¹Director, Department of Critical Care, S.L.Raheja Hospital- A Fortis Associate, Mumbai, India
²CEO, Docmode Health Technologies Private Limited, Mumbai, Maharashtra, India
³ICU Registrar, Department of Critical Care, S.l.Raheja Hospital- A Fortis Associate, Mumbai, Maharashtra, India

*Corresponding author:Sanjith Saseedharan, Director, Department of Critical Care, S.L.Raheja Hospital- A Fortis Associate, Mumbai, India, Email Id: docsanjith@rediffmail.com

Article Information:Submission: 08/09/2024; Accepted: 27/09/2024; Published: 30/09/2024

Copyright: © 2024 Sanjith Saseedharan, et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

The field of healthcare has witnessed a remarkable transformation in recent years, largely owing to the integration of Artificial Intelligence (AI) into various aspects of medical practice. AI, in its current state, has become a powerful tool for improving patient care, and one of its promising applications is in the realm of clinical nutrition. This article explores the growing significance of AI in clinical nutrition, highlighting its role in nutrition therapy across diverse healthcare settings.

Before delving into the application of AI in clinical nutrition, it is essential to define what AI represents today. Artificial Intelligence refers to the simulation of human intelligence in machines that are programmed to think and learn like humans. These machines are capable of performing tasks that typically require human intelligence, such as problem-solving, decision-making, language understanding, and pattern recognition.

In the context of healthcare, AI leverages advanced algorithms and data analysis techniques to assist healthcare professionals in diagnosing diseases, designing treatment plans, and improving overall patient care. One notable application of AI in healthcare is Clinical Decision Support Systems (CDSS), which play a pivotal role in guiding healthcare providers through complex medical scenarios.

Nutrition therapy has gained immense importance in modern healthcare, not only for disease prevention but also for aiding recovery and improving overall patient outcomes. AI has found its way into various healthcare domains, and clinical nutrition is no exception. Here are some key areas where AI is making a significant impact:

In outpatient settings, AI-driven tools assist healthcare professionals in assessing patients’ nutritional needs, planning personalized dietary interventions, and monitoring progress over time. These tools use patient data and medical guidelines to recommend tailored nutrition plans.

In critical care units, AI plays a crucial role in optimizing the nutrition delivery for patients who are critically ill. AI-driven systems can calculate the precise nutritional requirements of patients, monitor their intake, and ensure that they receive the appropriate nutrients for their condition.

AI-powered solutions are used to provide nutritional guidance to pregnant women, ensuring they receive the essential nutrients during pregnancy. These tools can help manage nutrition-related complications and support healthy pregnancies.

For individuals with diabetes, AI-based systems can assist in tracking blood glucose levels, analyzing dietary choices, and recommending suitable meal plans to help manage the condition effectively.

AI is also becoming increasingly valuable in home care and palliative care settings. It helps caregivers and patients manage nutritional needs at home, ensuring that individuals receive proper nutrition even when they are not in a clinical setting.

In the context of cancer and autoimmune diseases, AI-driven nutrition management tools assist in designing specialized diets that support patients during their treatment journeys. These tools consider the unique nutritional requirements of patients with these conditions.

iNutrimon: A Game-Changer in Clinical Nutrition One noteworthy AI-driven solution in the field of clinical nutrition is iNutrimon. This innovative tool, co-created by Dr. Sanjith Sasheedharan, Head of Critical Care at SL Raheja Hospital, is currently employed in 15 of India’s top-tier hospitals and serves approximately 3000 patients daily.

iNutrimon is a web application designed to empower clinical nutrition teams, including dietitians, physicians, nurses, and food and beverage professionals. It streamlines the nutrition management process, addressing malnutrition cases in hospitals while minimizing food wastage, thereby improving the bottom line. Here’s how it works:
Automated Anthropometric Data Calculation: iNutrimon automates the calculation of ideal, actual, and adjusted body weight based on chosen BMI formulas. This eliminates the need for manual calculations and potential errors.
Nutritional Assessment and Screening Tools: The tool provides access to various assessment and screening tools, including Nutric Score, Nutritional Risk Screening (NRS), Subjective Global Assessment (SGS), and more. These tools aid in comprehensive patient evaluation.
Customized Nutritional Recommendations: iNutrimon suggests macro-nutritional requirements based on guidelines from the European Society for Clinical Nutrition and Metabolism (ESPEN) and the American Society for Parenteral and Enteral Nutrition (ASPEN). It can also integrate readings from indirect calorimeters for precise calorie requirements.
Nutrition Delivery Management: The tool facilitates the management of nutrition delivery, including kitchen feed, enteral or parenteral nutrition, and additives. It monitors delivery and deficits, enabling healthcare professionals to track patient recovery due to improved nutrition.

