AI in Medical

Abstract

Artificial Intelligence (AI) stands as a transformative influence in the realm of medicine, poised to reshape healthcare delivery and elevate patient outcomes. This summary offers an overview of AI\'s impact on medical applications, with key technologies such as machine learning, natural language processing, and computer vision playing pivotal roles across various healthcare domains, from diagnostics to treatment planning and patient care. A notable stride in this evolution is the integration of AI-driven telemedicine solutions, effectively expanding access to healthcare services, particularly in underserved regions. Telehealth platforms leverage sophisticated AI algorithms for remote monitoring, facilitating real-time analysis of patient data and enabling prompt interventions.However, despite the immense potential, challenges persist. Issues surrounding data privacy, regulatory complexities, and the continual need for validating and refining AI models present hurdles to seamless integration. Addressing these challenges is imperative to fully capitalize on the benefits of AI in healthcare. In summary, the incorporation of AI into medicine holds the potential to deliver healthcare that is more tailored, efficient, and accessible, ultimately leading to enhanced patient outcomes and reduced healthcare costs. As the field advances, overcoming challenges and effectively harnessing AI\'s capabilities becomes crucial for the continued progression of global healthcare systems.

Introduction

I. INTRODUCTION

1. What is Artificial Intelligence?

AI is definitely a kind of new general-purpose technology (GPT), which is capable of complementing human labor and human tasks such as reasoning, creativity and managing multiple tasks altogether. In short, AI uses various technologies such as computer vision, audio processing, cognitive robotics, speech analytics, language processing and machine learning techniques to derive AI-based solutions.AI has the capability to unleash the hidden potential of human beings which helps them to contribute more effectively to the society and also helps bring in more timely, accurate and fast solutions.

It has an immense potential to transform the healthcare landscape of India, as it can effectively turn enormous medical and patient data into useful pieces of information which can further help in detecting complicated diseases, accelerate heath-based responses and improve public health surveillance.

2. Why is Artificial Intelligence Important?

AI is important for its potential to change how we live, work and play. It has been effectively used in business to automate tasks done by humans, including customer service work, lead generation, fraud detection and quality control.

In a number of areas, AI can perform tasks much better than humans. Particularly when it comes to repetitive, detail- oriented tasks, such as analyzing large numbers of legal documents to ensure relevant fields are filled in properly, AI tools often complete jobs quickly and with relatively few errors. Because of the massive data sets it can process, AI can also give enterprises insights into their operations they might not have been aware of. The rapidly expanding population of generative AI tools will be important in fields ranging from education and marketing to product design.

Indeed, advances in AI techniques have not only helped fuel an explosion in efficiency, but opened the door to entirely new business opportunities for some larger enterprises. Prior to the current wave of AI, it would have been hard to imagine using computer software to connect riders to taxis, but Uber has become a Fortune 500 company by doing just that. AI has become central to many of today's largest and most successful companies, including Alphabet, Apple, Microsoft and Meta, where AI technologies are used to improve operations and outpace competitors.

3. Artificial Intelligence in Medical

Artificial intelligence in medicine is the use of machine learning models to search medical data and uncover insights to help improve health outcomes and patient experiences.

Thanks to recent advances in computer science and informatics, artificial intelligence (AI) is quickly becoming an integral part of modern healthcare. AI algorithms and other applications powered by AI are being used to support medical professionals in clinical settings and in ongoing research.

Currently, the most common roles for AI in medical settings are clinical decision support and imaging analysis. Clinical decision support tools help providers make decisions about treatments, medications, mental health and other patient needs by providing them with quick access to information or research that's relevant to their patient. In medical imaging, AI tools are being used to analyze CT scans, x-rays, MRIs and other images for lesions or other findings that a human radiologist might miss. The challenges that the COVID-19 pandemic created for many health systems also led many healthcare organizations around the world to start field-testing new AI-supported technologies, such as algorithms designed to help monitor patients and AI-powered tools to screen COVID-19 patients. The research and results of these tests are still being gathered, and the overall standards for the use AI in medicine are still being defined. Yet opportunities for AI to benefit clinicians, researchers and the patients they serve are steadily increasing. At this point, there is little doubt that AI will become a core part of the digital health systems that shape and support modern medicine.

