Alexander, C. S., A. Smith, and R. Ivanek. 2023a. Safer not to know?: Shaping liability law and policy to incentivize adoption of predictive AI technologies in the food system. Frontiers in Artificial Intelligence 6:1298604.
Alexander, C., M. Yarborough, and A. Smith. 2023b. Who is responsible for “responsible AI”?: Navigating challenges to build trust in AI agriculture and food system technology. Precision Agriculture 1–40.
Ayoub Shaikh, T., T. Rasool, and F. Rasheed Lone. 2022. Towards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming. Computers and Electronics in Agriculture 198:107119.
Azarianpour Esfahani, S., P. Fu, H. Mahdi, and A. Madabhushi. 2021. Computational features of TIL architecture are differentially prognostic of uterine cancer between African and Caucasian American women. Journal of Clinical Oncology 39(15_suppl):5585.
Bathgate, K. E., J. L. Sherriff, H. Leonard, S. S. Dhaliwal, E. J. Delp, C. J. Boushey, and D. A. Kerr. 2017. Feasibility of assessing diet with a mobile food record for adolescents and young adults with Down Syndrome. Nutrients 9(3).
Ben-Yacov, O., A. Godneva, M. Rein, S. Shilo, D. Kolobkov, N. Koren, N. Cohen Dolev, T. Travinsky Shmul, B. C. Wolf, N. Kosower, K. Sagiv, M. Lotan-Pompan, N. Zmora, A. Weinberger, E. Elinav, and E. Segal. 2021. Personalized postprandial glucose response-targeting diet versus Mediterranean diet for glycemic control in prediabetes. Diabetes Care 44(9):1980–1991.
Bera, K., N. Braman, A. Gupta, V. Velcheti, and A. Madabhushi. 2022. Predicting cancer outcomes with radiomics and artificial intelligence in radiology. Nature Reviews: Clinical Oncology 19(2):132–146.
Brown, A. G. M., S. Shi, S. Adas, J. E. A. Boyington, P. A. Cotton, B. Jirles, N. Rajapakse, J. Reedy, K. Regan, D. Xi, G. Zappalà, and T. Agurs-Collins. 2022. A decade of nutrition and health disparities research at NIH, 2010–2019. American Journal of Preventive Medicine 63(2):e49–e57.
Cech, E. A., and T. J. Waidzunas. 2021. Systemic inequalities for LGBTQ professionals in STEM. Science Advances 7(3):eabe0933. https://doi.org/10.1126/sciadv.abe0933.
Chen, G., W. Jia, Y. Zhao, Z. H. Mao, B. Lo, A. K. Anderson, G. Frost, M. L. Jobarteh, M. A. McCrory, E. Sazonov, M. Steiner-Asiedu, R. S. Ansong, T. Baranowski, L. Burke, and M. Sun. 2021. Food/non-food classification of real-life egocentric images in low- and middle-income countries based on image tagging features. Frontiers in Artificial X_Inteligence 4:644712.
Coleman-Jensen, A., M. Rabbitt, C. Gregory, and A. Singh. 2022. Household food security in the United States in 2021. ERR-309. U.S. Department of Agriculture, Economic Research Service. https://www.ers.usda.gov/publications/pub-details/?pubid=104655 (accessed December 26, 2023).
Côté, M., and B. Lamarche. 2021. Artificial intelligence in nutrition research: Perspectives on current and future applications. Applied Physiology, Nutrition, and Metabolism 1–8.
Côté, M., M. A. Osseni, D. Brassard, É. Carbonneau, J. Robitaille, M. C. Vohl, S. Lemieux, F. Laviolette, and B. Lamarche. 2022. Are machine learning algorithms more accurate in predicting vegetable and fruit consumption than traditional statistical models? An exploratory analysis. Frontiers in Nutrition 9:740898.
Das, S. 2021. Delivering locally sourced nutritious food to Indian households. MIT. https://ctl.mit.edu/pub/thesis/delivering-locally-sourced-nutritious-food-indian-households (accessed December 26, 2023).
de la Cuesta-Zuluaga, J., S. T. Kelley, Y. Chen, J. S. Escobar, N. T. Mueller, R. E. Ley, D. McDonald, S. Huang, A. D. Swafford, R. Knight, and V. G. Thackray. 2019. Age- and sex-dependent patterns of gut microbial diversity in human adults. mSystems 4(4).
DeGrave, A. J., J. D. Janizek, and S.-I. Lee. 2021. AI for radiographic COVID-19 detection selects shortcuts over signal. Nature Machine Intelligence 3(7):610–619.
Dhamdhere, R., G. Modanwal, M. H. E. Makhlouf, N. S. Hassani, S. Bharadwaj, P. Fu, I. Milloglou, M. Rahman, S. Al-Kindi, and A. Madabhushi. 2023. STAR-echo: A novel biomarker for prognosis of MACE in chronic kidney disease patients using spatiotemporal analysis and transformer-based radiomics models. Paper presented at Medical Image Computing and Computer-Assisted Intervention 2023: 26th International Conference, Vancouver, BC.
Dong, V., D. D. Sevgi, S. S. Kar, S. K. Srivastava, J. P. Ehlers, and A. Madabhushi. 2022. Evaluating the utility of deep learning for predicting therapeutic response in diabetic eye disease. Frontiers in Ophthalmology 2:852107. https://doi.org/10.3389/fopht.2022.852107.
