ChatGPT as a Virtual Dietitian: Exploring Its Potential as a Tool for Improving Nutrition Knowledge
Abstract
:1. Introduction
- What are the features of nutrition applications that ChatGPT can or cannot emulate?
- How can ChatGPT assist throughout each stage of the Nutrition Care Process?
- Can ChatGPT effectively replace dietitians and nutrition professionals?
2. Background of the Study
2.1. Technological Innovations in the Healthcare Sector
2.2. Novel Digital Technologies for Personalized Nutrition
2.3. Virtual Dietitian: A Knowledge-Driven Nutrition System
2.4. Current Research on Nutrition and ChatGPT
3. ChatGPT as a Nutrition Tool
3.1. What Are the Features of Nutrition Applications That ChatGPT Can or Cannot Emulate?
3.1.1. Verified Nutrition Database
3.1.2. Personalized Meal Planner
3.1.3. Food Intake Tracker
3.1.4. Dietary Advice and Guidance
3.1.5. Community and Support
3.1.6. Assessment Tools and Calculators
3.1.7. Tips and Reminders
3.1.8. Nutrition Education Materials
3.2. How Can ChatGPT Assist throughout Each Stage of the Nutrition Care Process?
3.2.1. Nutrition Assessment
- Answer questions about specific nutrients, their functions, and dietary sources.
- Provide general information on dietary guidelines and recommendations.
- Assess individual nutrition needs based on factors such as age, sex, and activity level.
- Offer dietary assessment tools and calculators to estimate nutrient intake.
- Provide guidance on assessing body composition and interpreting results.
- Explain common nutrition-related health conditions or risk factors.
3.2.2. Nutrition Diagnosis
- Provide sample evidence-based nutrition diagnoses for common health conditions.
- Assist in identifying nutrition-related problems based on the assessment findings.
- Explain the significance of nutrition diagnoses in developing intervention plans.
- Help identify potential comorbidities that may influence the nutrition diagnosis.
- Offer guidance on documenting nutrition diagnoses in a standardized format.
- Assist in identifying nutrition-related risk factors for specific populations.
3.2.3. Nutrition Intervention
- Offer general dietary recommendations for various health conditions or goals.
- Recommend personalized meal plans based on specific dietary needs or preferences.
- Provide tips for modifying recipes to improve nutritional content.
- Suggest behavior change techniques that can support adherence to interventions.
- Give guidance on portion control and meal frequency for weight management.
- Suggest resources for finding nutritionally balanced recipes and meal ideas.
3.2.4. Nutrition Monitoring and Evaluation
- Provide guidance in setting measurable goals related to nutrition interventions.
- Offer tools for tracking food intake, physical activity, and other lifestyle factors.
- Assist in analyzing nutrition-related data collected during monitoring.
- Explain how to interpret changes in laboratory results related to interventions.
- Assist in identifying potential challenges or barriers to achieving nutrition goals.
- Offer resources for conducting follow-up assessments and adjusting interventions.
