Artificial intelligence & clinical nutrition: What the future might have in store

Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer

Ehteshami Bejnordi, B., Veta, M., Johannes van Diest, P., van Ginneken, B., Karssemeijer, N., Litjens, G., van der Laak, J. A. W. M., and the CAMELYON16 Consortium, Hermsen, M., Manson, Q. F., et al. (2017) · JAMA · doi:10.1001/jama.2017.14585 · Cited by 3,293 · see citing works

Artificial intelligence in drug discovery and development

Paul, D., Sanap, G., Shenoy, S., Kalyane, D., Kalia, K., & Tekade, R. K. (2021) · Drug Discovery Today · doi:10.1016/j.drudis.2020.10.010 · Cited by 1,695 · see citing works

Health Outcomes of Sarcopenia: A Systematic Review and Meta-Analysis

Beaudart, C., Zaaria, M., Pasleau, F., Reginster, J., & Bruyère, O. (2017) · PLoS ONE · doi:10.1371/journal.pone.0169548 · Cited by 1,275 · see citing works

ESPEN endorsed recommendations. Definition and classification of intestinal failure in adults

Pironi, L., Arends, J., Baxter, J. P., Bozzetti, F., Burgos, R., Cuerda, C., Forbes, A., Gabe, S. M., Gillanders, L. K., Holst, M., et al. (2014) · Clinical Nutrition · doi:10.1016/j.clnu.2014.08.017 · Cited by 691 · see citing works

Personalised nutrition and health

Ordovás, J. M., Ferguson, L. R., Tai, E. S., & Mathers, J. C. (2018) · BMJ · doi:10.1136/bmj.k2173 · Cited by 634 · see citing works

Digital pathology and artificial intelligence in translational medicine and clinical practice

Baxi, V., Edwards, R., Montalto, M., & Saha, S. (2022) · Modern Pathology · doi:10.1038/s41379-021-00919-2 · Cited by 592 · see citing works

Deep-Learning-Based Drug–Target Interaction Prediction

Wen, M., Zhang, Z., Niu, S., Sha, H., Yang, R., Yun, Y., & Lü, H. (2017) · Journal of Proteome Research · doi:10.1021/acs.jproteome.6b00618 · Cited by 583 · see citing works

Chatbot for health care and oncology applications using artificial intelligence and machine learning: systematic review

Xu, L., Sanders, L., Li, K., & Chow, J. C. L. (2021) · JMIR Cancer · doi:10.2196/27850 · Cited by 571 · see citing works

ESPEN guideline on home enteral nutrition

Bischoff, S. C., Austin, P. D., Boeykens, K., Chourdakis, M., Cuerda, C., Jonkers‐Schuitema, C. F., Lichota, M., Nyulasi, I. B., Schneider, S. M., Stanga, Z. G., et al. (2019) · Clinical Nutrition · doi:10.1016/j.clnu.2019.04.022 · Cited by 437 · see citing works

Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare

Goh, K. H., Wang, L., Yeow, A. Y. K., Poh, H., Li, K., Yeow, J. J. L., & Tan, G. Y. H. (2021) · Nature Communications · doi:10.1038/s41467-021-20910-4 · Cited by 415 · see citing works

The emerging clinical role of wearables: Factors for successful implementation in healthcare

Smuck, M., Odonkor, C. A., Wilt, J. K., Schmidt, N., & Swiernik, M. A. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00418-3 · Cited by 393 · see citing works

Perceptions of Artificial Intelligence Among Healthcare Staff: A Qualitative Survey Study

Castagno, S., & Khalifa, M. (2020) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2020.578983 · Cited by 304 · see citing works

Discrimination of Breast Cancer with Microcalcifications on Mammography by Deep Learning

Wang, J., Yang, X., Cai, H., Tan, W., Jin, C., & Li, L. (2016) · Scientific Reports · doi:10.1038/srep27327 · Cited by 291 · see citing works

Patterns of Use and Key Predictors for the Use of Wearable Health Care Devices by US Adults: Insights from a National Survey

Chandrasekaran, R., Katthula, V., & Moustakas, E. (2020) · Journal of Medical Internet Research · doi:10.2196/22443 · Cited by 291 · see citing works

A systematic review of artificial intelligence chatbots for promoting physical activity, healthy diet, and weight loss

Oh, Y. J., Zhang, J., Fang, M.-L., & Fukuoka, Y. (2021) · International Journal of Behavioral Nutrition and Physical Activity · doi:10.1186/s12966-021-01224-6 · Cited by 278 · see citing works