Artificial intelligence in healthcare is a term used to describe the use of machine-learning algorithms and software, or artificial intelligence (AI), to copy human cognition in the analysis, presentation, and understanding of complex medical and health care data, or to exceed human capabilities by providing new ways to diagnose, treat, or prevent disease. Specifically, AI is the ability of computer algorithms to approximate conclusions based solely on input data. The primary aim of health-related AI applications is to analyze relationships between clinical data and patient outcomes. AI programs are applied to practices such as diagnostics, treatment protocol development, drug development, personalized medicine, and patient monitoring and care. What differentiates AI technology from traditional technologies in healthcare is the ability to gather larger and more diverse data, process it, and produce a well-defined output to the end-user. AI does this through machine learning algorithms and deep learning. Because radiographs are the most common imaging tests conducted in most radiology departments, the potential for AI to help with triage and interpretation of traditional radiographs (X-ray pictures) is particularly noteworthy. These processes can recognize patterns in behavior and create their own logic. To gain useful insights and predictions, machine learning models must be trained using extensive amounts of input data. AI algorithms behave differently from humans in two ways: algorithms are literal: once a goal is set, the algorithm learns exclusively from the input data and can only understand what it has been programmed to do, and some deep learning algorithms are black boxes; algorithms can predict with extreme precision, but offer little to no comprehensible explanation to the logic behind its decisions aside from the data and type of algorithm used.

As widespread use of AI in healthcare is relatively new, research is ongoing into its application in various fields of medicine and industry. Additionally, greater consideration is being given to the unprecedented ethical concerns related to its practice such as data privacy, automation of jobs, and representation biases.Furthermore, new technologies brought about by AI in healthcare are often resisted by healthcare leaders, leading to slow and erratic adoption.

4. Motivation

The advantages of integrating Artificial Intelligence (AI) into the field of medicine have been extensively discussed in the medical literature. AI, equipped with sophisticated algorithms, has the ability to learn features from a vast volume of healthcare data, using these insights to assist in clinical practice. Its learning and self- correcting capabilities contribute to improving accuracy over time, with the added benefit of providing physicians with up-to-date medical information from various sources. AI systems can play a crucial role in reducing diagnostic and therapeutic errors inherent in human clinical practice, while also extracting valuable information from large patient populations for real-time health risk alerts and outcome predictions.

The motivation for incorporating AI into medicine stems from its remarkable potential to revolutionize healthcare. The promise of enhanced diagnostic accuracy allows healthcare professionals to make more precise and timely diagnoses, minimizing the risk of human error. AI's data-driven approach supports personalized treatment plans, optimizing effectiveness while minimizing side effects. The ability to detect subtle signs of illnesses facilitates early disease detection, a critical factor in improving patient outcomes. AI also streamlines healthcare processes, optimizing resource allocation and reducing costs. The integration of AI in telemedicine and remote care expands healthcare access, particularly in underserved regions. AI-driven drug discovery accelerates the development of new medications and therapies, offering hope for previously untreatable conditions. Beyond individual patient care, AI supports public health efforts, aids in tracking disease outbreaks, and provides essential insights to researchers. Ultimately, the motivation for integrating AI in medicine is rooted in a commitment to enhancing patient care, improving healthcare outcomes, and addressing the challenges faced by healthcare systems in an ever-evolving landscape.

While challenges and ethical considerations exist, the potential benefits of AI in medicine continue to drive ongoing research and implementation efforts. The overarching goal is to create a healthcare system that is more efficient, accurate, and accessible, especially in the face of challenges such as an aging population and rising costs.

5. Purpose

The integration of Artificial Intelligence (AI) into medical applications serves a multifaceted purpose, aiming to enhance and transform healthcare across various dimensions. Foremost among these objectives is the improvement of diagnostic accuracy. AI provides crucial support to healthcare professionals by analyzing intricate medical data and images, ensuring more precise and consistent diagnoses. Early disease detection is also a primary goal, enabling timely interventions that lead to improved patient outcomes. Personalized treatment plans represent a central focus of AI in medicine. By leveraging AI to process patient-specific data, including genetics and medical histories, healthcare providers can tailor treatments to individual patients. This personalized approach maximizes treatment effectiveness while minimizing potential side effects. Additionally, AI is instrumental in optimizing resource allocation within healthcare systems, ensuring efficient distribution and identifying high-risk patient populations for targeted interventions. The expansion of telemedicine and remote care is another vital objective of AI, particularly in making healthcare more accessible to remote or underserved regions. AI-driven telemedicine solutions facilitate remote consultations and remote patient monitoring. Furthermore, AI plays a crucial role in drug discovery and development by accelerating the identification of potential drug candidates and streamlining clinical trials.