Dong, Y., A. Hoover, J. Scisco, and E. Muth. 2012. A new method for measuring meal intake in humans via automated wrist motion tracking. Applied Psychophysiology and Biofeedback 37(3):205–215.
Doulah, A., M. Farooq, X. Yang, J. Parton, M. A. McCrory, J. A. Higgins, and E. Sazonov. 2017. Meal microstructure characterization from sensor-based food intake detection. Frontiers in Nutrition 4:31.
Doulah, A., T. Ghosh, D. Hossain, M. Imtiaz, and E. Sazonov. 2021. “Automatic ingestion monitor version 2”—a novel wearable device for automatic food intake detection and passive capture of food images. IEEE Journal of Biomedical and Health Informatics 25(2):568–576.
Doulah, A., T. Ghosh, D. Hossain, T. Marden, J. M. Parton, J. A. Higgins, M. A. McCrory, and E. Sazonov. 2022. Energy intake estimation using a novel wearable sensor and food images in a laboratory (pseudo-free-living) meal setting: Quantification and contribution of sources of error. International Journal of Obesity 46(11):2050–2057.
Dratsch, T., X. Chen, M. R. Mehrizi, R. Kloeckner, A. Mähringer-Kunz, M. Püsken, B. Baeßler, S. Sauer, D. Maintz, and D. Pinto dos Santos. 2023. Automation bias in mammography: The impact of artificial intelligence BI-RADS suggestions on reader performance. Radiology 307(4):e222176.
Drukker, K., W. Chen, J. Gichoya, N. Gruszauskas, J. Kalpathy-Cramer, S. Koyejo, K. Myers, R. C. Sá, B. Sahiner, H. Whitney, Z. Zhang, and M. Giger. 2023. Toward fairness in artificial intelligence for medical image analysis: Identification and mitigation of potential biases in the roadmap from data collection to model deployment. Journal of Medical Imaging 10(6):061104.
FAO (Food and Agriculture Organization). 2020. The state of food security and nutrition in the world 2020. Transforming food systems for affordable healthy diets. Rome: FAO. https://doi.org/10.4060/ca9692en (accessed January 9, 2024).
Farooq, M., and E. Sazonov. 2017. Segmentation and characterization of chewing bouts by monitoring temporalis muscle using smart glasses with piezoelectric sensor. IEEE Journal of Biomedical Health Information 21(6):1495–1503.
Farooq, M., J. M. Fontana, and E. Sazonov. 2014. A novel approach for food intake detection using electroglottography. Physiological Measurement 35(5):739–751.
Farooq, M., P. C. Chandler-Laney, M. Hernandez-Reif, and E. Sazonov. 2015. Monitoring of infant feeding behavior using a jaw motion sensor. Journal of Healthcare Engineering 6(1):23–40.
Garaulet, M., P. Gómez-Abellán, J. J. Alburquerque-Béjar, Y. C. Lee, J. M. Ordovás, and F. A. Scheer. 2013. Timing of food intake predicts weight loss effectiveness. International Journal of Obesity 37(4):604–611.
Garaulet, M., B. Vera, G. Bonnet-Rubio, P. Gomez-Abellan, Y. C. Lee, and J. M. Ordovas. 2016. Lunch eating predicts weight-loss effectiveness in carriers of the common allele at PERILIPIN1: The ONTIME (Obesity, Nutrigenetics, Timing, Mediterranean) study. American Journal of Clinical Nutrition 104(4):1160–1166.
Gauglitz, J. M., K. A. West, W. Bittremieux, C. L. Williams, K. C. Weldon, M. Panitchpakdi, F. Di Ottavio, C. M. Aceves, E. Brown, N. C. Sikora, A. K. Jarmusch, C. Martino, A. Tripathi, M. J. Meehan, K. Dorrestein, J. P. Shaffer, R. Coras, F. Vargas, L. D. Goldasich, T. Schwartz, M. Bryant, G. Humphrey, A. J. Johnson, K. Spengler, P. Belda-Ferre, E. Diaz, D. McDonald, Q. Zhu, E. O. Elijah, M. Wang, C. Marotz, K. E. Sprecher, D. Vargas-Robles, D. Withrow, G. Ackermann, L. Herrera, B. J. Bradford, L. M. M. Marques, J. G. Amaral, R. M. Silva, F. P. Veras, T. M. Cunha, R. D. R. Oliveira, P. Louzada-Junior, R. H. Mills, P. K. Piotrowski, S. L. Servetas, S. M. Da Silva, C. M. Jones, N. J. Lin, K. A. Lippa, S. A. Jackson, R. K. Daouk, D. Galasko, P. S. Dulai, T. I. Kalashnikova, C. Wittenberg, R. Terkeltaub, M. M. Doty, J. H. Kim, K. E. Rhee, J. Beauchamp-Walters, K. P. Wright, M. G. Dominguez-Bello, M. Manary, M. F. Oliveira, B. S. Boland, N. P. Lopes, M. Guma, A. D. Swafford, R. J. Dutton, R. Knight, and P. C. Dorrestein. 2022. Enhancing untargeted metabolomics using metadata-based source annotation. Nature Biotechnology 40(12):1774–1779.
Gelaye, B., S. J. Sumner, S. McRitchie, J. E. Carlson, C. V. Ananth, D. A. Enquobahrie, C. Qiu, T. K. Sorensen, and M. A. Williams. 2016. Maternal early pregnancy serum metabolomics profile and abnormal vaginal bleeding as predictors of placental abruption: A prospective study. PloS One 11(6):e0156755.