Precision nutrition: A systematic literature review

Kirk, D., Catal, C., & Tekinerdogan, B. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104365 · Cited by 238 · see citing works

A machine learning approach to multi-level ECG signal quality classification

Li, Q., Rajagopalan, C., & Clifford, G. D. (2014) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2014.09.002 · Cited by 230 · see citing works

When Chatbots Meet Patients: One-Year Prospective Study of Conversations Between Patients With Breast Cancer and a Chatbot

Chaix, B., Bibault, J.-E., Pienkowski, A., Delamon, G., Guillemassé, A., Nectoux, P., & Brouard, B. (2019) · JMIR Cancer · doi:10.2196/12856 · Cited by 227 · see citing works

Prognostic value of combined clinical and myocardial perfusion imaging data using machine learning

Betancur, J., Otaki, Y., Motwani, M., Fish, M., Lemley, M., Dey, D., Gransar, H., Tamarappoo, B., Germano, G., Sharir, T., et al. (2017) · JACC Cardiovascular Imaging · doi:10.1016/j.jcmg.2017.07.024 · Cited by 220 · see citing works

Evaluation of New Technology-Based Tools for Dietary Intake Assessment—An ILSI Europe Dietary Intake and Exposure Task Force Evaluation

Eldridge, A. L., Piernas, C., Illner, A., Gibney, M. J., Gurinović, M. A., de Vries, J. H. M., & Cade, J. E. (2018) · Nutrients · doi:10.3390/nu11010055 · Cited by 212 · see citing works

Deep Learning and Convolutional Neural Networks for Medical Image Computing

Lu, L., Zheng, Y., Carneiro, G., & Yang, L. (2017) · Advances in Computer Vision and Pattern Recognition · doi:10.1007/978-3-319-42999-1 · Cited by 208 · see citing works

A Chatbot Versus Physicians to Provide Information for Patients With Breast Cancer: Blind, Randomized Controlled Noninferiority Trial

Bibault, J., Chaix, B., Guillemassé, A., Cousin, S., Escande, A., Perrin, M., Pienkowski, A., Delamon, G., Nectoux, P., & Brouard, B. (2019) · Journal of Medical Internet Research · doi:10.2196/15787 · Cited by 198 · see citing works

Interpretable Machine Learning Framework Reveals Robust Gut Microbiome Features Associated With Type 2 Diabetes

Gou, W., Ling, C.-W., He, Y., Jiang, Z., Fu, Y., Xu, F., Miao, Z., Sun, T., Lin, J.-S., Zhu, H., et al. (2020) · Diabetes Care · doi:10.2337/dc20-1536 · Cited by 174 · see citing works

Wearable Food Intake Monitoring Technologies: A Comprehensive Review

Vu, T., Lin, F., Alshurafa, N. I., & Xu, W. (2017) · Computers · doi:10.3390/computers6010004 · Cited by 170 · see citing works

Ethical issues in using ambient intelligence in health-care settings

Martinez‐Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S., Wieten, S. E., Cho, M. K., Magnus, D. C., Fei-Fei, L., et al. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30275-2 · Cited by 165 · see citing works

Automatic Malignant and Benign Skin Cancer Classification Using a Hybrid Deep Learning Approach

Bassel, A., Abdulkareem, A. B., Alyasseri, Z. A. A., Sani, N. S., & Mohammed, H. J. (2022) · Diagnostics · doi:10.3390/diagnostics12102472 · Cited by 160 · see citing works

Molecular subtyping of cancer: current status and moving toward clinical applications

Zhao, L., Lee, V. H.-F., Ng, M. K., Yan, H., & Bijlsma, M. F. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bby026 · Cited by 150 · see citing works

Artificial intelligence, machine learning, and deep learning for clinical outcome prediction

Pettit, R. W., Fullem, R. L., Cheng, C., & Amos, C. I. (2021) · Emerging Topics in Life Sciences · doi:10.1042/etls20210246 · Cited by 137 · see citing works

Developing, implementing, and governing artificial intelligence in medicine: a step-by-step approach to prevent an artificial intelligence winter

van de Sande, D., Van Genderen, M. E., Smit, J. M., Huiskens, J., Visser, J. J., Veen, R. E. R., van Unen, E., BA, O. H., Gommers, D., & Bommel, J. V. (2022) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2021-100495 · Cited by 135 · see citing works

