson MP, et al. (2021) Intermittent and periodic fasting, longevity and disease. Nat Aging 1: 47-59. https://doi.org/10.1038/s43587-020-00013-3
[77]	Reicher L, Shilo S, Godneva A, et al. (2025) Deep phenotyping of health–disease continuum in the human phenotype project. Nat Med 31: 3191-3203. https://doi.org/10.1038/s41591-025-03790-9
[78]	Arya SS, Dias SB, Jelinek HF, et al. (2023) The convergence of traditional and digital biomarkers through AI-assisted biosensing: A new era in translational diagnostics?. Biosens Bioelectron 235: 115387. https://doi.org/10.1016/j.bios.2023.115387
[79]	Lyall DM, Kormilitzin A, Lancaster C, et al. (2023) Artificial intelligence for dementia—Applied models and digital health. Alzheimers Dement 19: 5872-5884. https://doi.org/10.1002/alz.13391
[80]	Xu Y, Liu X, Cao X, et al. (2021) Artificial intelligence: A powerful paradigm for scientific research. Innov 2: 100179. https://doi.org/10.1016/j.xinn.2021.100179
[81]	Forrest IS, Petrazzini BO, Duffy Á, et al. (2023) Machine learning-based marker for coronary artery disease: derivation and validation in two longitudinal cohorts. Lancet 1: 215-225. https://doi.org/10.1016/s0140-6736(22)02079-7
[82]	He B, Kwan AC, Cho JH, et al. (2023) Blinded, randomized trial of sonographer versus AI cardiac function assessment. Nature 616: 520-524. https://doi.org/10.1038/s41586-023-05947-3
[83]	Naoumov NV, Brees D, Loeffler J, et al. (2022) Digital pathology with artificial intelligence analyses provides greater insights into treatment-induced fibrosis regression in NASH. J Hepatol 77: 1399-1409. https://doi.org/10.1016/j.jhep.2022.06.018
[84]	Noureddin M, Goodman Z, Tai D, et al. (2023) Machine learning liver histology scores correlate with portal hypertension assessments in nonalcoholic steatohepatitis cirrhosis. Aliment Pharmacol Ther 57: 409-417. https://doi.org/10.1111/apt.17363
[85]	Bosch J, Chung C, Carrasco-Zevallos OM, et al. (2021) A machine learning approach to liver histological evaluation predicts clinically significant portal hypertension in NASH cirrhosis. Hepatology 74: 3146-3160. https://doi.org/10.1002/hep.32087
[86]	Li J, Yuan P, Hu X, et al. (2021) A tongue features fusion approach to predicting prediabetes and diabetes with machine learning. J Biomed Inform 115: 103693. https://doi.org/10.1016/j.jbi.2021.103693
[87]	Zhang K, Liu X, Xu J, et al. (2021) Deep-learning models for the detection and incidence prediction of chronic kidney disease and type 2 diabetes from retinal fundus images. Nat Biomed Eng 5: 533-545. https://doi.org/10.1038/s41551-021-00745-6
[88]	Takahashi M, Tahara Y (2022) Timing of food/nutrient intake and its health benefits. J Nutr Sci Vitaminol (Tokyo) 68: S2-S4. https://doi.org/10.3177/jnsv.68.S2
[89]	Zeevi D, Korem T, Zmora N, et al. (2015) Personalized nutrition by prediction of glycemic responses. Cell 163: 1079-1094. https://doi.org/10.1016/j.cell.2015.11.001
[90]	Amiri M, Li J, Hasan W (2023) Personalized flexible meal planning for individuals with diet-related health concerns: System design and feasibility validation study. JMIR Form Res 7: e46434. https://doi.org/10.2196/46434
[91]	Gisbert S (2018) Automating drug discovery. Nat Rev Drug Discov 17: 97-113. https://doi.org/10.1038/nrd.2017.232
[92]	Alsubaie MG, Luo S, Shaukat K (2024) Alzheimer's disease detection using deep learning on neuroimaging: A systematic review. Mach Learn-Sci Techn 6: 464-505. https://doi.org/10.3390/make6010024
[93]	Saleem TJ, Zahra SR, Wu F, et al. (2022) Deep learning-based diagnosis of alzheimer's disease. J Pers Med 12: 815. https://doi.org/10.3390/jpm12050815
[94]	Xu M, Ouyang Y, Yuan Z (2023) Deep learning aided neuroimaging and brain regulation. Sensors 23: 4993. https://doi.org/10.3390/s23114993
[95]	He B, Dong D, She Y, et al. (2020) Predicting response to immunotherapy in advanced non-small-cell lung cancer using tumor mutational burden radiomic biomarker. JICT 8: e000550. https://doi.org/10.1136/jitc-2020-000550
[96]	Bao X, Shi R, Zhao T, et al. (2020) Immune landscape and a novel immunotherapy-related gene signature associated with clinical outcome in early-stage lung adenocarcinoma. J Mol Med 98: 805-818. https://doi.org/10.1007/s00109-020-01908-9
[97]	Johannet P, Coudray N, Donnelly DM, et al. (2021) Using machine learning algorithms to predict immunotherapy response in patients with advanced melanoma. Clin Cancer Res 27: 131-140. https://doi.org/10.1158/1078-0432.Ccr-20-2415
[98]	Arbour KC, Luu AT, Luo J, et al. (2021) Deep learning to estimate RECIST in patients with NSCLC treated with PD-1 blockade. Cancer Discov 11: 59-67. https://doi.org/10.1158/2159-8290.Cd-20-0419
[99]	Javaid M, Haleem A, Pratap Singh R, et al. (2022) Significance of machine learning in healthcare: Features, pillars and applications. IJIN 3: 58-73. https://doi.org/10.1016/j.ijin.2022.05.002