Ghanbari, R., Y. Li, W. Pathmasiri, S. McRitchie, A. Etemadi, J. D. Pollock, H. Poustchi, A. Rahimi-Movaghar, M. Amin-Esmaeili, G. Roshandel, A. Shayanrad, B. Abaei, R. Malekzadeh, and S. C. J. Sumner. 2021. Metabolomics reveals biomarkers of opioid use disorder. Translational Psychiatry 11(1):103.
Ghosh, T., D. Hossain, and E. Sazonov. 2021. Detection of food intake sensor’s wear compliance in free-living. IEEE Sensors Journal 21(24):27728–27735.
Gichoya, J. W., I. Banerjee, A. R. Bhimireddy, J. L. Burns, L. A. Celi, L. C. Chen, R. Correa, N. Dullerud, M. Ghassemi, S. C. Huang, P. C. Kuo, M. P. Lungren, L. J. Palmer, B. J. Price, S. Purkayastha, A. T. Pyrros, L. Oakden-Rayner, C. Okechukwu, L. Seyyed-Kalantari, H. Trivedi, R. Wang, Z. Zaiman, and H. Zhang. 2022. AI recognition of patient race in medical imaging: A modelling study. Lancet Digital Health 4(6):e406–e414.
Hassan, M. A., and E. Sazonov. 2020. Selective content removal for egocentric wearable camera in nutritional studies. IEEE Access 8:198615–198623.
He, Y., C. Xu, N. Khanna, C. J. Boushey, and E. J. Delp. 2013. Food image analysis: Segmentation, identification and weight estimation. Proceedings of the IEEE International Conference on Multimedia and Expo. https://doi.org/10.1109/ICME.2013.6607548.
Hoppe, T. A., A. Litovitz, K. A. Willis, R. A. Meseroll, M. J. Perkins, B. I. Hutchins, A. F. Davis, M. S. Lauer, H. A. Valantine, J. M. Anderson, and G. M. Santangelo. 2019. Topic choice contributes to the lower rate of NIH awards to African-American/Black scientists. Science Advances 5(10):eaaw7238.
Hossain, D., T. Ghosh, M. H. Imtiaz, and E. Sazonov. 2023. Ear canal pressure sensor for food intake detection. Frontiers in Electronics 4:1173607.
Hussar, B., J. Zhang, S. Hein, K. Wang, A. Robers, J. Cui, M. Smith, F. Bullock Mann, A. Barmer, and R. Dilig. 2020. The condition of education 2020. Washington, DC: National Center for Education Statistics.
Jones, J. W., J. M. Antle, B. Basso, K. J. Boote, R. T. Conant, I. Foster, H. C. J. Godfray, M. Herrero, R. E. Howitt, S. Janssen, B. A. Keating, R. Munoz-Carpena, C. H. Porter, C. Rosenzweig, and T. R. Wheeler. 2017. Toward a new generation of agricultural system data, models, and knowledge products: State of agricultural systems science. Agricultural Systems 155:269–288.
Kalantarian, H., N. Alshurafa, T. Le, and M. Sarrafzadeh. 2015. Monitoring eating habits using a piezoelectric sensor-based necklace. Computers in Biology and Medicine 58:46–55.
Kandori, A., T. Yamamoto, Y. Sano, M. Oonuma, T. Miyashita, M. Murata, and S. Sakoda. 2012. Simple magnetic swallowing detection system. IEEE Sensors Journal 12(4):805–811.
Kelly, P., S. J. Marshall, H. Badland, J. Kerr, M. Oliver, A. R. Doherty, and C. Foster. 2013. An ethical framework for automated, wearable cameras in health behavior research. American Journal of Preventive Medicine 44(3):314–319.
Kirk, D., E. Kok, M. Tufano, B. Tekinerdogan, E. J. M. Feskens, and G. Camps. 2022. Machine learning in nutrition research. Advances in Nutrition 13(6):2573–2589.
Knights, D., L. W. Parfrey, J. Zaneveld, C. Lozupone, and R. Knight. 2011. Human-associated microbial signatures: Examining their predictive value. Cell Host & Microbe 10(4):292–296.
Kumanyika, S. K., M. C. Whitt-Glover, T. L. Gary, T. E. Prewitt, A. M. Odoms-Young, J. Banks-Wallace, B. M. Beech, C. H. Halbert, N. Karanja, K. J. Lancaster, and C. D. Samuel-Hodge. 2007. Expanding the obesity research paradigm to reach African American communities. Preventing Chronic Disease 4(4):A112.
Lee, B. Y., J. M. Ordovás, E. J. Parks, C. A. M. Anderson, A. L. Barabási, S. K. Clinton, K. de la Haye, V. B. Duffy, P. W. Franks, E. M. Ginexi, K. J. Hammond, E. C. Hanlon, M. Hittle, E. Ho, A. L. Horn, R. S. Isaacson, P. L. Mabry, S. Malone, C. K. Martin, J. Mattei, S. N. Meydani, L. M. Nelson, M. L. Neuhouser, B. Parent, N. P. Pronk, H. M. Roche, S. Saria, F. Scheer, E. Segal, M. A. Sevick, T. D. Spector, L. Van Horn, K. A. Varady, V. S. Voruganti, and M. F. Martinez. 2022. Research gaps and opportunities in precision nutrition: An NIH workshop report. American Journal of Clinical Nutrition 116(6):1877–1900.