Bite Weight Prediction From Acoustic Recognition of Chewing

Amft, O., Kusserow, M., & Troster, G. (2009) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2009.2015873 · Cited by 123 · see citing works

Deep learning-based EEG analysis to classify normal, mild cognitive impairment, and dementia: Algorithms and dataset

Kim, M., Youn, Y. C., & Paik, J. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120054 · Cited by 109 · see citing works

A smartphone Chatbot application to optimize monitoring of older patients with cancer

Piau, A., Crissey, R., Bréchemier, D., Balardy, L., & Nourhashémi, F. (2019) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2019.05.013 · Cited by 101 · see citing works

Development and Validation of a Deep Learning Model for Predicting Treatment Response in Patients With Newly Diagnosed Epilepsy

Hakeem, H., Feng, W. S., Chen, Z. Z., Choong, J., Brodie, M. J., Fong, S., Lim, K. S., Wu, J., Wang, X., Lawn, N. D., et al. (2022) · JAMA Neurology · doi:10.1001/jamaneurol.2022.2514 · Cited by 100 · see citing works

Precision medicine in inflammatory bowel disease: concept, progress and challenges

Borg‐Bartolo, S., Boyapati, R. K., Satsangi, J., & Kalla, R. (2020) · F1000Research · doi:10.12688/f1000research.20928.1 · Cited by 90 · see citing works

Personalised medicine in Crohn's disease

Noor, N. M., Verstockt, B., Parkes, M., & Lee, J. C. (2019) · The Lancet. Gastroenterology & hepatology · doi:10.1016/s2468-1253(19)30340-1 · Cited by 83 · see citing works

Exploring Tunable Hyperparameters for Deep Neural Networks with Industrial ADME Data Sets

Zhou, Y., Cahya, S., Combs, S. A., Nicolaou, C. A., Wang, J., Desai, P. V., & Shen, J. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00671 · Cited by 78 · see citing works

Convolutional Neural Network Technology in Endoscopic Imaging: Artificial Intelligence for Endoscopy

Choi, J., Shin, K., Jung, J., Bae, H., Kim, D. H., Byeon, J.-S., & Kim, N. (2020) · Clinical Endoscopy · doi:10.5946/ce.2020.054 · Cited by 76 · see citing works

Legal concerns in health-related artificial intelligence: a scoping review protocol

Silva, M. D., Horsley, T., Singh, D. A., Silva, E. D., Ly, V., Thomas, B., Daniel, R. C., Chagal-Feferkorn, K., Iantomasi, S., White, K., et al. (2022) · Systematic Reviews · doi:10.1186/s13643-022-01939-y · Cited by 57 · see citing works

Assessment of Critical Feeding Tube Malpositions on Radiographs Using Deep Learning

Singh, V., Danda, V. R., Gorniak, R., Flanders, A. E., & Lakhani, P. C. (2019) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-019-00229-9 · Cited by 53 · see citing works

Use of artificial intelligence in the imaging of sarcopenia: A narrative review of current status and perspectives

Rozynek, M., Kucybała, I., Urbanik, A., & Wojciechowski, W. (2021) · Nutrition · doi:10.1016/j.nut.2021.111227 · Cited by 50 · see citing works

Relative Validation of an Artificial Intelligence–Enhanced, Image-Assisted Mobile App for Dietary Assessment in Adults: Randomized Crossover Study

Moyen, A., Rappaport, A. I., Fleurent-Grégoire, C., Tessier, A., Brazeau, A., & Chevalier, S. (2022) · Journal of Medical Internet Research · doi:10.2196/40449 · Cited by 41 · see citing works

Role of Artificial Intelligence in Video Capsule Endoscopy

Tziortziotis, I., Laskaratos, F., & Coda, S. (2021) · Diagnostics · doi:10.3390/diagnostics11071192 · Cited by 35 · see citing works

Prognostic artificial intelligence model to predict 5 year survival at 1 year after gastric cancer surgery based on nutrition and body morphometry

Chung, H., Ko, Y., Lee, I., et al. (2023) · Journal of Cachexia Sarcopenia and Muscle · doi:10.1002/jcsm.13176 · Cited by 34 · see citing works

Recent Advances in Melanoma Diagnosis and Prognosis Using Machine Learning Methods

Grossarth, S. N., Mosley, D. A., Madden, C., Ike, J., Smith, I. T., Huo, Y., & Wheless, L. (2023) · Current Oncology Reports · doi:10.1007/s11912-023-01407-3 · Cited by 33 · see citing works

