Artificial Intelligence: Basics and Recent Health Care Protocols in Dentistry

The integration of artificial intelligence (AI) in healthcare has the potential to revolutionize the industry by improving patient outcomes and increasing efficiency. However, the rapid development and implementation of AI technologies raise complex legal issues and challenges. This article explores the key legal aspects of AI integration in healthcare, including data privacy and security, liability and accountability, intellectual property, and regulatory compliance. It examines relevant international and national legal instruments, regulations, and guidelines, as well as industry-specific standards that apply to AI in healthcare. The study also analyzes case studies and practical applications to highlight legal challenges and resolutions, lessons learned, and best practices. The discussion addresses the implications of the results, comparing the legal landscape for AI in healthcare to other industries and countries and highlighting potential future legal developments and challenges. The conclusion summarizes key findings, offers recommendations for integrating AI in healthcare systems while addressing legal concerns, and proposes future directions for legal research and policy development in the context of AI and healthcare. This comprehensive analysis aims to inform healthcare providers, AI developers, and policymakers on the legal landscape surrounding AI in healthcare, providing valuable insights to navigate this complex domain and harness the potential of AI to transform healthcare delivery.

The Promise Fund and Hologic Inc. have partnered to expand access to AI-supported breast cancer screening exams in a medically underserved population. As part of this initiative, patient focus groups and patient navigator interviews were conducted to explore patient experiences and perceptions related to breast health, mammography, and artificial intelligence (AI) in healthcare. These interviews were held, virtually, between February and March 2024, and focus groups were held, in-person, on April 9-10, 2024, with participants at FoundCare and the Community Health Center. FoundCare, a federally qualified health center (FQHC), and Community Health Center, a free clinic, both collaborate with the Promise Fund in promoting access to women’s breast and cervical health screenings, diagnostic follow up and cancer care services. The in-person sessions, attended by twenty patients, and the virtual interviews, attended by six patient navigators, were designed to represent diverse linguistic, racial, and cultural backgrounds, including multiple Spanish-speaking communities from Mexico, Dominican Republic, Guatemala, Chile, and Venezuela. All patient navigators were adult women from various free clinics and FQHCs. Patient participants were adult females, all at or below 200% of the Federal Poverty Guidelines. Key themes from the discussions included: 1. Health Information Sources: Patients primarily relied on the internet, community health centers, medical visits, family, and cultural community communications for health information. Some patients expressed difficulty in keeping up with health information and emphasized the importance of direct medical consultations with providers and preventive care. Patient navigators were a critical source of information once the patient could access and be connected to that resource. Clinical health and more upstream social services, such as transportation to health appointments and family food security, were also critical needs that patient navigators were key in facilitating. 2. Mammogram Experiences: Experiences with mammograms varied, with some patients reporting pain and discomfort, particularly those with implants. Regular screenings, family history of breast cancer, and personal vigilance were common motivators for mammograms. Financial barriers, insurance issues, and the need for patient advocacy were frequently highlighted. 3. AI in Healthcare: Patients had mixed levels of awareness and understanding of AI. Many associated AI with advanced diagnostics and potential improvements in early disease detection and surgical precision. Concerns included the potential loss of personal interactions with healthcare providers, privacy issues, and the fear of job displacement. However, patients had key opinions on AI that were more favorable than anticipated. There was optimism about AI’s ability to enhance diagnostic accuracy, patient experience, provider trustworthiness, and treatment outcomes. Additionally, both patients and patient navigators preferred that physicians and AI work together. Patient navigators were even more familiar with AI. Given the critical role patient navigators hold, their knowledge may play an integral role in improving patient education and access to new technologies in the future. 4. Whole Health Experience: Patients expressed a desire for a more comfortable and supportive mammography process, greater access to care, enhanced trust between patients and providers, and comprehensive information sharing across healthcare providers. Education on breast cancer, early detection, and AI in healthcare was identified as a critical need. Conclusion: The findings underscore the importance of trustworthy, patient-centered approaches in healthcare, particularly in the integration of AI technologies. Enhancing patient education and provider transparency, improving access to preventive services, and maintaining the human element in healthcare are essential for optimizing patient outcomes and satisfaction. Citation Format: Nancy Brinker. Patient Experience and Perceptions Related to Breast Health, Mammography and Artificial Intelligence in Healthcare [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2024; 2024 Dec 10-13; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(12 Suppl):Abstract nr P3-05-11.