[100]	McLeish E, Slater N, Mastaglia FL, et al. (2024) From data to diagnosis: How machine learning is revolutionizing biomarker discovery in idiopathic inflammatory myopathies. Brief Bioinform 25: bbad514. https://doi.org/10.1093/bib/bbad514
[101]	Peng J, Jury EC, Dönnes P, et al. (2021) Machine learning techniques for personalised medicine approaches in immune-mediated chronic inflammatory diseases: Applications and challenges. Front Pharmacol 12: 720694. https://doi.org/10.3389/fphar.2021.720694
[102]	Panagoulias DP, Sotiropoulos DN, Tsihrintzis GA (2021) Nutritional biomarkers and machine learning for personalized nutrition applications and health optimization. Intell Decis Technol 15: 645-653. https://doi.org/10.1109/IISA52424.2021.9555512
[103]	Brian Hie EDZ, Bonnie Berger, Bryan Bryson (2021) Learning the language of viral evolution and escape. Science 371: 284-288. https://doi.org/10.1126/science.abd7331
[104]	Chen H, Li F, Wang L, et al. (2021) Systematic evaluation of machine learning methods for identifying human–pathogen protein–protein interactions. Brief Bioinform 22: bbaa068. https://doi.org/10.1093/bib/bbaa068
[105]	Bojar D, Powers RK, Camacho DM, et al. (2021) Deep-learning resources for studying glycan-mediated host-microbe interactions. Cell Host Microbe 29: 132-144.e3. https://doi.org/10.1016/j.chom.2020.10.004
[106]	Rahman MM, Vadrev SM, Magana-Mora A, et al. (2022) A novel graph mining approach to predict and evaluate food-drug interactions. Sci Rep 12: 1061. https://doi.org/10.1038/s41598-022-05132-y
[107]	Cohen Y, Valdés-Mas R, Elinav E (2023) The role of artificial intelligence in deciphering diet–disease relationships: Case studies. Annu Rev Nutr 43: 225-250. https://doi.org/10.1146/annurev-nutr-061121-090535
[108]	Cifuentes L, Anazco D, O'Connor T, et al. (2025) Genetic and physiological insights into satiation variability predict responses to obesity treatment. Cell Metab 37: 1655-1666.e5. https://doi.org/10.1016/j.cmet.2025.05.008
[109]	Wang T, Fu Y, Shuai M, et al. (2024) Microbiome-based correction for random errors in nutrient profiles derived from self-reported dietary assessments. Nat Commun 15: 9112. https://doi.org/10.1038/s41467-024-53567-w
[110]	Wang T, Holscher HD, Maslov S, et al. (2025) Predicting metabolite response to dietary intervention using deep learning. Nat Commun 16: 815. https://doi.org/10.1038/s41467-025-56165-6
[111]	Nishijima S, Stankevic E, Aasmets O, et al. (2025) Fecal microbial load is a major determinant of gut microbiome variation and a confounder for disease associations. Cell 188: 222-236.e15. https://doi.org/10.1016/j.cell.2024.10.022
[112]	Spoladore D, Stella F, Tosi M, et al. (2024) A knowledge-based decision support system to support family doctors in personalizing type-2 diabetes mellitus medical nutrition therapy. Comput Biol Med 180: 109001. https://doi.org/10.1016/j.compbiomed.2024.109001
[113]	Xian G, Yanbing Z, Jingying L, et al. (2023) Visualization analysis of global advances and hot spots in intermittent fasting. CGP 26: 2036-2046. https://doi.org/10.12114/j.issn.1007-9572.2022.0811
[114]	Chen Y, Li X, Yang M, et al. (2024) Time-restricted eating reveals a “younger” immune system and reshapes the intestinal microbiome in human. Redox Biology 78: 103422. https://doi.org/10.1016/j.redox.2024.103422
[115]	Fang L, Huang C, Lin B, et al. (2025) Evaluation of mobile intermittent fasting applications in Chinese app stores: Quality evaluations and content analysis. JMIR mHealth uHealth 13: e66339-e. https://doi.org/10.2196/66339
[116]	Frizzell TO, Glashutter M, Liu CC, et al. (2022) Artificial intelligence in brain MRI analysis of Alzheimer's disease over the past 12 years: A systematic review. Ageing Res Rev 77: 101614. https://doi.org/10.1016/j.arr.2022.101614
[117]	Soroush A, Giuffrè M, Chung S, et al. (2025) Generative artificial intelligence in clinical medicine and impact on gastroenterology. Gastroenterology 169: 502-517.e1. https://doi.org/10.1053/j.gastro.2025.03.038
[118]	Narang A, Bae R, Hong H, et al. (2021) Utility of a deep-learning algorithm to guide novices to acquire echocardiograms for limited diagnostic use. JAMA Cardiol 6: 624-632. https://doi.org/10.1001/jamacardio.2021.0185
[119]	Smeland OB, Busch C, Andreassen OA, et al. (2025) Novel multimodal precision medicine approaches and the relevance of developmental trajectories in bipolar disorder. Biol Psychiat 98: 343-353. https://doi.org/10.1016/j.biopsych.2025.03.010
[120]	Folson GK, Bannerman B, Atadze V, et al. (2023) Validation of mobile artificial intelligence technology–assisted dietary assessment tool against weighed records and 24-hour recall in adolescent females in Ghana. J Nutr 153: 2328-2338. https://doi.org/10.1016/j.tjnut.2023.06.001
[121]	Aggarwal A, Bharadwaj S, Corredor G, et al. (2025) Artificial intelligence in digital pathology — time for a reality check. Nat Rev Clin Oncol 22: 283-291. https://doi.org/10.1038/s41571-025-00991-6

Reader Comments

Your name:*

Email:*
© 2025 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)