3.3. Can ChatGPT Effectively Replace Dietitians and Nutrition Professionals?
3.3.1. Incorrect Responses
3.3.2. Real-Time Monitoring and Feedback
3.3.3. Coordinated Nutrition Services
3.3.4. Hands-On Demonstration
3.3.5. Physical Examination
3.3.6. Verbal and Non-Verbal Cues
3.3.7. Emotional and Psychological Aspects
3.3.8. Wearable Device Integration
3.3.9. Data Visualization
3.3.10. Ethical and Privacy Concerns
4. Discussion, Implications, and Limitations
5. Conclusions
Funding
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Miller, L.M.S.; Cassady, D.L. The Effects of Nutrition Knowledge on Food Label Use. A Review of the Literature. Appetite 2015, 92, 207–216. [Google Scholar] [CrossRef] [PubMed]
- Spronk, I.; Kullen, C.; Burdon, C.; O’Connor, H. Relationship Between Nutrition Knowledge and Dietary Intake. Br. J. Nutr. 2014, 111, 1713–1726. [Google Scholar] [CrossRef] [PubMed]
- Carbonneau, E.; Lamarche, B.; Provencher, V.; Desroches, S.; Robitaille, J.; Vohl, M.-C.; Bégin, C.; Bélanger, M.; Couillard, C.; Pelletier, L.; et al. Associations Between Nutrition Knowledge and Overall Diet Quality: The Moderating Role of Sociodemographic Characteristics—Results From the PREDISE Study. Am. J. Health Promot. 2020, 35, 38–47. [Google Scholar] [CrossRef] [PubMed]
- D’Adamo, C.R.; McArdle, P.F.; Balick, L.; Peisach, E.; Ferguson, T.; Diehl, A.; Bustad, K.; Bowden, B.; Pierce, B.A.; Berman, B.M. Spice MyPlate: Nutrition Education Focusing Upon Spices and Herbs Improved Diet Quality and Attitudes Among Urban High School Students. Am. J. Health Promot. 2016, 30, 346–356. [Google Scholar] [CrossRef] [PubMed]
- Quaidoo, E.Y.; Ohemeng, A.; Amankwah-Poku, M. Sources of Nutrition Information and Level of Nutrition Knowledge Among Young Adults in the Accra Metropolis. BMC Public Health 2018, 18, 1323. [Google Scholar] [CrossRef]
- Sadegholvad, S.; Yeatman, H.; Parrish, A.M.; Worsley, A. Professionals’ Recommended Strategies to Improve Australian Adolescents’ Knowledge of Nutrition and Food Systems. Nutrients 2017, 9, 844. [Google Scholar] [CrossRef]
- Garcia, M.B.; Garcia, P.S. Intelligent Tutoring System as an Instructional Technology in Learning Basic Nutrition Concepts: An Exploratory Sequential Mixed Methods Study. In Handbook of Research on Instructional Technologies in Health Education and Allied Disciplines; IGI Global: Hershey, PA, USA, 2023; pp. 265–284. [Google Scholar] [CrossRef]
- Rosales, A.; Young, S.; Mendez, T.; Shelden, K.; Holdaway, M. Collaborative Strategies to Improve Nutrition Security and Education: Lessons Learned During a Pandemic. J. Sch. Health 2023, 93, 148–152. [Google Scholar] [CrossRef]
- Mitsui, T.; Yamamoto, S.; Endo, M. Science Electives in High School will Improve Nutrition Knowledge but not Enough to Make Accurate Decisions. Nutr. Res. Pract. 2023, 17, 803–811. [Google Scholar] [CrossRef]
- Vrkatić, A.; Grujičić, M.; Jovičić-Bata, J.; Novaković, B. Nutritional Knowledge, Confidence, Attitudes towards Nutritional Care and Nutrition Counselling Practice among General Practitioners. Healthcare 2022, 10, 2222. [Google Scholar] [CrossRef]
- Aggarwal, M.; Devries, S.; Freeman, A.M.; Ostfeld, R.; Gaggin, H.; Taub, P.; Rzeszut, A.K.; Allen, K.; Conti, R.C. The Deficit of Nutrition Education of Physicians. Am. J. Med. 2018, 131, 339–345. [Google Scholar] [CrossRef]
- Kołłajtis-Dołowy, A.; Żamojcin, K. The Level of Knowledge on Nutrition and its Relation to Health Among Polish young men. Rocz. Panstw. Zakl. Hig. 2016, 67, 155–161. [Google Scholar]