Objectives The use of artificial intelligence (AI) in healthcare is a growing field of research and clinical application. The views of the general public, ie future healthcare users, need to be surveyed and interpreted so that researchers and the public have a shared understanding of the appropriate use of AI. Currently, there is only limited data on the public’s views. Methods An anonymous, quantitative questionnaire was administered as part of a public exhibition on AI. The questionnaire was based on previously validated questions designed to assess respondents’ views on the use of AI in healthcare. Brief demographic data were also collected. Results The population surveyed was more diverse and younger than the general UK population (65% white, 45% aged 18-29). Respondents were largely comfortable with the application of AI in healthcare: 80% felt positively about its use, 56% thought it would be safe. 70% did not feel that it would replace doctors, and most would not be happy for AI to make decisions without considering their feelings. Conclusions Our study shows that the population we surveyed, particularly young future healthcare users, are comfortable with the use of AI in healthcare, but do not see it as a replacement for doctors. Advances in knowledge This paper highlights views from the general public on the use of AI in healthcare, which is largely under researched.

Artificial Intelligence in the American Healthcare Industry: Looking Forward to 2030

Artificial intelligence (AI) is the ability of a computer program or machine to think or learn that possess human-like intelligence. These computing devices use this intelligence to provide services such as speech recognition, natural language processing and identifying disease in healthcare. To work efficiently, AI requires adequate data that is used to train systems. The efficiency of any AI system depends on the availability of this data. This article is mainly focused on recent advents in the technology of Artificial Intelligence. The importance of AI in healthcare is identified and described in this report. The applications of Artificial Intelligence in healthcare such as clinical care, medical research, drug research and public healthcare are briefly discussed here. The purpose of this article is to demonstrate that artificial intelligence is being used in all domains of life and particularly in the field of healthcare. This report presents the role of Artificial Intelligence in healthcare.

This all-inclusive research paper plunges into the transformative nature of artificial intelligence (AI) in healthcare, emphasizing its ability to change patient care and administrative activities. The investigation examines systematically how AI technologies are creating major improvements towards patients’ outcomes and enhanced operational efficiency within the healthcare systems. From diagnostics to personalized treatment plans and administrative tasks, AI is being integrated using sophisticated algorithms in numerous aspects of healthcare delivery. One major focus is improving diagnostic accuracy. AI-backed diagnostic tools such as image recognition software as well as predictive models have outperformed traditional methods in medical condition identification and diagnosis. For instance, artificial intelligence algorithms show exceptional skills in analysing medical images resulting to earlier detection as well as accurate diagnoses of diseases like cancer, cardiovascular conditions among others neurological disorders. These enhancements not only allow for timely intervention but they significantly reduce instances where diagnosis goes wrong hence enhancing patient prognosis while reducing health costs drastically. The ability to customize personalized therapy is yet another essential aspect of AI in healthcare. With such information as genetic data, medical records and lifestyle factors, and other patient information, AI systems can develop individualized treatment plans. It is for this reason that personalization of treatments enhances their efficiency while reducing the risk of any adverse effects as well as ensuring patients’ adherence to prescribed therapies. Besides, it is important to note that AI-driven predictive models are helpful in identifying individuals with a higher probability of developing specific conditions thereby allowing interventions aimed at preventing diseases and bettering overall population health. AI has also transformed administrative tasks within hospitals besides its clinical applications. For example, AI driven automation is now used to streamline scheduling of appointments or billing processes or even storage of patient records which are normally considered routine administrative works. In addition, through these means there is reduced burden on administrative duties for healthcare workers who continue to perform them with increased precision and timeliness. The net effect is that time and resources can be redirected towards more direct patient care thus improving service quality across the board’s entire system

Purpose (stating the main purposes and research question) Anthropogenic resource use contributes to pollution, violent conflict over scarce resources, loss of biodiversity, and diminished quality of life for humans. Moreover, the “safe” amount of carbon dioxide—350 parts per million—has been exceeded. The health care industry is responsible for 4–5% of total world emissions,[i] which is similar to the global food sector.[ii] Health care carbon emissions come from health care infrastructures, supply chains and health care delivery. Increasingly, health care delivery is reliant on technologies which require the use of artificial intelligence to provide supportive care, such as triage algorithms, electronic patient records, and robotics.[iii] While these technological innovations have advanced health care significantly, they also contribute to the negative effects on the environment, among others, through carbon emissions. The environmental impacts of artificial intelligence (AI) in health care—in particular—are understudied. This research seeks to fill this gap. Methods Our team ran an exploratory search in Scopus and PubMed to identify studies that integrate environmental sustainability, artificial intelligence, and health. Results Our research initially yielded 735 studies. 77 of these studies focused on an environmental concern of a health technology or AI-application in a health care setting, but most of the articles in this subset addressed lowering energy consumption of a specific technology, such as a sensor or monitoring technology. Conclusions While there have been studies looking at AI in health care; sustainability in AI; and sustainability in health care, little attention has been paid to the interface between all three. [i] Karliner, J., Slotterback, S., Boyd, R., Ashby, B., & Steele, K. 2019. Health Care’s Climate Footprint: How the Health Sector Contributes to the Global Climate Crisis and Opportunities for Action Healthcare Without HarmARUP; September. [ii] Pichler, P. P., Jaccard, I. S., Weisz, U., & Weisz, H. 2019 International Comparison of Health Care Carbon Footprints, Environmental Research Letters 14, no. 6: 064004. [iii] Khaliq, Abdul, Ali Waqas, Qasim Ali Nisar, Shahbaz Haider, and Zunaina Asghar. 2022. Application of AI and robotics in hospitality sector: A resource gain and resource loss perspective. Technology in Society 68: 101807.