The next frontier for iNutrimon is AI integration, which involves leveraging Large Language Models (LLMs) and semantic search. An accompanying tool, AIDE, aims to provide evidence-based insights to healthcare professionals with 100% accuracy, relying on references from evidence-based papers and documents. The AIenhanced iNutrimon seeks to deliver even more precise and informed nutritional guidance to clinical teams.

Despite the potential benefits of AI in clinical nutrition, there are challenges that need to be addressed:

One major problem with AI implementation in clinical nutrition, as well as in healthcare in general, is the reliance on generalized datasets. These datasets may not always align with the specific medical data required for accurate nutritional assessments and recommendations.

Resistance to AI adoption in healthcare can be attributed to concerns about its reliance on non-specialized data sources. Healthcare professionals may be hesitant to trust AI recommendations if the underlying data sources are not from trusted and reliable medical references.

To ensure the accuracy of AI-driven clinical nutrition solutions, primary knowledge sets from medical literature and secondary knowledge sets from specialized sources are essential. The integration of these knowledge sets helps in making more informed and reliable recommendations.

The world of clinical nutrition and dietetics is undergoing a profound transformation, thanks to the integration of Artificial Intelligence (AI) into the field. Dietitians, in particular, are experiencing a paradigm shift in the way they assess, plan, and manage their patients’ nutritional needs. In this article, we explore the various facets of AI in clinical nutrition and dietetics, with a specific focus on how AI-powered tools are being tailored to meet the unique needs of dietitians.

Before delving into the specialized versions of AI tools designed for dietitians, it’s crucial to understand the broader role of AI in this field. AI, in its current state, is a powerful ally for dietitians, enabling them to provide more accurate, efficient, and personalized nutritional guidance to their patients. Here are some key areas where AI is making a significant impact:

AI-driven tools like iNutrimon assist dietitians in assessing patients’ nutritional needs more comprehensively. These tools take into account a wide range of factors, including medical history, dietary preferences, and specific health conditions. As a result, dietitians can create highly personalized nutrition plans that are tailored to each patient’s unique requirements. Dietitians can plan nutritional therapy through kitchen diet, enteral, parenteral, supplements and additives or their combination. For example, stressfree calculation of feeding formula is suggested by the system to meet patient nutrition requirements. It provides us with options to select the available products and check the number of scoops that meet nutrient requirement.

AI-powered platforms will enable physicians and dietitians to monitor and track their patients’ progress more effectively. By analyzing data from wearables, patient-reported outcomes, and other sources, dietitians can make real-time adjustments to nutrition plans and offer timely guidance to their patients.The AI tool acts as an aid to design novel recipes using certain blood parameters of patients. However, it may not prescribe a cooking process to increase the bioavailability of nutrients based on patient’s blood parameters and vitals.

Clinical Decision Support Systems (CDSS) powered by AI provide (Healthcare Professionals) HCPs with evidence-based recommendations and guidelines. These systems help dietitians make informed decisions about dietary interventions, ensuring that their advice is in line with the latest research and best practices.

AI-driven apps and platforms encourage patient engagement by providing educational content, meal planning assistance, and reminders for tracking dietary intake. This engagement fosters better adherence to nutrition plans and improves overall patient outcomes.

B. Specialized AI Versions for Dietitians Now, let’s explore how AI is being tailored to meet the specific needs of dietitians across various aspects of their practice:

1. CDSS Version - Streamlining Nutritional Guidance The CDSS Version of AI is designed to be a dietitian’s trusted companion in providing evidence-based nutritional guidance. It relies on knowledge-based datasets that include a vast array of nutrition-related research, including PubMed ID articles and PMC article datasets. Furthermore, it covers critical aspects of nutrition, such as drug-to-drug interactions, drug and food interactions, and mapping branded drug names to generic names in different regions. This AI version empowers dietitians with a comprehensive database of nutritional knowledge, enabling them to make more informed decisions when creating dietary plans for their patients. It ensures that dietitians have access to the latest research, guidelines, and insights, resulting in more effective nutritional interventions.