- Vilar-Compte, M.; Burrola-Méndez, S.; Lozano-Marrufo, A.; Ferré-Eguiluz, I.; Flores, D.; Gaitán-Rossi, P.; Teruel, G.; Pérez-Escamilla, R. Urban Poverty and Nutrition Challenges Associated With Accessibility to a Healthy Diet: A Global Systematic Literature Review. Int. J. Equity Health 2021, 20, 40. [Google Scholar] [CrossRef] [PubMed]
- Stewart-Knox, B.J.; Markovina, J.; Rankin, A.; Bunting, B.P.; Kuznesof, S.; Fischer, A.R.H.; van der Lans, I.A.; Poínhos, R.; de Almeida, M.D.V.; Panzone, L.; et al. Making Personalised Nutrition the Easy Choice: Creating Policies to Break Down the Barriers and Reap the Benefits. Food Policy 2016, 63, 134–144. [Google Scholar] [CrossRef]
- Garcia, M.B.; Mangaba, J.B.; Vinluan, A.A. Towards the Development of a Personalized Nutrition Knowledge-Based System: A Mixed-Methods Needs Analysis of Virtual Dietitian. Int. J. Sci. Technol. Res. 2020, 9, 2068–2075. [Google Scholar]
- Mikkelsen, B.E.; Engesveen, K.; Afflerbach, T.; Barnekow, V. The Human Rights Framework, the School and Healthier Eating Among Young People: A European Perspective. Public Health Nutr. 2016, 19, 15–25. [Google Scholar] [CrossRef] [PubMed]
- Bhawra, J.; Kirkpatrick, S.I.; Hall, M.G.; Vanderlee, L.; White, C.M.; Hammond, D. Patterns and Correlates of Nutrition Knowledge Across Five Countries in the 2018 International Food Policy Study. Nutr. J. 2023, 22, 19. [Google Scholar] [CrossRef]
- Salinari, A.; Machì, M.; Armas Diaz, Y.; Cianciosi, D.; Qi, Z.; Yang, B.; Ferreiro Cotorruelo, M.S.; Villar, S.G.; Dzul Lopez, L.A.; Battino, M.; et al. The Application of Digital Technologies and Artificial Intelligence in Healthcare: An Overview on Nutrition Assessment. Diseases 2023, 11, 97. [Google Scholar] [CrossRef] [PubMed]
- Iyanna, S.; Kaur, P.; Ractham, P.; Talwar, S.; Najmul Islam, A.K.M. Digital Transformation of Healthcare Sector. What is Impeding Adoption and Continued Usage of Technology-Driven Innovations by End-users? J. Bus. Res. 2022, 153, 150–161. [Google Scholar] [CrossRef]
- Tavares, D.; Lopes, A.I.; Castro, C.; Maia, G.; Leite, L.; Quintas, M. The Intersection of Artificial Intelligence, Telemedicine, and Neurophysiology: Opportunities and Challenges. In Handbook of Research on Instructional Technologies in Health Education and Allied Disciplines; IGI Global: Hershey, PA, USA, 2023; pp. 130–152. [Google Scholar] [CrossRef]
- Haleem, A.; Javaid, M.; Singh, R.P.; Suman, R.; Rab, S. Blockchain Technology Applications in Healthcare: An Overview. Int. J. Intell. Netw. 2021, 2, 130–139. [Google Scholar] [CrossRef]
- Maaliw, R.R.; Susa, J.A.B.; Alon, A.S.; Lagman, A.C.; Ambat, S.C.; Garcia, M.B.; Piad, K.C.; Raguro, M.C.F. A Deep Learning Approach for Automatic Scoliosis Cobb Angle Identification. In Proceedings of the 2022 IEEE World AI IoT Congress (AIIoT), Washington, DC, USA, 6–9 June 2022; pp. 111–117. [Google Scholar]
- Barua, R.; Sarkar, A.; Datta, S. Emerging Advancement of 3D Bioprinting Technology in Modern Medical Science and Vascular Tissue Engineering Education. In Handbook of Research on Instructional Technologies in Health Education and Allied Disciplines; IGI Global: Hershey, PA, USA, 2023; pp. 153–175. [Google Scholar] [CrossRef]
- Çaliş, H.T.; Cüce, İ.; Polat, E.; Hopcan, S.; Yaprak, E.; Karabaş, Ç.; Çelik, İ.; Demir, F.G.Ü. An Educational Mobile Health Application for Pulmonary Rehabilitation in Patients With Mild to Moderate COVID-19 Pneumonia. In Handbook of Research on Instructional Technologies in Health Education and Allied Disciplines; IGI Global: Hershey, PA, USA, 2023; pp. 220–242. [Google Scholar] [CrossRef]