Efficiency and cost savings are integral to AI's purpose in healthcare. By automating administrative tasks and optimizing healthcare processes, AI contributes to reducing costs and improving overall system efficiency. Its role in epidemiology and public health is equally vital, with AI aiding in disease outbreak prediction, population health monitoring, and informed decision-making in public health.

Beyond these objectives, AI in medicine also aims to provide mental health support, advance research, enhance patient engagement, and reduce the workload for healthcare professionals. In essence, the purpose of AI in the medical field is to elevate patient care, optimize healthcare outcomes, address evolving challenges in healthcare systems, and contribute to scientific discoveries, ultimately promoting the overall well-being of patients

6. Scope

The scope of AI in the medical field is both wide-ranging and dynamic, with numerous applications and opportunities for advancement. At its core, AI holds the potential to revolutionize healthcare by providing innovative solutions to longstanding challenges One prominent aspect of its scope is in medical imaging, where AI enhances the interpretation of complex scans and images, improving diagnostic accuracy and enabling the early detection of diseases. AI's ability to predict and diagnose diseases, from common ailments to rare disorders, extends its utility. Moreover, it plays a crucial role in personalized medicine, using patient-specific data to develop tailored treatment plans, reducing adverse effects, and optimizing therapeutic outcomes. In the realm of drug discovery, AI expedites the identification of potential medications, streamlining clinical trials and ultimately accelerating the development of novel therapies. Electronic health records (EHRs) benefit from AI-powered solutions, making data management more efficient, improving data accuracy, and facilitating clinical decision support.Telemedicine and remote monitoring represent a significant part of AI's scope, expanding access to healthcare services, especially for individuals in remote or underserved areas. Additionally, AI-driven mental health support promotes accessibility and reduces stigma by engaging patients, offering counseling, and directing them to appropriate resources.

AI's impact on public health and epidemiology is substantial, enabling the prediction and tracking of disease outbreaks, monitoring population health, and aiding in public health decision-making. It also contributes to research advancements by sifting through vast datasets, uncovering patterns, and accelerating scientific discoveries across various medical disciplines. The engagement of patients through AI-powered apps and tools encourages self- management, adherence to treatment plans, and informed healthcare decisions, ultimately improving patient outcomes. Furthermore, AI assists in the early detection and classification of tumors, thus refining cancer diagnosis and treatment planning.

The scope of AI in genomic medicine is vital, with AI supporting the analysis of genomic data to identify genetic mutations, predispositions, and personalized treatment options. As AI technologies evolve, they offer ongoing potential for innovation and positive change in the field of medicine, providing opportunities to enhance patient care, research, and healthcare delivery. To fully realize this potential, it is imperative to address ethical, regulatory, and privacy considerations, ensuring the responsible and effective use of AI in healthcare.

7. Objectives

The objectives of AI in the medical field are diverse, encompassing a wide array of crucial goals aimed at revolutionizing healthcare delivery and patient well-being. Foremost among these objectives is the pursuit of enhanced diagnostic accuracy. AI is designed to augment the diagnostic capabilities of healthcare professionals by analyzing complex medical data and images, thereby reducing the likelihood of diagnostic errors and ultimately improving patient care.

Early disease detection represents another vital objective. By identifying subtle signs and patterns indicative of diseases at their nascent stages, AI enables timely intervention and better patient outcomes, particularly in conditions where early detection significantly impacts prognosis. Personalized treatment plans are at the heart of AI's mission in medicine. AI leverages patient-specific data, including genetics, medical history, and treatment responses, to tailor therapies to individual patients. This objective maximizes treatment effectiveness while minimizing side effects, contributing to the practice of precision medicine.

Resource optimization is a key priority. AI's role in identifying high-risk patient populations and predicting healthcare trends enables targeted interventions and resource allocation to enhance patient care. The expansion of telemedicine and remote care signifies a fundamental goal of increasing healthcare accessibility. AI-driven telemedicine solutions facilitate remote consultations and real-time patient monitoring, addressing the objective of providing quality care to individuals in remote or underserved areas.