Explainable machine learning model to predict refeeding hypophosphatemia

Choi, T. Y., Chang, M.-Y., Heo, S., & Jang, J. Y. (2021) · Clinical Nutrition ESPEN · doi:10.1016/j.clnesp.2021.08.022 · Cited by 31 · see citing works

The NMR added value to the green foodomics perspective: Advances by machine learning to the holistic view on food and nutrition

Picone, G., Mengucci, C., & Capozzi, F. (2022) · Magnetic Resonance in Chemistry · doi:10.1002/mrc.5257 · Cited by 31 · see citing works

Enhancing Multi-disease Diagnosis of Chest X-rays with Advanced Deep-learning Networks in Real-world Data

Chen, Y., Wan, Y., & Pan, F. (2023) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-023-00801-4 · Cited by 31 · see citing works

The future of telemedicine and wearable technology in IBD

Rowan, C. R., & Hirten, R. P. (2022) · Current Opinion in Gastroenterology · doi:10.1097/mog.0000000000000845 · Cited by 30 · see citing works

Machine Learning-Based Prediction of In-Hospital Complications in Elderly Patients Using GLIM-, SGA-, and ESPEN 2015-Diagnosed Malnutrition as a Factor

Ren, S.-S., Zhu, M., Zhang, K., Chen, B., Yang, C., Xiao, R., & Li, P.-G. (2022) · Nutrients · doi:10.3390/nu14153035 · Cited by 30 · see citing works

Clinical impact of artificial intelligence-based solutions on imaging of the pancreas and liver

Berbis, M. A., Paulano‐Godino, F., del Val, J. R., Mata, L. A., & Luna, A. (2023) · World Journal of Gastroenterology · doi:10.3748/wjg.v29.i9.1427 · Cited by 30 · see citing works

Survival and CT defined sarcopenia in patients with intestinal failure on home parenteral support

Oke, S. M., Rye, B., Malietzis, G. A., Baldwin‐Cleland, R., Bottle, A., Gabe, S. M., & Lung, P. F. C. (2019) · Clinical Nutrition · doi:10.1016/j.clnu.2019.03.015 · Cited by 29 · see citing works

Development of a Reinforcement Learning Algorithm to Optimize Corticosteroid Therapy in Critically Ill Patients with Sepsis

Bologheanu, R., Kapral, L., Laxar, D., Maleczek, M., Dibiasi, C., Zeiner, S., Agibetov, A., Ercole, A., Thoral, P., Elbers, P., et al. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12041513 · Cited by 27 · see citing works

Machine learning algorithms assist early evaluation of enteral nutrition in ICU patients

Wang, Y.-X., Li, X.-L., Zhang, L., Li, H., Liu, X.-M., Song, W., & Pang, X. (2023) · Frontiers in Nutrition · doi:10.3389/fnut.2023.1060398 · Cited by 26 · see citing works

Convolutional neural network-based segmentation network applied to image recognition of angiodysplasias lesion under capsule endoscopy

Chu, Y., Huang, F., Gao, M., et al. (2023) · World Journal of Gastroenterology · doi:10.3748/wjg.v29.i5.879 · Cited by 20 · see citing works

Five things every clinician should know about AI ethics in intensive care

Shaw, J., Sethi, N., & Block, B. L. (2020) · Intensive Care Medicine · doi:10.1007/s00134-020-06277-y · Cited by 19 · see citing works

Meta-analysis and machine learning-augmented mixed effects cohort analysis of improved diets among 5847 medical trainees, providers and patients

Monlezun, D. J., Carr, C., Niu, T., Nordio, F., DeValle, N., Sarris, L., & Harlan, T. (2021) · Public Health Nutrition · doi:10.1017/s1368980021002809 · Cited by 19 · see citing works

Performance and clinical impact of machine learning based lung nodule detection using vessel suppression in melanoma patients

Aissa, J., Schaarschmidt, B. M., Below, J., Bethge, O., Böven, J., Sawicki, L. M., Hoff, N., Kröpil, P., Antoch, G., & Boos, J. (2018) · Clinical Imaging · doi:10.1016/j.clinimag.2018.09.001 · Cited by 16 · see citing works

Artificial intelligence and inflammatory bowel disease: practicalities and future prospects

Brooks-Warburton, J., Ashton, J. J., Dhar, A., Tham, T., Allen, P. B., Hoque, S., Lovat, L. B., & Sebastian, S. (2021) · Frontline Gastroenterology · doi:10.1136/flgastro-2021-102003 · Cited by 16 · see citing works