- Solanki, R.K.; Rajawat, A.S.; Gadekar, A.R.; Patil, M.E. Building a Conversational Chatbot Using Machine Learning: Towards a More Intelligent Healthcare Application. In Handbook of Research on Instructional Technologies in Health Education and Allied Disciplines; IGI Global: Hershey, PA, USA, 2023; pp. 285–309. [Google Scholar] [CrossRef]
- Sheikh, A.; Anderson, M.; Albala, S.; Casadei, B.; Franklin, B.D.; Richards, M.; Taylor, D.; Tibble, H.; Mossialos, E. Health Information Technology and Digital Innovation for National Learning Health and Care Systems. Lancet Digit. Health 2021, 3, 383–396. [Google Scholar] [CrossRef]
- Abrahams, M.; Matusheski, N.V. Personalised Nutrition Technologies: A New Paradigm for Dietetic Practice and Training in a Digital Transformation Era. J. Hum. Nutr. Diet. 2020, 33, 295–298. [Google Scholar] [CrossRef] [PubMed]
- Paramastri, R.; Pratama, S.A.; Ho, D.K.N.; Purnamasari, S.D.; Mohammed, A.Z.; Galvin, C.J.; Hsu, Y.-H.E.; Tanweer, A.; Humayun, A.; Househ, M.; et al. Use of Mobile Applications to Improve Nutrition Behaviour: A Systematic Review. Comput. Methods Programs Biomed. 2020, 192, 105459. [Google Scholar] [CrossRef] [PubMed]
- Hauptmann, H.; Leipold, N.; Madenach, M.; Wintergerst, M.; Lurz, M.; Groh, G.; Böhm, M.; Gedrich, K.; Krcmar, H. Effects and Challenges of Using a Nutrition Assistance System: Results of a Long-Term Mixed-Method Study. User Model. User-Adapt. Interact. 2022, 32, 923–975. [Google Scholar] [CrossRef]
- Garcia, M.B.; Mangaba, J.B.; Tanchoco, C.C. Acceptability, Usability, and Quality of a Personalized Daily Meal Plan Recommender System: The Case of Virtual Dietitian. In Proceedings of the 2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), Manila, Philippines, 28–30 November 2021; pp. 1–6. [Google Scholar]
- Abhari, S.; Safdari, R.; Azadbakht, L.; Lankarani, K.B.; Niakan Kalhori, S.R.; Honarvar, B.; Abhari, K.; Ayyoubzadeh, S.M.; Karbasi, Z.; Zakerabasali, S.; et al. A Systematic Review of Nutrition Recommendation Systems: With Focus on Technical Aspects. J. Biomed. Phys. Eng. 2019, 9, 591–602. [Google Scholar] [CrossRef]
- Herbert, J.; Schumacher, T.; Brown, L.J.; Clarke, E.D.; Collins, C.E. Delivery of Telehealth Nutrition and Physical Activity Interventions to Adults Living in Rural Areas: A Scoping Review. Int. J. Behav. Nutr. Phys. Act. 2023, 20, 110. [Google Scholar] [CrossRef]
- Garcia, M.B.; Mangaba, J.B.; Tanchoco, C.C. Virtual Dietitian: A Nutrition Knowledge-Based System Using Forward Chaining Algorithm. In Proceedings of the 2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT), Online, 29–30 September 2021; pp. 309–314. [Google Scholar]
- Toledo, R.Y.; Alzahrani, A.A.; Martínez, L. A Food Recommender System Considering Nutritional Information and User Preferences. IEEE Access 2019, 7, 96695–96711. [Google Scholar] [CrossRef]
- Rostami, M.; Farrahi, V.; Ahmadian, S.; Mohammad Jafar Jalali, S.; Oussalah, M. A Novel Healthy and Time-Aware Food Recommender System Using Attributed Community Detection. Expert Syst. Appl. 2023, 221, 119719. [Google Scholar] [CrossRef]
- Hamdollahi Oskouei, S.; Hashemzadeh, M. FoodRecNet: A Comprehensively Personalized Food Recommender System Using Deep Neural Networks. Knowl. Inf. Syst. 2023, 65, 3753–3775. [Google Scholar] [CrossRef]