Drug discovery and development are objectives with far-reaching implications. AI expedites the identification of potential drug candidates, streamlines clinical trial designs, and accelerates the development of novel medications and therapies. Efficiency and cost savings are critical goals. By automating administrative tasks and optimizing healthcare processes, AI aims to reduce healthcare costs and enhance the overall efficiency of healthcare systems.

II. LITERATURE SURVEY

Large AI Models in Health Informatics: Applications, Challenges, and the Future [1] In thisLarge AI Models in Health Informatics: Applications, Challenges, and the Future. Scholars such as Jianing Qiu , Lin Li , Jiankai Sun , Graduate Student Member, IEEE, Jiachuan Peng , Peilun Shi, Ruiyang Zhang, Yinzhao Dong, Kyle Lam , Frank P.-W. Lo , Bo Xiao ,Wu Yuan , Senior Member, IEEE, Ningli Wang, Dong Xu , Member, IEEE, and Benny Lo , Senior Member, IEEE have advanced research in this field.Recent developments in artificial intelligence (AI) have given rise to large AI models, often referred to as foundation models, characterized by their substantial scale in both parameters and data. These models, which can extend into the billions in magnitude, exhibit remarkable performance across a spectrum of tasks after being pretrained. Notably, models like ChatGPT, with its exceptional capabilities, have sparked imagination regarding the extensive impact that large AI models could wield and their potential to revolutionize various facets of our daily lives.In the field of health informatics, the emergence of large AI models has ushered in new paradigms for methodological design. The biomedical and health domain, experiencing a continuous expansion of multi-modal data, has particularly flourished since the adoption of deep learning. This paradigm shift provides a fertile ground for the development, validation, and advancement of large AI models, promising breakthroughs in various health-related areas.This article aims to provide a comprehensive review of large AI models, covering their background and delving into their diverse applications. By exploring the evolution and utilization of these models, the article seeks to shed light on the transformative potential they hold in the realm of health informatics

A Medical AI Diagnosis Platform Based on Vision Transformer for Coronavirus[2] global spread of the novel coronavirus, COVID-19, has prompted serious concerns related to its diagnosis and treatment. Presently, the predominant method for diagnosing COVID-19 involves the use of lung CT scans. However, this approach is notably inefficient, particularly when dealing with a large number of patients. The manual nature of this method not only increases the workload for healthcare professionals but can also lead to delays in patient treatment. Consequently, numerous scholars and organizations have sought to address this challenge by developing auxiliary diagnosis platforms to expedite the diagnostic process.Despite these efforts, the current platforms face limitations, primarily due to constraints in imaging technology, resulting in relatively low diagnostic accuracy. To overcome these challenges, this paper proposes the design of a medical AI dialogue diagnosis platform based on Vision Transformer technology. This approach involves distilling knowledge from traditional image recognition models to extract more valuable medical information and significantly enhance the predictive performance specifically for COVID-19.

Additionally, the paper suggests incorporating a simple dialogue system to improve the efficiency of interactions between humans and machines. This dual strategy aims to not only advance the accuracy of diagnosis through innovative AI techniques but also streamline communication for more effective man-machine interactions. In conclusion, the proposed medical dialogue system for COVID-19 detection, leveraging Vision Transformer technology and a dialogue system, demonstrates the potential to fulfill its intended purpose and holds practical significance in improving the efficiency and accuracy of COVID-19 diagnostics

AI in Medical Imaging Informatics: Current Challenges and Future Directions [3] This paper provides a comprehensive review of cutting-edge research solutions in the realm of medical imaging informatics, with a focus on clinical translation and future directions for advancing clinical practice. The review spans various aspects, including advancements in medical imaging acquisition technologies for different modalities. Emphasis is placed on the crucial need for efficient medical data management strategies within the context of artificial intelligence (AI) in the analytics of large healthcare datasets.The paper also offers a summary of contemporary and emerging algorithmic methods, particularly in the areas of disease classification and organ/tissue segmentation. Notably, it delves into AI and deep learning architectures, which have emerged as the predominant approaches in these domains. The clinical benefits of advancements in in-silico modeling, coupled with evolving 3D reconstruction and visualization applications, are thoroughly explored.In conclusion, the paper anticipates a revolution in imaging informatics across the healthcare continuum, particularly in radiology and digital pathology applications. The integration of analytics approaches, driven by associated research branches highlighted in the study, holds the promise of transforming medical diagnostics. This transformation is expected to enable more informed and accurate diagnoses, timely prognoses, and effective treatment planning, thereby contributing to the realization of precision medicine.

