AI for Public Health and Population Health Management

1. Introduction

AI technologies such as natural language processing, machine learning, and data analytics have demonstrated exceptional capabilities in a wide range of fields, including healthcare. By analysing massive amounts of patient data, genetic data, research studies, and drug databases, AI can improve drug discovery, enable targeted therapy, optimise the administration of medications, identify adverse reactions along with security queries, offer real-time monitoring, personalised authorization assistance, and facilitate remote medical treatment. While looking forward to the next a century of DRP leadership with AI integration, it's clear that these advancements have a chance to revolutionise how substances are recommended, administered, and examined, ultimately improving patient safety, therapy efficacy, and overall standard of care. However, there are challenges to this integration, including safeguarding information, adherence to laws, and comparable availability of AI-powered products. Some of the strategies for overcoming these challenges include promoting cooperation among medical professionals, technology developers, and lawmakers that would help to capitalise on AI's full potential in revolutionising drug administration and improving patient-centred care in the future (Fatima et al., 2023; Leechun, 2015). Drug management entails a variety of tasks, including as providing suitable prescriptions, reducing medication mistakes, monitoring interactions among medications and adverse effects, and encouraging patient medication adherence. The introduction of AI (Artificial Intelligence) technology has resulted in substantial breakthroughs in the landscape of drug administration in Indian Cotecare, with the potential for transforming the way medications are prescribed, delivered, and monitored. In the ever-changing face of the medical field, there are several sectors where the use of AI technology has the potential to generate favourable outcomes. This investigation on the potential influence of artificial intelligence attempts to identify important sectors where these revolutionary innovations might dramatically improve healthcare delivery and outcome for patients. AI-powered healthcare conversation bots will grow more sophisticated and accessible. Such virtual assistants will provide patients with accurate and customised data on their drugs, as well as potential through utilising the possibilities of AI in specialised healthcare applications, may open up fresh opportunities for improved diagnosis, customised treatment plans, more quickly administrative operations, and improved patient care. Through the investigations, it is anticipated to shed the spotlight on the exciting possibilities of AI for developing a more efficient, effective, and patient-focused healthcare ecosystem. Based on the data available in this arena, the most significant health care providers can be identified and how they help in controlling drug-related difficulties can be recognized (Grover et al., 2024, Ahuja, 2019). The healthcare industry is capable of employing these resources to handle and oversee a variety of problems associated with drugs effects. Artificial Intelligence (AI) may emulate human intelligence on various scales. Deep learning and machine learning (DL) (ML) are AI segments that allow systems to learn. To gain insight from data at a particularly basic level. DL is a form of ML that employs greater complexity models are built using structures. Traditional strategies for artificial intelligence (for example, expert systems), Obemeyer and Emanuel state that can “apply general concepts of medicine” and employ them to new patients” in a specific way comparable to first-year physicians an entire year of citizenship is required. ML derives rules from facts, analogous to what a doctor might encounters throughout his time in residence Patients shall be able to engage with one another. Such virtual assistants to clear up concerns, record symptoms, and find solutions instructions on drug administration, so allowing individuals to actively participate optimise their health and therapy journey. AI will help with personalised medicine dosing suggestions. Algorithms based on AI are going to be able to discover the best therapy options by analysing particular patient records, genetic data, and treatment responses dose for each patient. This kind of precise dosage will maximise guaranteeing pharmacological effectiveness while minimising the likelihood of unwanted consequences ensure patients receive the appropriate dose of medicine based on their needs certain requirements. Fig1 depicts the emerging technologies based on the Internet of Things (IoT) or (AI) to combat infectious illnesses such as COVID-19 and dengue, worldwide establishing framework; radio waves verification; early detection and accordance mechanism; synthetic artificial neural system; decision tree; and naive Bayes theory are all examples of artificial neural networks (Hinton, 2018). In the world of healthcare, artificial intelligence (AI) has become a disruptive force that is changing how medical practitioners identify, treat, and manage illnesses. The history of artificial intelligence (AI) in healthcare is covered in this chapter, along with its development, present uses, and prospects for the future. The 1960s saw the first attempts to apply AI to healthcare, with an emphasis on systems of experts and tools for supporting decisions. Rule-based systems and simple algorithms were the main tools used in early applications for activities like patient monitoring and diagnosis. More advanced AI applications in healthcare were made possible by the development of algorithms for machine learning in the late 20th century. algorithms with the ability to learn from data and make judgements or predictions. Methods for producing and comprehending human language, which make jobs like clinical documentation and medical transcription possible. Artificial intelligence (AI) systems that can read and analyse visual data are essential for jobs like medical imaging research. Robotics and AI integration for surgery, rehabilitation, and virtual patient care.AI algorithms are improving the efficiency and accuracy of activities including organ segmentation, pathology analysis, and tumour identification. AI-powered solutions evaluate patient data and suggest treatments, assisting healthcare professionals in making decisions based on solid evidence. By evaluating massive information, forecasting drug-target interactions, and improving molecular structures, artificial intelligence (AI) speeds up the drug discovery process. Large volumes of data may be quickly and accurately analysed by AI algorithms, which can result in more precise diagnosis and individualised treatment regimens. AI improves health outcomes and lowers death rates by enabling early identification, effective therapies, and proactive interventions. Healthcare workflows are streamlined by AI-driven automation and optimisation, which reduces costs associated with evaluation, planning of therapy, and resource allocation. As AI is incorporated into healthcare systems more and more, worries about algorithm bias, data privacy, and liability arise (Secinaro, 2021: Cote, 2021; Das, 2023).