Lee, T., E. Puyol-Antón, B. Ruijsink, K. Aitcheson, M. Shi, and A. P. King. 2023. An investigation into the impact of deep learning model choice on sex and race bias in cardiac MR segmentation. In Clinical image-based procedures, fairness of AI in medical imaging, and ethical and philosophical issues in medical imaging, edited by S. Wesarg. Switzerland: Springer Nature. Pp. 215–224.
Li, H., K. Bera, H. Gilmore, N. E. Davidson, L. J. Goldstein, and A. Madabhushi. 2020. Abstract p5-06-16: Histomorphometric measure of disorder of collagen fiber orientation is associated with risk of recurrence in ER+ breast cancers in ECOG-ACRIN e2197 and TCGA-BRCA. Cancer Research 80(4_Supplement):P5-06-16.
Li, H., K. Bera, P. Toro, P. Fu, V. Rao, S. Siddique, A. Harbhajanka, H. Sechrist, Z. Zhang, S. Desai, V. Parmar, and A. Madabhushi. 2021a. Computerized image analysis of nuclear morphological features reveals differences in phenotype and prognosis of disease free survival of early stage ER+ breast cancers for South Asian and North American women. Cancer Research 81(4_Supplement):PS4-45.
Li, H., K. Bera, P. Toro, P. Fu, Z. Zhang, C. Lu, M. Feldman, S. Ganesan, L. J. Goldstein, N. E. Davidson, A. Glasgow, A. Harbhajanka, H. Gilmore, and A. Madabhushi. 2021b. Collagen fiber orientation disorder from H&E images is prognostic for early stage breast cancer: Clinical trial validation. NPJ Breast Cancer 7(1):104.
Li, Y. Y., R. Ghanbari, W. Pathmasiri, S. McRitchie, H. Poustchi, A. Shayanrad, G. Roshandel, A. Etemadi, J. D. Pollock, R. Malekzadeh, and S. C. J. Sumner. 2020. Untargeted metabolomics: Biochemical perturbations in Golestan cohort study opium users inform intervention strategies. Frontiers in Nutrition 7:584585.
Liu, J., E. Johns, L. Atallah, C. Pettitt, B. Lo, G. Frost, and G. Z. Yang. 2012. An intelligent food-intake monitoring system using wearable sensors. Paper read at 2012 Ninth International Conference on Wearable and Implantable Body Sensor Networks, May 9–12, 2012. London, UK.
Lo, F. P. W., Y. Sun, J. Qiu, and B. Lo. 2020. Image-based food classification and volume estimation for dietary assessment: A review. IEEE Journal of Biomedical and Health Informatics 24(7):1926–1939.
Loeser, R. F., L. Arbeeva, K. Kelley, A. A. Fodor, S. Sun, V. Ulici, L. Longobardi, Y. Cui, D. A. Stewart, S. J. Sumner, M. A. Azcarate-Peril, R. B. Sartor, I. M. Carroll, J. B. Renner, J. M. Jordan, and A. E. Nelson. 2022. Association of increased serum lipopolysaccharide, but not microbial dysbiosis, with obesity-related osteoarthritis. Arthritis and Rheumatology 74(2):227–236.
Loos, R. J. 2012. Genetic determinants of common obesity and their value in prediction. Best Practice & Research: Clinical Endocrinology & Metabolism 26(2):211–226.
Lorenzo, R., N. Voigt, K. Schetelig, A. Zawadzki, I. Welpe, and P. Brosi. 2017. The mix that matters: Innovation through diversity. Boston, MA: Boston Consulting Group.
Lu, C., H. Xu, J. Xu, H. Gilmore, M. Mandal, and A. Madabhushi. 2016. Multi-pass adaptive voting for nuclei detection in histopathological images. Scientific Reports 6:33985.
Makeyev, O., P. Lopez-Meyer, S. Schuckers, W. Besio, and E. Sazonov. 2012. Automatic food intake detection based on swallowing sounds. Biomedical Signal Processing and Control 7(6):649–656.
Marin, J., A. Biswas, F. Ofli, N. Hynes, A. Salvador, Y. Aytar, I. Weber, and A. Torralba. 2021. Recipe1M+: A dataset for learning cross-modal embeddings for cooking recipes and food images. IEEE Transactions on Pattern Analysis and Machine Intelligence 43(1):187–203.
Martin, C. K., H. Han, S. M. Coulon, H. R. Allen, C. M. Champagne, and S. D. Anton. 2009. A novel method to remotely measure food intake of free-living individuals in real time: The remote food photography method. British Journal of Nutrition 101(3):446–456.
Martin, S. L., M. I. Cardel, T. L. Carson, J. O. Hill, T. Stanley, S. Grinspoon, F. Steger, L. T. Blackman Carr, M. Ashby-Thompson, D. Stewart, J. Ard, and F. C. Stanford. 2023. Increasing diversity, equity, and inclusion in the fields of nutrition and obesity: A roadmap to equity in academia. American Journal of Clinical Nutrition 117(4):659–671.
McClung, H. L., H. A. Raynor, S. L. Volpe, J. T. Dwyer, and C. Papoutsakis. 2022. A primer for the evaluation and integration of dietary intake and physical activity digital measurement tools into nutrition and dietetics practice. Journal of the Academy of Nutrition and Dietetics 122(1):207–218.