Short Keynote Paper: Mainstreaming Personalized Healthcare–Transforming Healthcare Through New Era of Artificial Intelligence by Ketan Paranjape , Michiel Schinkel , and Prabath Nanayakkara[4]. Medicine is undergoing a transformative shift into the digital era, propelled by data from emerging modalities such as genomics and imaging, along with inputs from wearables and the Internet of Things. This wealth of data is providing a deeper understanding of disease biology and its individualized impact, paving the way for the development of targeted therapies that can be personalized to each patient. To fully leverage this potential, technologies like Artificial Intelligence (AI) play a pivotal role in supporting predictions for tailored and individualized treatments. However, the widespread integration of AI in healthcare faces challenges that must be addressed for successful adoption. Issues such as explainability, liability, and privacy are paramount concerns. Achieving transparency in AI algorithms, making them understandable and interpretable, is crucial for gaining trust from healthcare professionals and patients. Additionally, addressing liability concerns and ensuring patient privacy are essential prerequisites for the mainstream acceptance of AI in healthcare.Solutions to these challenges include the development of explainable algorithms that demystify the decision- making process of AI systems. Furthermore, integrating AI training into medical education is a proactive step that can familiarize healthcare professionals with AI technologies, fostering their acceptance and understanding.In conclusion, as medicine embraces digital advancements and personalized treatments, the integration of AI is essential. Addressing concerns related to explainability, liability, and privacy is crucial for the widespread adoption of AI in healthcare, and employing solutions such as explainable algorithms and incorporating AI training in medical education can help overcome these challenges.

A Comprehensive Survey of the Internet of Things (IoT) and AI-Based Smart Healthcare by FATIMA ALSHEHRI AND GHULAM MUHAMMAD [5] Smart healthcare stands as a critical facet of connected living, addressing a fundamental human need. Projections indicate that smart healthcare is poised to generate substantial revenue in the coming years. This burgeoning field encompasses various components, including the Internet of Things (IoT), the Internet of Medical Things (IoMT), medical sensors, artificial intelligence (AI), edge computing, cloud computing, and next-generation wireless communication technology. The literature contains numerous papers discussing smart healthcare and healthcare in general. In this context, we present a thorough survey specifically focused on IoT- and IoMT-based edge-intelligent smart healthcare, with a primary emphasis on journal articles published from 2014 to 2020.Our comprehensive survey encompasses several key research areas, including IoT and IoMT, AI, edge and cloud computing, security, and the fusion of medical signals. By addressing these areas, we aim to provide insights into the state of the field, highlighting developments and trends during the specified timeframe.Additionally, we delve into current research challenges, acknowledging the complexities inherent in smart healthcare implementation. This survey not only serves to encapsulate the existing body of knowledge but also offers valuable perspectives on potential future research directions. By identifying areas where advancements are needed, we contribute to the ongoing discourse and evolution of smart healthcare, paving the way for innovations that can further enhance the quality and accessibility of healthcare services.

III. DETAIL OF DESIGN

AI in medicine stands as a pivotal force driving innovation in healthcare, fundamentally reshaping the landscape of diagnostics, treatment, and patient care. At its core, AI serves as the bedrock of diagnostic excellence, providing healthcare professionals with powerful tools to enhance accuracy and consistency in disease identification. By meticulously analyzing extensive datasets, including medical images and clinical information, AI significantly mitigates the risk of diagnostic errors, streamlining the diagnostic process and enabling more precise and timely interventions.

The linchpin of AI's impact lies in its capacity for early disease detection, ushering in a new era where subtle patterns and signs are discerned, making early intervention a standard practice. This capability is particularly vital in conditions where early detection plays a decisive role in shaping patient outcomes. In the realm of personalized medicine, AI plays an instrumental role by harnessing patient-specific data, such as genetics and treatment history, to craft tailored treatment plans. This precision medicine approach not only amplifies the efficacy of therapies but also minimizes the likelihood of adverse effects, ushering in a paradigm shift towards patient-centric healthcare

Healthcare professionals often face challenges in building, deploying, and integrating machine learning models into clinical workflows due to a lack of expertise in these areas. To address this gap and facilitate the practical deployment of machine learning models in daily healthcare work, AutoML (Automated Machine Learning) has been developed. AutoML automates key steps in the machine learning process, including feature selection, model selection, and hyperparameter optimization. By automating these fundamental tasks, AutoML makes it more accessible for healthcare professionals to develop machine learning models for clinical data, reducing the need for extensive input from data scientists or machine learning engineers.

