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Introduction: The emergence of artificial intelligence (AI) chat programs has opened two distinct paths, one enhancing interaction and another potentially replacing personal understanding. Ethical and legal concerns arise due to the rapid development of these programs. This paper investigates academic discussions on AI in medicine, analyzing the context, frequency, and reasons behind these conversations. Methods: The study collected data from the Web of Science database on articles containing the keyword "ChatGPT" published from January to September 2023, resulting in 786 medically related journal articles. The inclusion criteria were peer-reviewed articles in English related to medicine. Results: The United States led in publications (38.1%), followed by India (15.5%) and China (7.0%). Keywords such as "patient" (16.7%), "research" (12%), and "performance" (10.6%) were prevalent. The Cureus Journal of Medical Science (11.8%) had the most publications, followed by the Annals of Biomedical Engineering (8.3%). August 2023 had the highest number of publications (29.3%), with significant growth between February to March and April to May. Medical General Internal (21.0%) was the most common category, followed by Surgery (15.4%) and Radiology (7.9%). Discussion: The prominence of India in ChatGPT research, despite lower research funding, indicates the platform's popularity and highlights the importance of monitoring its use for potential medical misinformation. China's interest in ChatGPT research suggests a focus on Natural Language Processing (NLP) AI applications, despite public bans on the platform. Cureus' success in publishing ChatGPT articles can be attributed to its open-access, rapid publication model. The study identifies research trends in plastic surgery, radiology, and obstetric gynecology, emphasizing the need for ethical considerations and reliability assessments in the application of ChatGPT in medical practice. Conclusion: ChatGPT's presence in medical literature is growing rapidly across various specialties, but concerns related to safety, privacy, and accuracy persist. More research is needed to assess its suitability for patient care and implications for non-medical use. Skepticism and thorough review of research are essential, as current studies may face retraction as more information emerges. [ABSTRACT FROM AUTHOR]

The average annual income of farmers in India is well below the poverty line, primarily due to off-season agriculture and a lack of awareness about optimal farming techniques and crop management. This research paper presents an AI-based web application designed to address these challenges and improve farmers' financial and mental well-being. Our solution, comprised of a farming tools leverage system and a collaborative farming forum, empowers farmers with the knowledge and tools needed to make informed decisions and increase their income. The system leverages MongoDB, asynchronous calling, and Fast API for efficient data management and real-time interactions. AI/ML services assist with crop recommendations, crop disease detection, and price predictions. Load balancing ensures optimal performance, and Pusher JS enables real-time communication. Billing services and a dashboard provide income insights, while geographic data enhances machine learning recommendations. In conclusion, this research contributes to alleviating poverty and enhancing the livelihoods of farmers in India by providing a comprehensive solution to the challenges they face. [ABSTRACT FROM AUTHOR]

In India, newborn screening (NBS) is essential for detecting health problems in infants. Despite significant progress, significant gaps and challenges persist. India has made great strides in genomics dueto the existence of the National Institute of Biomedical Genomics in West Bengal. The work emphasizes the challenges NBS programs confront with technology, budgetary constraints, insufficient counseling, inequality in illness panels, and a lack of awareness. Advancements in technology, such as genetic testing and next-generation sequencing, are expected to significantly transform the process. The integration of analytical tools, artificial intelligence, and machine learning algorithms could improve the efficiency of newborn screening programs, offering a personalized healthcare approach. It is critical to address gaps in information, inequities in illness incidence, budgetary restrictions, and inadequate counseling. Strengthening national NBS programs requires increased public awareness and coordinated efforts between state and central agencies. Quality control procedures must be used at every level for implementation to be successful. Additional studies endeavor to enhance NBS in India through public education, illness screening expansion, enhanced quality control, government incentive implementation, partnership promotion, and expert training. Improved neonatal health outcomes and the viability of the program across the country will depend heavily on new technology and counseling techniques., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