McDonald, D., Y. Jiang, M. Balaban, K. Cantrell, Q. Zhu, A. Gonzalez, J. T. Morton, G. Nicolaou, D. H. Parks, S. M. Karst, M. Albertsen, P. Hugenholtz, T. DeSantis, S. J. Song, A. Bartko, A. S. Havulinna, P. Jousilahti, S. Cheng, M. Inouye, T. Niiranen, M. Jain, V. Salomaa, L. Lahti, S. Mirarab, and R. Knight. 2023. Greengenes2 unifies microbial data in a single reference tree. Nature Biotechnology. https://doi.org/10.1038/s41587-023-01845-1.
Mentzer, J. T., W. DeWitt, J. S. Keebler, S. Min, N. W. Nix, C. D. Smith, and Z. G. Zacharia. 2001. Defining supply chain management. Journal of Business Logistics 22(2):1–25.
Metcalf, H., D. Russell, and C. Hill. 2018. Broadening the science of broadening participation in STEM through critical mixed methodologies and intersectionality frameworks. American Behavioral Scientist 62:000276421876887.
Mitsuyama, Y., T. Matsumoto, H. Tatekawa, S. L. Walston, T. Kimura, A. Yamamoto, T. Watanabe, Y. Miki, and D. Ueda. 2023. Chest radiography as a biomarker of ageing: Artificial intelligence–based, multi-institutional model development and validation in Japan. Lancet Healthy Longevity 4(9):e478–e486.
Modanwal, G., J. R. Walker, S. Al-Kindi, S. Rajagopalan, and A. Madabhushi. 2020. Abstract 16796: Machine learning-based hepatic fat assessment in low-dose coronary artery calcium scans is correlated with human reader assessment. Circulation 142(Suppl_3):A16796.
Müller, M. J., and A. Bosy-Westphal. 2020. From a “metabolomics fashion” to a sound application of metabolomics in research on human nutrition. European Journal of Clinical Nutrition 74(12):1619–1629.
Nirschl, J. J., A. Janowczyk, E. G. Peyster, R. Frank, K. B. Margulies, M. D. Feldman, and A. Madabhushi. 2018. A deep-learning classifier identifies patients with clinical heart failure using whole-slide images of H&E tissue. PloS One 13(4):e0192726.
Noriega, M., J. Larco, C. Antonini, and C. Mejia. 2021. Market size and direct accessibility as mediators for explaining potato prices. Paper read at MIT SCALE Latin America Conference for Latin America & the Caribbean. https://scale.mit.edu/sites/scale.mit.edu/files/MIT-SCALE-Latin-America-Caribbean-2020-2021-Proceedings-Abstracts.pdf (accessed December 26, 2023).
Oakden-Rayner, L., J. Dunnmon, G. Carneiro, and C. Ré. 2020. Hidden stratification causes clinically meaningful failures in machine learning for medical imaging. Proceedings of the ACM Conference on Health Inference and Learning 2020:151–159.
Obermeyer, Z., B. Powers, C. Vogeli, and S. Mullainathan. 2019. Dissecting racial bias in an algorithm used to manage the health of populations. Science 366(6464):447–453.
OECD (Organisation for Economic Co-operation and Development). 2019. The heavy burden of obesity: The economics of prevention. Paris: OECD Publishing. https://doi.org/10.1787/67450d67-en (accessed January 9, 2024).
Our World in Data. 2023. Change in cereal production, yield, land use and population, world. https://ourworldindata.org/grapher/index-of-cereal-production-yield-and-land-use (accessed January 9, 2023).
Pallmann, P., A. W. Bedding, B. Choodari-Oskooei, M. Dimairo, L. Flight, L. V. Hampson, J. Holmes, A. P. Mander, L. Odondi, M. R. Sydes, S. S. Villar, J. M. S. Wason, C. J. Weir, G. M. Wheeler, C. Yap, and T. Jaki. 2018. Adaptive designs in clinical trials: Why use them, and how to run and report them. BMC Medicine 16(1):29.
Pardey, P. G., and J. M. Alston. 2021. Unpacking the agricultural black box: The rise and fall of American farm productivity growth. The Journal of Economic History 81(1):114-155.
Päßler, S., M. Wolff, and W. J. Fischer. 2012. Food intake monitoring: An acoustical approach to automated food intake activity detection and classification of consumed food. Physiological Measurement 33(6):1073–1093.
Perry, A. M., M. Steinbaum, and C. Romer. 2021. Student loans, the racial wealth divide, and why we need full student debt cancellation. Washington, DC: Brookings Institute.
Pyrros, A., J. M. Rodríguez-Fernández, S. M. Borstelmann, J. W. Gichoya, J. M. Horowitz, B. Fornelli, N. Siddiqui, Y. Velichko, O. Koyejo Sanmi, and W. Galanter. 2022. Detecting racial/ethnic health disparities using deep learning from frontal chest radiography. Journal of the American College of Radiology 19(1 Pt B):184–191.
Pyrros, A., S. M. Borstelmann, R. Mantravadi, Z. Zaiman, K. Thomas, B. Price, E. Greenstein, N. Siddiqui, M. Willis, I. Shulhan, J. Hines-Shah, J. M. Horowitz, P. Nikolaidis, M. P. Lungren, J. M. Rodríguez-Fernández, J. W. Gichoya, S. Koyejo, A. E. Flanders, N. Khandwala, A. Gupta, J. W. Garrett, J. P. Cohen, B. T. Layden, P. J. Pickhardt, and W. Galanter. 2023. Opportunistic detection of type 2 diabetes using deep learning from frontal chest radiographs. Nature Communications 14(1):4039.