- Al-Chalabi, H.H.; Jasim, M.N. Food Recommendation System Based on Data Clustering Techniques and User Nutrition Records. In Proceedings of the New Trends in Information and Communications Technology Applications, Baghdad, Iraq, 17–18 November 2021; pp. 139–161. [Google Scholar]
- Arslan, S. Exploring the Potential of Chat GPT in Personalized Obesity Treatment. Ann. Biomed. Eng. 2023, 51, 1887–1888. [Google Scholar] [CrossRef]
- Sivasubramanian, J.; Shaik Hussain, S.M.; Virudhunagar Muthuprakash, S.; Periadurai, N.D.; Mohanram, K.; Surapaneni, K.M. Analysing the Clinical Knowledge of ChatGPT in Medical Microbiology in the Undergraduate Medical Examination. Indian J. Med. Microbiol. 2023, 45, 100380. [Google Scholar] [CrossRef]
- Seney, V.; Desroches, M.L.; Schuler, M.S. Using ChatGPT to Teach Enhanced Clinical Judgment in Nursing Education. Nurse Educ. 2023, 48, 124. [Google Scholar] [CrossRef]
- Huh, S. Are ChatGPT’s Knowledge and Interpretation Ability Comparable to Those of Medical Students in Korea for Taking a Parasitology Examination?: A Descriptive Study. J. Educ. Eval. Health Prof. 2023, 20, 1516081869. [Google Scholar] [CrossRef]
- Bhayana, R.; Krishna, S.; Bleakney, R.R. Performance of ChatGPT on a Radiology Board-style Examination: Insights into Current Strengths and Limitations. Radiology 2023, 307, 230582. [Google Scholar] [CrossRef] [PubMed]
- Sedaghat, S. Success Through Simplicity: What Other Artificial Intelligence Applications in Medicine Should Learn from History and ChatGPT. Ann. Biomed. Eng. 2023, 1–2. [Google Scholar] [CrossRef]
- Seetharaman, R. Revolutionizing Medical Education: Can ChatGPT Boost Subjective Learning and Expression? J. Med. Syst. 2023, 47, 61. [Google Scholar] [CrossRef]
- Zhu, L.; Mou, W.; Chen, R. Can the ChatGPT and Other Large Language Models with Internet-Connected Database Solve the Questions and Concerns of Patient With Prostate Cancer and Help Democratize Medical Knowledge? J. Transl. Med. 2023, 21, 269. [Google Scholar] [CrossRef]
- Kung, T.H.; Cheatham, M.; Medenilla, A.; Sillos, C.; De Leon, L.; Elepaño, C.; Madriaga, M.; Aggabao, R.; Diaz-Candido, G.; Maningo, J.; et al. Performance of ChatGPT on USMLE: Potential for Ai-Assisted Medical Education Using Large Language Models. PLoS Digit. Health 2023, 2, e0000198. [Google Scholar] [CrossRef]
- Garcia, M.B. Plan-Cook-Eat: A Meal Planner App with Optimal Macronutrient Distribution of Calories Based on Personal Total Daily Energy Expenditure. In Proceedings of the 2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), Laoag, Philippines, 29 November–1 December 2019; pp. 1–5. [Google Scholar]
- Garcia, M.B.; Revano, T.F., Jr.; Loresco, P.J.M.; Maaliw, R.R., III; Oducado, R.M.F.; Uludag, K. Virtual Dietitian as a Precision Nutrition Application for Gym and Fitness Enthusiasts: A Quality Improvement Initiative. In Proceedings of the 2022 IEEE 14th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), Boracay Island, Philippines, 1–4 December 2022; pp. 1–5. [Google Scholar]
- Michalski, C.A.; Diemert, L.M.; Helliwell, J.F.; Goel, V.; Rosella, L.C. Relationship Between Sense of Community Belonging and Self-rated Health Across Life Stages. SSM-Popul. Health 2020, 12, 100676. [Google Scholar] [CrossRef] [PubMed]
- Tonkin, E.; Brimblecombe, J.; Wycherley, T.P. Characteristics of Smartphone Applications for Nutrition Improvement in Community Settings: A Scoping Review. Adv. Nutr. 2017, 8, 308–322. [Google Scholar] [CrossRef] [PubMed]