This work introduces a novel approach to remotely count and monitor potato plants in high-altitude regions of India using an unmanned aerial vehicle (UAV) and an artificial intelligence (AI)-based deep learning (DL) network. The proposed methodology involves the use of a self-created AI model called PlantSegNet, which is based on VGG-16 and U-Net architectures, to analyze aerial RGB images captured by a UAV. To evaluate the proposed approach, a self-created dataset of aerial images from different planting blocks is used to train and test the PlantSegNet model. The experimental results demonstrate the effectiveness and validity of the proposed method in challenging environmental conditions. The proposed approach achieves pixel accuracy of 98.65%, a loss of 0.004, an Intersection over Union (IoU) of 0.95, and an F1-Score of 0.94. Comparing the proposed model with existing models, such as Mask-RCNN and U-NET, demonstrates that PlantSegNet outperforms both models in terms of performance parameters. The proposed methodology provides a reliable solution for remote crop counting in challenging terrain, which can be beneficial for farmers in the Himalayan regions of India. The methods and results presented in this paper offer a promising foundation for the development of advanced decision support systems for planning planting operations., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Background: Large language models (LLMs) have revolutionized natural language processing with their ability to generate human-like text through extensive training on large data sets. These models, including Generative Pre-trained Transformers (GPT)-3.5 (OpenAI), GPT-4 (OpenAI), and Bard (Google LLC), find applications beyond natural language processing, attracting interest from academia and industry. Students are actively leveraging LLMs to enhance learning experiences and prepare for high-stakes exams, such as the National Eligibility cum Entrance Test (NEET) in India., Objective: This comparative analysis aims to evaluate the performance of GPT-3.5, GPT-4, and Bard in answering NEET-2023 questions., Methods: In this paper, we evaluated the performance of the 3 mainstream LLMs, namely GPT-3.5, GPT-4, and Google Bard, in answering questions related to the NEET-2023 exam. The questions of the NEET were provided to these artificial intelligence models, and the responses were recorded and compared against the correct answers from the official answer key. Consensus was used to evaluate the performance of all 3 models., Results: It was evident that GPT-4 passed the entrance test with flying colors (300/700, 42.9%), showcasing exceptional performance. On the other hand, GPT-3.5 managed to meet the qualifying criteria, but with a substantially lower score (145/700, 20.7%). However, Bard (115/700, 16.4%) failed to meet the qualifying criteria and did not pass the test. GPT-4 demonstrated consistent superiority over Bard and GPT-3.5 in all 3 subjects. Specifically, GPT-4 achieved accuracy rates of 73% (29/40) in physics, 44% (16/36) in chemistry, and 51% (50/99) in biology. Conversely, GPT-3.5 attained an accuracy rate of 45% (18/40) in physics, 33% (13/26) in chemistry, and 34% (34/99) in biology. The accuracy consensus metric showed that the matching responses between GPT-4 and Bard, as well as GPT-4 and GPT-3.5, had higher incidences of being correct, at 0.56 and 0.57, respectively, compared to the matching responses between Bard and GPT-3.5, which stood at 0.42. When all 3 models were considered together, their matching responses reached the highest accuracy consensus of 0.59., Conclusions: The study's findings provide valuable insights into the performance of GPT-3.5, GPT-4, and Bard in answering NEET-2023 questions. GPT-4 emerged as the most accurate model, highlighting its potential for educational applications. Cross-checking responses across models may result in confusion as the compared models (as duos or a trio) tend to agree on only a little over half of the correct responses. Using GPT-4 as one of the compared models will result in higher accuracy consensus. The results underscore the suitability of LLMs for high-stakes exams and their positive impact on education. Additionally, the study establishes a benchmark for evaluating and enhancing LLMs' performance in educational tasks, promoting responsible and informed use of these models in diverse learning environments., (©Faiza Farhat, Beenish Moalla Chaudhry, Mohammad Nadeem, Shahab Saquib Sohail, Dag Øivind Madsen. Originally published in JMIR Medical Education (https://mededu.jmir.org), 21.02.2024.)

Artificial Intelligence is quickly emerging as a technological solution for the agriculture industry to surmount its classical challenges. Artificial Intelligence is facilitating farmers to refine their products and alleviate unfavourable impacts due to the environment. The central concern of this paper is predictive analytics to develop a machine learning model to identify and predict crop yield based on multiple environmental factors. In this paper, a hybrid learner 'RaNN' is proposed that combines the feature sampling and majority voting technique of Random Forest in-combination with the multilayer Feedforward Neural Network to predict the crop yield. Research has also ascertained the essential features responsible for accurate yield prediction. The proposed model works for rice yield prediction, one of the chief grains of India. The region chosen for the work is Punjab, which is among the largest producer states of India for rice. The dataset consists of 15 attributes comprising the weather and agriculture data collected from the Indian Meteorological Department Pune, and Punjab Environment Information System (ENVIS) Center, Government of India. The study has also made a comparative assessment of 'RaNN' with machine learning methods like Multiple Linear Regression, Random Forest, Decision Tree, Boosting Regression, Support Vector Machine Regression, Ensemble Learner, and Artificial Neural Network. Our model RaNN has listed a better prediction accuracy with minimal error among the other techniques providing a 98% correlation between the actual and the predicted yield. Abbreviations: AI – Artificial Intelligence; ANN – Artificial Neural Network; BR – Boosting Regression; Chem Fert – chemical fertilisers; DT – Decision Tree; EL – Ensemble Learner; ENVIS – Punjab Environment Information System; GBM – Stochastic Gradient Boosting Method; GPS – Global Positioning System; HMAX – highest maximum temperature in degrees C; IMD – Indian Meteorological Department; L1 – Lasso regression; L2 – Ridge regression; LMIN – lowest minimum temperature; ML – Machine Learning; MAE – Mean Absolute Error; MEVP – mean evaporation in mm; MLR – Multiple Linear Regression; MMAX – mean maximum temperature in degrees C; MMIN – mean minimum temperature in degrees C; MSSH – Mean sunshine duration in hours; MWS – mean wind speed in km/h; P1 – number of days with precipitation (0.1–0.2 mm); P2 – number of days with precipitation (greater than or equal to 0.3 mm); RaNN – Hybrid RF-ANN model; RMSE – Root Mean Squared Error; $${R^2}$$ R 2 – Coefficient of determination; RD – number of rainy days; RF – Random Forest; SVM Reg – Support Vector Machine Regression; TMRF – total rainfall per month in mm [ABSTRACT FROM AUTHOR]