In the realm of machine learning, a significant portion of a data scientist's time, approximately 80%, is typically spent on data preparation and feature engineering. This process often requires expertise from domain knowledge experts to identify the most discriminative features, providing valuable insights into the healthcare problem at hand. Various machine learning frameworks, including popular approaches like expand-reduce, have been developed to select, rank, and optimize feature engineering processes. For example, expand-reduce applies transformation functions to obtain optimal features, streamlining the feature engineering phase.

Genetic programming, based on the concept of natural evolution and a survival function, has been employed for feature construction and selection. Hyperparameters also play a crucial role in influencing model performance, and optimizing them can be considered an art that requires practical experience. Different optimization methods, such as brute force searches through grid search or random searches within a computational budget, are used to find optimal hyperparameter configurations. Guided searches, which iteratively generate new configurations based on prior performance, represent another approach. AutoML automates the entire feature engineering and hyperparameter optimization process, as well as the model selection process. This automation empowers non-technical professionals, including healthcare practitioners, to effectively use machine learning models to address healthcare challenges without requiring in-depth technical expertise.

Conclusion

In summary, the integration of Artificial Intelligence (AI) into medicine represents a groundbreaking and transformative development in the healthcare sector. AI\'s versatile applications are reshaping patient care, diagnostics, treatment, and healthcare systems at large. AI plays a pivotal role in enhancing diagnostics by providing healthcare professionals with tools that significantly improve the accuracy and precision of disease identification. This advancement contributes to better patient care and reduces the potential for diagnostic errors. A defining achievement of AI in medicine is its role in the early detection of diseases. Its capability to discern subtle patterns and indicators, especially in conditions where early intervention is crucial, has the potential to redefine patient outcomes and treatment possibilities. Personalized medicine, driven by AI, is revolutionizing healthcare by tailoring treatment plans to individual patients based on their unique characteristics. This approach maximizes the effectiveness of therapies while minimizing adverse effects, placing patients at the forefront of care. AI\'s influence in drug discovery expedites the identification of potential medications, streamlines clinical trial design, and accelerates the development of new therapies, opening doors to previously unattainable treatments. Telemedicine and remote care, facilitated by AI, have ushered in a new era of healthcare accessibility. Remote consultations and real-time patient monitoring offer patients the option of receiving care and oversight from the comfort of their homes. Resource optimization, a critical need in modern healthcare, is central to AI\'s impact. AI\'s predictive analytics identify high-risk patient populations and forecast healthcare trends, leading to more efficient resource allocation. In the realm of mental health support, AI-driven virtual assistants contribute to reduced stigma and enhanced accessibility. These assistants provide counseling, information, and guidance, offering crucial assistance to individuals facing emotional or psychological challenges. The overarching impact of AI in medicine is shaping a future where healthcare is more accurate, personalized, and accessible.

References

[1] Large AI Models in Health Informatics: Applications, Challenges, and the Future [2] A Medical AI Diagnosis Platform Based on Vision Transformer for Coronavirus [3] AI in Medical Imaging Informatics: Current Challenges and Future Directions [4] Short Keynote Paper: Mainstreaming Personalized Healthcare–Transforming Healthcare Through New Era of Artificial Intelligence by Ketan Paranjape , Michiel Schinkel , and Prabath Nanayakkara [5] A Comprehensive Survey of the Internet of Things (IoT) and AI-Based Smart Healthcare by FATIMA ALSHEHRI AND GHULAM MUHAMMAD [6] https://www.ibm.com/topics/artificial-intelligencemedicine#:~:text=How%20is%20artificial%20in telligence%20used,health%20 outcomes%20and% 20patient%20experie nces. [7] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7640807/ [8] https://en.wikipedia.org/wiki/Artificial_intelligence_in_healthcare [9] ]https://www.datarevenue.com/en-blog/artificial-intelligence-in-medicine [10] https://en.wikipedia.org/wiki/Internet_of_things [11] https://www.adafruit.com/product/386 [12] https://en.wikipedia.org/wiki/ESP8266 [13] https://thingspeak.com/

Copyright

Copyright © 2024 Devika Sawant. 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.