- Mueller, C.; Compher, C.; Ellen, D.M. American Society for Parenteral and Enteral Nutrition (ASPEN) Board of Directors. Clinical Guidelines: Nutrition Screening, Assessment, and Intervention in Adults. J. Parenter. Enter. Nutr. 2011, 35, 16–24. [Google Scholar] [CrossRef] [PubMed]
- Eliot, K.A.; L’Horset, A.M.; Gibson, K.; Petrosky, S. Interprofessional Education and Collaborative Practice in Nutrition and Dietetics 2020: An Update. J. Acad. Nutr. Diet. 2021, 121, 637–643. [Google Scholar] [CrossRef]
- Tappenden, K.A.; Quatrara, B.; Parkhurst, M.L.; Malone, A.M.; Fanjiang, G.; Ziegler, T.R. Critical Role of Nutrition in Improving Quality of Care: An Interdisciplinary Call to Action to Address Adult Hospital Malnutrition. J. Acad. Nutr. Diet. 2013, 113, 1219–1237. [Google Scholar] [CrossRef] [PubMed]
- Cheng, K.; Li, Z.; He, Y.; Guo, Q.; Lu, Y.; Gu, S.; Wu, H. Potential Use of Artificial Intelligence in Infectious Disease: Take ChatGPT as an Example. Ann. Biomed. Eng. 2023, 51, 1130–1135. [Google Scholar] [CrossRef]
- Frank, V.; Daniel, W.; Clément, P. ChatGPT: When Artificial Intelligence Replaces the Rheumatologist in Medical Writing. Ann. Rheum. Dis. 2023, 82, 1–3. [Google Scholar] [CrossRef]
- Pennella, A.R.; Rubano, C. Understanding Emotional Issues of Clients Approaching to Nutrition Counseling: A Qualitative, Exploratory Study in Italy. J. Health Soc. Sci. 2019, 4, 73–84. [Google Scholar] [CrossRef]
- Sanmarchi, F.; Bucci, A.; Nuzzolese, A.G.; Carullo, G.; Toscano, F.; Nante, N.; Golinelli, D. A Step-by-Step Researcher’s Guide to the Use of an AI-Based Transformer in Epidemiology: An Exploratory Analysis of ChatGPT using the STROBE Checklist for Observational Studies. J. Public Health 2023, 1–36. [Google Scholar] [CrossRef]
- Niszczota, P.; Rybicka, I. The Credibility of Dietary Advice Formulated by ChatGPT: Robo-Diets for People With Food Allergies. Nutrition 2023, 112, 112076. [Google Scholar] [CrossRef]
- Garcia, M.B. Can ChatGPT Substitute Human Companionship for Coping with Loss and Trauma? J. Loss Trauma 2023, 28, 784–786. [Google Scholar] [CrossRef]
- DiFilippo, K.N.; Huang, W.-H.; Andrade, J.E.; Chapman-Novakofski, K.M. The Use of Mobile Apps to Improve Nutrition Outcomes: A Systematic Literature Review. J. Telemed. Telecare 2015, 21, 243–253. [Google Scholar] [CrossRef]
- Garcia, M.B.; Yousef, A.M.F.; Pereira de Almeida, R.P.; Arif, Y.M.; Happonen, A.; Barber, W. Teaching Physical Fitness and Exercise Using Computer-Assisted Instruction: A School-Based Public Health Intervention. In Handbook of Research on Instructional Technologies in Health Education and Allied Disciplines; IGI Global: Hershey, PA, USA, 2023; pp. 177–195. [Google Scholar] [CrossRef]
- Rattray, M.; Roberts, S. Dietitians’ Perspectives on the Coordination and Continuity of Nutrition Care for Malnourished or Frail Clients: A Qualitative Study. Healthcare 2022, 10, 986. [Google Scholar] [CrossRef]
- Ali, S.I.; Begum, J.; Badusha, M.; Reddy, E.S.; Rali, P.; Lalitha, D.L. Participatory Cooking Demonstrations: A Distinctive Learning Approach Towards Positive Health. J. Fam. Med. Prim. Care 2022, 11, 7101–7105. [Google Scholar] [CrossRef]
- Hummell, A.C.; Cummings, M. Role of the Nutrition-Focused Physical Examination in Identifying Malnutrition and Its Effectiveness. Nutr. Clin. Pract. 2022, 37, 41–49. [Google Scholar] [CrossRef] [PubMed]