Qiu, J., F. P.-W. Lo, X. Gu, M. L. Jobarteh, W. Jia, T. Baranowski, M. Steiner-Asiedu, A. K. Anderson, M. A. McCrory, and E. Sazonov. 2023. Egocentric image captioning for privacy-preserved passive dietary intake monitoring. IEEE Transactions on Cybernetics. https://doi.org/10.1109/TCYB.2023.3243999.
Quinn, R. A., A. V. Melnik, A. Vrbanac, T. Fu, K. A. Patras, M. P. Christy, Z. Bodai, P. Belda-Ferre, A. Tripathi, L. K. Chung, M. Downes, R. D. Welch, M. Quinn, G. Humphrey, M. Panitchpakdi, K. C. Weldon, A. Aksenov, R. da Silva, J. Avila-Pacheco, C. Clish, S. Bae, H. Mallick, E. A. Franzosa, J. Lloyd-Price, R. Bussell, T. Thron, A. T. Nelson, M. Wang, E. Leszczynski, F. Vargas, J. M. Gauglitz, M. J. Meehan, E. Gentry, T. D. Arthur, A. C. Komor, O. Poulsen, B. S. Boland, J. T. Chang, W. J. Sandborn, M. Lim, N. Garg, J. C. Lumeng, R. J. Xavier, B. I. Kazmierczak, R. Jain, M. Egan, K. E. Rhee, D. Ferguson, M. Raffatellu, H. Vlamakis, G. G. Haddad, D. Siegel, C. Huttenhower, S. K. Mazmanian, R. M. Evans, V. Nizet, R. Knight, and P. C. Dorrestein. 2020. Global chemical effects of the microbiome include new bile-acid conjugations. Nature 579(7797):123–129.
Raghu, V. K., J. Weiss, U. Hoffmann, H. J. W. L. Aerts, and M. T. Lu. 2021. Deep learning to estimate biological age from chest radiographs. JACC: Cardiovascular Imaging 14(11):2226–2236.
Reedy, J., A. F. Subar, S. M. George, and S. M. Krebs-Smith. 2018. Extending methods in dietary patterns research. Nutrients 10(5).
Ribeiro, M. T., S. Singh, and C. Guestrin. 2016. “Why should I trust you?” Explaining the predictions of any classifier. Paper read at Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data mining. https://dl.acm.org/doi/10.1145/2939672.2939778 (accessed December 21, 2023).
Ritchie, H., P. Rosado, and M. Roser. 2022. Environmental impacts of food production. https://ourworldindata.org/environmental-impacts-of-food (accessed December 1, 2023).
Rock, D., and H. Grant. 2016. Why diverse teams are smarter. Harvard Business Review. November 4. https://hbr.org/2016/11/why-diverse-teams-are-smarter (accessed February 22, 2024).
Rudin, C. 2019. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Inteligence 1(5):206–215.
Rushing, B. R., S. McRitchie, L. Arbeeva, A. E. Nelson, M. A. Azcarate-Peril, Y. Y. Li, Y. Qian, W. Pathmasiri, S. C. J. Sumner, and R. F. Loeser. 2022. Fecal metabolomics reveals products of dysregulated proteolysis and altered microbial metabolism in obesity-related osteoarthritis. Osteoarthritis and Cartilage 30(1):81–91.
Russell, B. J., S. D. Brown, N. Siguenza, I. Mai, A. R. Saran, A. Lingaraju, E. S. Maissy, A. C. Dantas Machado, A. F. M. Pinto, C. Sanchez, L. A. Rossitto, Y. Miyamoto, R. A. Richter, S. B. Ho, L. Eckmann, J. Hasty, D. J. Gonzalez, A. Saghatelian, R. Knight, and A. Zarrinpar. 2022. Intestinal transgene delivery with native E. coli chassis allows persistent physiological changes. Cell 185(17):3263–3277.
Sabry, F., T. Eltaras, W. Labda, K. Alzoubi, and Q. Malluhi. 2022. Machine learning for healthcare wearable devices: The big picture. Journal of Healthcare Engineering 2022:4653923.
Saleiro, P., B. Kuester, L. Hinkson, J. London, A. Stevens, A. Anisfeld, K. T. Rodolfa, and R. Ghani. 2018. Aequitas: A bias and fairness audit toolkit. arXiv preprint arXiv:1811.05577. https://arxiv.org/abs/1811.05577 (accessed January 9, 2024).
Sanches, L. M., and C. Mejía Argueta. 2019. Rethinking fresh food supply chains. White Paper. MIT Center for Transportation and Logistics.
Sazonov, E. S., and J. M. Fontana. 2012. A sensor system for automatic detection of food intake through non-invasive monitoring of chewing. IEEE Sensors Journal 12(5):1340–1348.
Schwedhelm, C., L. M. Lipsky, G. E. Shearrer, G. M. Betts, A. Liu, K. Iqbal, M. S. Faith, and T. R. Nansel. 2021. Using food network analysis to understand meal patterns in pregnant women with high and low diet quality. International Journal of Behavioral Nutrition and Physical Activity 18(1):101.
Seyyed-Kalantari, L., H. Zhang, M. B. McDermott, I. Y. Chen, and M. Ghassemi. 2021. Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations. Nature Medicine 27(12):2176–2182.