Machine learning and synthetic outcome estimation for individualised antimicrobial cessation

Bolton, W., Rawson, T. M., Hernandez, B., Wilson, R. C., Antcliffe, D. B., Georgiou, P., & Holmes, A. H. (2022) · Frontiers in Digital Health · doi:10.3389/fdgth.2022.997219 · Cited by 15 · see citing works

The Use of Artificial Intelligence to Predict the Development of Atrial Fibrillation

Pipilas, D. C., Friedman, S. F., & Khurshid, S. S. (2023) · Current Cardiology Reports · doi:10.1007/s11886-023-01859-w · Cited by 14 · see citing works

British Intestinal Failure Alliance (BIFA) guidance - haematological and biochemical monitoring of adult patients receiving home parenteral nutrition

Mercer-Smith, G. W., Kirk, C., Gemmell, L., et al. (2021) · Frontline Gastroenterology · doi:10.1136/flgastro-2020-101758 · Cited by 13 · see citing works

A comparative study of two automated solutions for cross‐sectional skeletal muscle measurement from abdominal computed tomography images

Charrière, K., Boulouard, Q., Artemova, S., et al. (2023) · Medical Physics · doi:10.1002/mp.16261 · Cited by 13 · see citing works

The future of AI in critical care is augmented, not artificial, intelligence

Liu, V. X. (2020) · Critical Care · doi:10.1186/s13054-020-03404-5 · Cited by 12 · see citing works

Development of CNN models for the enteral feeding tube positioning assessment on a small scale data set

Liang, G., Ganesh, H. S., Steffe, D., et al. (2022) · BMC Medical Imaging · doi:10.1186/s12880-022-00766-w · Cited by 10 · see citing works

Hybrid model of compounded and multichamber bag parenteral nutrition for adults with chronic intestinal failure

Harrison, S., Kopczynska, M., Leahy, G., et al. (2022) · Journal of Parenteral and Enteral Nutrition · doi:10.1002/jpen.2406 · Cited by 9 · see citing works

Machine learning technologies in CT-based diagnostics and classification of intracranial hemorrhages

Сморчкова, А. К., Khoruzhaya, A. N., Kremneva, E. I., & Petryaikin, A. V. (2023) · Burdenko s Journal of Neurosurgery · doi:10.17116/neiro20238702185 · Cited by 4 · see citing works

Applications of Artificial Intelligence, Machine Learning, and Deep Learning in Nutrition: A Systematic Review

Theodore Armand, T. P., Nfor, K. A., Kim, J.-I., & Kim, H.-C. (2024) · Nutrients · doi:10.3390/nu16071073 · Cited by 253 · see citing works

The Role of Artificial Intelligence in Nutrition Research: A Scoping Review

Sosa-Holwerda, A., Park, O.-H., Albracht-Schulte, K., Niraula, S., Thompson, L., & Oldewage-Theron, W. (2024) · Nutrients · doi:10.3390/nu16132066 · Cited by 114 · see citing works

Beyond Calories: Individual Metabolic and Hormonal Adaptations Driving Variability in Weight Management—A State-of-the-Art Narrative Review

Theodorakis, N., Kreouzi, M., Pappas, A., & Nikolaou, M. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms252413438 · Cited by 43 · see citing works

PATTERN OF THERAPEUTIC INTERVENTIONS MADE BY CLINICAL PHARMACIST IN A SOUTH INDIAN HOSPITAL

Mohammed Saji, S., Raslif Khan, S., & Sundaran, S. (2013)

What's the difference? Using descriptors to classify the care provided to children and adolescents with behavioral and emotional problems

Evenboer, E., Huyghen, A.-M. N., Tuinstra, J., Knorth, E. J., & Reijneveld, S. A. (2016) · Children and Youth Services Review · doi:10.1016/j.childyouth.2016.01.010 · Cited by 6 · see citing works

Physical activity interventions in Hispanic American girls and women

Sharma, M. (2008) · Obesity Reviews · doi:10.1111/j.1467-789x.2008.00501.x · Cited by 23 · see citing works

Multilevel Interventions: Study Design and Analysis Issues

Cleary, P. D., Gross, C. P., Zaslavsky, A. M., & Taplin, S. H. (2012) · JNCI Monographs · doi:10.1093/jncimonographs/lgs010 · Cited by 81 · see citing works

Interventions to combat and prevent cyberbullying

Günther, N. (2016) · DSpace (Open University in the Netherlands)