Rapid advancement in Artificial Intelligence (AI) has transformed the dynamics of interaction between organizations and consumers. The rapid emergence and adoption of AI chatbots have ushered in a new era of convenient and efficient customer service. This paper addresses the gap of how Gen Z perceives chatbots as an alternative for service interaction, considering that this sample of the population is relatively more tech savvy and understands technology better. Utilizing semi-structured interviews for in-depth interaction, a thematic analysis reveals six key themes: trust and reliability, nature of interaction, perceived usefulness/ease of use, advantages, disadvantages, and areas of improvement. Gen Z generally views chatbots as limited in handling complex queries, highlighting the importance of human intervention and database expansion. The identified themes provide valuable insights for organizations to highlight strengths and address weaknesses in AI chatbots' interactions with customers. The findings assist managers responsible for technology implementation in understanding customer pain points, fostering enhanced value for both users and organizations leveraging chatbots. This paper offers a comprehensive analysis of user experiences to illuminate the advantages and shortcomings of chatbots as service agents. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to establish and empirically investigate a research model examining the effect of four dimensions of the technology readiness index – optimism, innovativeness, discomfort and insecurity – on customer engagement that further influences purchase intention in the context of online shopping through artificial intelligence voice assistants (AI VAs). Design/methodology/approach: Data were collected in India from 429 customers in a self-administered online survey. Data analysis uses the structural equation modelling technique. Findings: Technology readiness dimensions, e.g. optimism, innovativeness, discomfort and insecurity, are critical factors driving customer engagement. Customer engagement further results in purchase intention in online shopping through AI VAs. Research limitations/implications: This study adds to the literature by understanding how customers' technology readiness levels drive engagement and purchase intention. However, this study includes customer engagement as a unidimensional construct. Further research can consist of customer engagement as a multidimensional construct. Practical implications: The findings offer guidelines for e-retailers to enhance customer engagement that matches their personality traits, thereby strengthening their purchase intention through AI VAs. Originality/value: The research contributes to the literature by empirically investigating a research model, revealing optimism, innovativeness, discomfort and insecurity as crucial parameters for customer engagement and purchase intention. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to investigate students' adoption intention (ADI) and actual usage (ATU) of artificial intelligence (AI)-based teacher bots (T-bots) for learning using technology adoption model (TAM) and context-specific variables. Design/methodology/approach: A mixed-method design is used wherein the quantitative and qualitative approaches were used to explore the adoption of T-bots for learning. Overall, 45 principals/directors/deans/professors were interviewed and NVivo 8.0 was used for interview data analysis. Overall, 1,380 students of higher education institutes were surveyed, and the collected data was analyzed using the Partial Least Squares Structural Equation Modeling (PLS-SEM) technique. Findings: The T-bot's ADI's antecedents found were perceived ease of use, perceived usefulness, personalization, interactivity, perceived trust, anthropomorphism and perceived intelligence. The ADI influences the ATU of T-bots, and its relationship is negatively moderated by stickiness to learn from human teachers in the classroom. It comprehends the insights of senior authorities of the higher education institutions in India toward the adoption of T-bots. Practical implications: The research provides distinctive insights for principals, directors and professors in higher education institutes to understand the factors affecting the students' behavioral intention and use of T-bots. The developers and designers of T-bots need to ensure that T-bots are more interactive, provide personalized information to students and ensure the anthropomorphic characteristics of T-bots. The education policymakers can also comprehend the factors of T-bot adoption for developing the policies related to T-bots and their implications in education. Originality/value: T-bot is a new disruptive technology in the education sector, and this is the first step in exploring the adoption factors. The TAM model is extended with context-specific factors related to T-bot technology to offer a comprehensive explanatory power to the proposed model. The research outcome provides the unique antecedents of the adoption of T-bots. [ABSTRACT FROM AUTHOR]