- Cardoso, A.P.; Ferreira, V.; Leal, M.; Ferreira, M.; Campos, S.; Guiné, R.P.F. Perceptions about Healthy Eating and Emotional Factors Conditioning Eating Behaviour: A Study Involving Portugal, Brazil and Argentina. Foods 2020, 9, 1236. [Google Scholar] [CrossRef] [PubMed]
- Suh, M.; Youngblom, E.; Terry, M.; Cai, C.J. AI as Social Glue: Uncovering the Roles of Deep Generative AI during Social Music Composition. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, Yokohama, Japan, 8–13 May 2021. [Google Scholar]
- Javaid, M.; Haleem, A.; Singh, R.P. ChatGPT for Healthcare Services: An Emerging Stage for an Innovative Perspective. BenchCouncil Trans. Benchmarks Stand. Eval. 2023, 3, 100105. [Google Scholar] [CrossRef]
- Parray, A.A.; Inam, Z.M.; Ramonfaur, D.; Haider, S.S.; Mistry, S.K.; Pandya, A.K. ChatGPT and Global Public Health: Applications, Challenges, Ethical Considerations and Mitigation Strategies. Glob. Transit. 2023, 5, 50–54. [Google Scholar] [CrossRef]
- Li, H.; Moon, J.T.; Purkayastha, S.; Celi, L.A.; Trivedi, H.; Gichoya, J.W. Ethics of Large Language Models in Medicine and Medical Research. Lancet Digit. Health 2023, 5, 333–335. [Google Scholar] [CrossRef]
- Ryan, M. The Ethics of Dietary Apps: Technology, Health, and the Capability Approach. Technol. Soc. 2022, 68, 101873. [Google Scholar] [CrossRef]
- Dahdah, J.E.; Kassab, J.; Helou, M.C.E.; Gaballa, A.; Sayles, S.; Phelan, M.P. ChatGPT: A Valuable Tool for Emergency Medical Assistance. Ann. Emerg. Med. 2023, 82, 411–413. [Google Scholar] [CrossRef]
- Safranek, C.W.; Sidamon-Eristoff, A.E.; Gilson, A.; Chartash, D. The Role of Large Language Models in Medical Education: Applications and Implications. JMIR Med. Educ. 2023, 9, e50945. [Google Scholar] [CrossRef]
- Liaw, W.; Chavez, S.; Pham, C.; Tehami, S.; Govender, R. The Hazards of Using ChatGPT: A Call to Action for Medical Education Researchers. PRiMER 2023, 7, 27. [Google Scholar] [CrossRef]
- Homolak, J. Opportunities and Risks of ChatGPT in Medicine, Science, and Academic Publishing: A Modern Promethean Dilemma. Croat. Med. J. 2023, 64, 1–3. [Google Scholar] [CrossRef] [PubMed]
- Sezgin, E. Artificial Intelligence in Healthcare: Complementing, Not Replacing, Doctors and Healthcare Providers. Digit. Health 2023, 9, 20552076231186520. [Google Scholar] [CrossRef] [PubMed]
Characteristics | ChatGPT | Knowledge-Based Systems |
---|---|---|
Computational approach | Data-driven | Knowledge-driven |
Learning capability | Transfer learning | Explicit knowledge |
Decision making | Contextual understanding | Rule-based Logic |
User interaction | Human-like conversations | Guided knowledge queries |
Use cases | Conversational applications | Expert systems |
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Garcia, M.B. ChatGPT as a Virtual Dietitian: Exploring Its Potential as a Tool for Improving Nutrition Knowledge. Appl. Syst. Innov. 2023, 6, 96. https://doi.org/10.3390/asi6050096
Garcia MB. ChatGPT as a Virtual Dietitian: Exploring Its Potential as a Tool for Improving Nutrition Knowledge. Applied System Innovation. 2023; 6(5):96. https://doi.org/10.3390/asi6050096
Chicago/Turabian StyleGarcia, Manuel B. 2023. "ChatGPT as a Virtual Dietitian: Exploring Its Potential as a Tool for Improving Nutrition Knowledge" Applied System Innovation 6, no. 5: 96. https://doi.org/10.3390/asi6050096
APA StyleGarcia, M. B. (2023). ChatGPT as a Virtual Dietitian: Exploring Its Potential as a Tool for Improving Nutrition Knowledge. Applied System Innovation, 6(5), 96. https://doi.org/10.3390/asi6050096