Shah, N., G. Srivastava, D. W. Savage, and V. Mago. 2019. Assessing Canadians health activity and nutritional habits through social media. Frontiers in Public Health 7:400.
Snoek, H. M., L. M. T. Eijssen, M. Geurts, C. Vors, K. A. Brown, M.-J. Bogaardt, R. A. M. Dhonukshe-Rutten, C. T. Evelo, L. K. Fezeu, P. M. Finglas, M. Laville, M. Ocké, G. Perozzi, K. Poppe, N. Slimani, I. Tetens, L. Timotijevic, K. Zimmermann, and P. van ’t Veer. 2018. Advancing food, nutrition, and health research in Europe by connecting and building research infrastructures in a DISH-RI: Results of the Eurodish project. Trends in Food Science & Technology 73:58–66.
Sohn, J. H., Y. Chen, D. Lituiev, J. Yang, K. Ordovas, D. Hadley, T. H. Vu, B. L. Franc, and Y. Seo. 2022. Prediction of future healthcare expenses of patients from chest radiographs using deep learning: A pilot study. Scientific Reports 12(1):8344.
Sørensen, C. G., S. Fountas, E. Nash, L. Pesonen, D. Bochtis, S. M. Pedersen, B. Basso, and S. B. Blackmore. 2010. Conceptual model of a future farm management information system. Computers and Electronics in Agriculture 72(1):37–47.
Spencer, C. N., J. L. McQuade, V. Gopalakrishnan, J. A. McCulloch, M. Vetizou, A. P. Cogdill, M. A. W. Khan, X. Zhang, M. G. White, C. B. Peterson, M. C. Wong, G. Morad, T. Rodgers, J. H. Badger, B. A. Helmink, M. C. Andrews, R. R. Rodrigues, A. Morgun, Y. S. Kim, J. Roszik, K. L. Hoffman, J. Zheng, Y. Zhou, Y. B. Medik, L. M. Kahn, S. Johnson, C. W. Hudgens, K. Wani, P. O. Gaudreau, A. L. Harris, M. A. Jamal, E. N. Baruch, E. PerezGuijarro, C. P. Day, G. Merlino, B. Pazdrak, B. S. Lochmann, R. A. Szczepaniak-Sloane, R. Arora, J. Anderson, C. M. Zobniw, E. Posada, E. Sirmans, J. Simon, L. E. Haydu, E. M. Burton, L. Wang, M. Dang, K. Clise-Dwyer, S. Schneider, T. Chapman, N. A. S. Anang, S. Duncan, J. Toker, J. C. Malke, I. C. Glitza, R. N. Amaria, H. A. Tawbi, A. Diab, M. K. Wong, S. P. Patel, S. E. Woodman, M. A. Davies, M. I. Ross, J. E. Gershenwald, J. E. Lee, P. Hwu, V. Jensen, Y. Samuels, R. Straussman, N. J. Ajami, K. C. Nelson, L. Nezi, J. F. Petrosino, P. A. Futreal, A. J. Lazar, J. Hu, R. R. Jenq, M. T. Tetzlaff, Y. Yan, W. S. Garrett,
C. Huttenhower, P. Sharma, S. S. Watowich, J. P. Allison, L. Cohen, G. Trinchieri, C. R. Daniel, and J. A. Wargo. 2021. Dietary fiber and probiotics influence the gut microbiome and melanoma immunotherapy response. Science 374(6575):1632–1640.
Sumner, S. C. J., S. McRitchie, and W. Pathmasiri. 2020. Chapter 10—Metabolomics for biomarker discovery and to derive genetic links to disease. In Principles of nutrigenetics and nutrigenomics, edited by R. D. E. Caterina, J. A. Martinez and M. Kohlmeier. Academic Press. Pp. 75–79.
Sun, M., L. E. Burke, Z. H. Mao, Y. Chen, H. C. Chen, Y. Bai, Y. Li, C. Li, and W. Jia. 2014. Ebutton: A wearable computer for health monitoring and personal assistance. Proceedings of the 37th Annual Design Automation Conference 2014:1–6.
Ten Hagen, K. G., C. Wolinetz, J. A. Clayton, and M. A. Bernard. 2022. Community voices: NIH working toward inclusive excellence by promoting and supporting women in science. Nature Communications 13(1):1682.
Topol, E. J. 2014. Individualized medicine from prewomb to tomb. Cell 157(1):241–253.
Verma, M., R. Hontecillas, N. Tubau-Juni, V. Abedi, and J. Bassaganya-Riera. 2018. Challenges in personalized nutrition and health. Frontiers in Nutrition 5.
Vrbanac, A., K. A. Patras, A. K. Jarmusch, R. H. Mills, S. R. Shing, R. A. Quinn, F. Vargas, D. J. Gonzalez, P. C. Dorrestein, R. Knight, and V. Nizet. 2020. Evaluating organism-wide changes in the metabolome and microbiome following a single dose of antibiotic. mSystems 5(5).
Wegge, J., F. Jungmann, S. Liebermann, M. Shemla, B. C. Ries, S. Diestel, and K. H. Schmidt. 2012. What makes age diverse teams effective? Results from a six-year research program. Work 41 (Suppl 1):5145–5151.
Williams, D., and G. Shipley. 2021. Enhancing artificial intelligence with indigenous wisdom. Open Journal of Philosophy 11:43–58.