2. PG and UG Learning Version - Shaping the Future of Dietetic Education Education is the cornerstone of the dietetics profession, and AI is playing a pivotal role in revolutionizing the way dietitians are trained. The PG (Postgraduate) and UG (Undergraduate) Learning Version of AI acknowledges the specific needs of dietetics students and educators.

To support the education of future dietitians, this AI version requires access to proprietary datasets from medical book publishers and question banks. It serves as a valuable educational tool that supplements traditional coursework, ensuring that dietetics students are well-prepared to meet the challenges of real-world nutrition practice. Dietetics students can engage with AI-powered resources that offer in-depth insights into nutritional concepts, clinical scenarios, and dietary planning. This version aims to produce dietitians who are not only knowledgeable but also adept at leveraging AI to provide the best possible care to their patients.

3. HCPs Practice Version - Optimizing Dietetic Consultations The HCPs Practice Version of AI is a game-changer for physicians and dietitians practicing in clinical settings. It streamlines the process of integrating patients’ primary health data into dietary consultations. This AI version possesses the capability to interpret various data formats, including PDFs and DiCOM files, to create concise and informative summaries. In practical terms, this means that HCPs can significantly save time during patient consultations. AI efficiently extracts relevant information from patient records, medical reports, and diagnostic tests, providing a comprehensive overview of the patient’s health and dietary needs. Importantly, the Practice Version adheres to stringent privacy regulations, such as ABHA (Ayushman Bharat Health Account) and HIPAA guidelines, ensuring that patient data remains secure and confidential. By adopting the Practice Version, can enhance the efficiency of their practice, deliver more personalized dietary recommendations, and dedicate more time to meaningful patient interactions.

3. Patient Community Version - Empowering Patients through Nutrition The Patient Community Version of AI recognizes that patients are active participants in their dietary and health journeys. This version aims to integrate datasets from reputable open patient support communities, such as MedlinePlus and Mayo Clinic, into the dietitian’s toolkit. By tapping into these valuable resources, dietitians can guide their patients to access credible information, engage with peer support groups, and contribute to reviews and discussions related to nutrition and health. AI assists in organizing and curating communitygenerated content, making it easily accessible and trustworthy. This version fosters a collaborative approach to nutrition, where dietitians and patients work together to achieve dietary goals. It empowers patients to become well-informed advocates for their health, leading to better adherence to dietary plans and improved health outcomes.

4. Research Version - Advancing Nutritional Science The Research Version of AI serves as a catalyst for innovation and scientific discovery in the field of dietetics. It supports researchers, institutions, and data scientists in conducting rigorous studies and collaborative research efforts related to nutrition and dietary science. This AI version relies on proprietary licensed full-text articles and raw datasets from open-access data repositories, including sources like Figsh are and DocMode’s SURE platform. These resources facilitate retrospective studies, data analysis, and the exploration of new research questions in the realm of clinical nutrition and dietetics. Additionally, the Research Version encourages partnerships with health providers, existing EMR/EHR companies, and data aggregators like Ellkay to access diverse and agnostic data sources. Researchers can draw insights from real-world patient data, enabling advancements in dietary practices and therapies.

In conclusion, the specialized versions of AI tools tailored for dietitians are poised to revolutionize the field of clinical nutrition and dietetics. These AI-powered solutions empower Healthcare professionals including physicians and dietitians to provide evidence based, personalized dietary guidance to their patients, enhance their education and training, optimize their practice, foster patient engagement, and contribute to cutting-edge research in the field of nutrition. As AI continues to evolve, it will play an increasingly vital role in elevating the standards of care in clinical nutrition and dietetics, ultimately leading to improved health and well-being for individuals and communities. iNutrimon, with its AI integration plans, promises to enhance the accuracy and efficiency of nutritional care delivery. Through such AI tools we can provide highly focused and accurate nutritive value to patient depending on various factors such as comorbidities and BMI versus referring to Recommended dietary allowances given by NIN, ICMR as the same requirements may not apply to each individual in a particular age group. As AI continues to evolve and adapt to the specific needs of healthcare, it has the potential to revolutionize clinical nutrition, ultimately leading to improved patient outcomes and a healthier future for all.