Wu, E., K. Wu, R. Daneshjou, D. Ouyang, D. E. Ho, and J. Zou. 2021a. How medical AI devices are evaluated: Limitations and recommendations from an analysis of FDA approvals. Nature Medicine 27(4):582–584.
Wu, X., X. Fu, Y. Liu, E.-P. Lim, S. C. Hoi, and Q. Sun. 2021b. A large-scale benchmark for food image segmentation. Paper read at Proceedings of the 29th ACM International Conference on Multimedia. Chengdu, China.
Xu, J., L. Xiang, Q. Liu, H. Gilmore, J. Wu, J. Tang, and A. Madabhushi. 2016. Stacked sparse autoencoder (SSAE) for nuclei detection on breast cancer histopathology images. IEEE Transactions on Medical Imaging 35(1):119–130.
Yang, X., A. Doulah, M. Farooq, J. Parton, M. A. McCrory, J. A. Higgins, and E. Sazonov. 2019. Statistical models for meal-level estimation of mass and energy intake using features derived from video observation and a chewing sensor. Scientific Reports 9(1):45.
Yang, Y., T. Y. Tian, T. K. Woodruff, B. F. Jones, and B. Uzzi. 2022. Gender-diverse teams produce more novel and higher-impact scientific ideas. Proceedings of the National Academy of Sciences of the United States of America 119(36):e2200841119.
Yatsunenko, T., F. E. Rey, M. J. Manary, I. Trehan, M. G. Dominguez-Bello, M. Contreras, M. Magris, G. Hidalgo, R. N. Baldassano, A. P. Anokhin, A. C. Heath, B. Warner, J. Reeder, J. Kuczynski, J. G. Caporaso, C. A. Lozupone, C. Lauber, J. C. Clemente, D. Knights, R. Knight, and J. I. Gordon. 2012. Human gut microbiome viewed across age and geography. Nature 486(7402):222–227.
Yu, Z., A. De Vries, Y. Ampatzidis, and D. D. Sokol. 2021. Who owns and controls farming data. AE564/AE564 10(5):2021.
Zeevi, D., T. Korem, N. Zmora, D. Israeli, D. Rotshchild, A. Weinberger, O. Ben-Yacov, D. Lador, T. Avnit-Sagi, M. Lotan-Pompan, J. Suez, J. Mahdi, E. Matot, G. Malka, N. Kosower, M. Rein, G. Zilberman-Schapira, L. Dohnalová, M. Pevsner-Fischer, and E. Segal. 2015. Personalized nutrition by prediction of glycemic responses. Cell 163:1079–1094.
Zhang, X., H. Dou, T. Ju, J. Xu, and S. Zhang. 2016. Fusing heterogeneous features from stacked sparse autoencoder for histopathological image analysis. IEEE Journal of Biomedical and Health Informatics 20(5):1377–1383.
Zuffa, S., R. Schmid, A. Bauermeister, P. W. P. Gomes, A. M. Caraballo-Rodriguez, Y. El Abiead, A. T. Aron, E. C. Gentry, J. Zemlin, M. J. Meehan, N. E. Avalon, R. H. Cichewicz, E. Buzun, M. C. Terrazas, C.-Y. Hsu, R. Oles, A. V. Ayala, J. Zhao, H. Chu, M. C. M. Kuijpers, S. L. Jackrel, F. Tugizimana, L. P. Nephali, I. A. Dubery, N. E. Madala, E. A. Moreira, L. V. Costa-Lotufo, N. P. Lopes, P. Rezende-Teixeira, P. C. Jimenez, B. Rimal, A. D. Patterson, M. F. Traxler, R. de Cassia Pessotti, D. Alvarado-Villalobos, G. TamayoCastillo, P. Chaverri, E. Escudero-Leyva, L.-M. Quiros-Guerrero, A. J. Bory, J. Joubert, A. Rutz, J.-L. Wolfender, P.-M. Allard, A. Sichert, S. Pontrelli, B. S. Pullman, N. Bandeira, W. H. Gerwick, K. Gindro, J. Massana-Codina, B. C. Wagner, K. Forchhammer, D. Petras, N. Aiosa, N. Garg, M. Liebeke, P. Bourceau, K. B. Kang, H. Gadhavi, L. P. S. de Carvalho, M. S. dos Santos, A. I. Pérez-Lorente, C. Molina-Santiago, D. Romero, R. Franke, M. Brönstrup, A. V. P. de León, P. B. Pope, S. L. La Rosa, G. La Barbera, H. M. Roager, M. F. Laursen, F. Hammerle, B. Siewert, U. Peintner, C. Licona-Cassani, L. Rodriguez-Orduña, E. Rampler, F. Hildebrand, G. Koellensperger, H. Schoeny, K. Hohenwallner, L. Panzenboeck, R. Gregor, E. C. O’Neill, E. T. Roxborough, J. Odoi, N. J. Bale, S. Ding, J. S. S. Damsté, X. L. Guan, J. J. Cui, K.-S. Ju, D. B. Silva, F. M. R. Silva, G. F. da Silva, H. H. F. Koolen, C. Grundmann, J. A. Clement, H. Mohimani, K. Broders, K. L. McPhail, S. E. Ober-Singleton, C. M. Rath, D. McDonald, R. Knight, M. Wang, and P. C. Dorrestein. 2023. A taxonomically-informed mass spectrometry search tool for microbial metabolomics data. bioRxiv 2023.07.20.549584. https://doi.org/10.1101/2023.07.20.549584.