Artificial intelligence (AI) has been developing fleetly in recent times in terms of software algorithms, tackle preparation, and operations in a vast number of areas. In this review, we epitomize the rearmost of operations of AI in biomedicine, including complaint diagnostics, living backing, biomedical information processing, and biomedical exploration. The end of this review is to keep track of new scientific accomplishments, to understand the vacuity of technologies, to appreciate the tremendous eventuality of AI in biomedicine, and to give experimenters in affiliated field’s alleviation. It can be asserted that, just like AI itself, the operation of AI in biomedicine is still in its early stage. New progress and improvements will continue to push the frontier and widen the compass of AI operations, and fast developments are envisaged in the near future.AI in healthcare is an umbrella term to describe the application of machine learning (ML) algorithms and other cognitive technologies in medical settings. In the simplest sense, AI is when computers and other machines mimic human cognition, and are capable of learning, thinking, and making decisions or taking actions. Artificial intelligence (AI) is gradationally changing medical practice. With recent progress in digitized data accession, machine literacy and computing structure, AI operations are expanding into areas that were preliminary allowed to be only the fiefdom of mortal experts. In this Review composition, we outline recent breakthroughs in AI technologies and their biomedical applications, identify the challenges for further progress in medical AI systems, and epitomize the profitable, legal and counteraccusations of AI in healthcare.

BackgroundArtificial intelligence technology can be applied in several aspects of healthcare delivery and its integration into the Nigerian healthcare value chain is expected to bring about new opportunities. This study aimed at assessing the knowledge and perception of healthcare professionals in Nigeria regarding the application of artificial intelligence and machine learning in the health sector.MethodsA cross-sectional study was undertaken amongst healthcare professionals in Nigeria with the use of a questionnaire. Data were collected across the six geopolitical zones in the Country using a stratified multistage sampling method. Descriptive and inferential statistical analyses were undertaken for the data obtained.ResultsFemale participants (55.7%) were slightly higher in proportion compared to the male respondents (44.3%). Pharmacists accounted for 27.7% of the participants, and this was closely followed by medical doctors (24.5%) and nurses (19.3%). The majority of the respondents (57.2%) reported good knowledge regarding artificial intelligence and machine learning, about a third of the participants (32.2%) were of average knowledge, and 10.6% of the sample had poor knowledge. More than half of the respondents (57.8%) disagreed with the notion that the adoption of artificial intelligence in the Nigerian healthcare sector could result in job losses. Two-thirds of the participants (66.7%) were of the view that the integration of artificial intelligence in healthcare will augment human intelligence. Three-quarters (77%) of the respondents agreed that the use of machine learning in Nigerian healthcare could facilitate efficient service delivery.ConclusionThis study provides novel insights regarding healthcare professionals' knowledge and perception with respect to the application of artificial intelligence and machine learning in healthcare. The emergent findings from this study can guide government and policymakers in decision-making as regards deployment of artificial intelligence and machine learning for healthcare delivery.

Background and objective: The rapid expansion of artificial intelligence (AI) in healthcare has resulted in substantial advances in diagnostics, prognostics, clinical decision support systems, and patient monitoring. Despite promising performance, many AI-based systems remain insufficiently understood by clinicians and patients due to their “black-box” nature. This lack of transparency may undermine trust, hinder acceptance, and limit safe integration into routine clinical practice. Explainable artificial intelligence (XAI) has emerged as a response to these challenges by enabling human-interpretable explanations of algorithmic decisions. The objective of this narrative review is to synthesize current evidence on XAI in healthcare, with particular emphasis on its technical foundations, clinical applications, influence on trust and decision-making, and broader social and ethical implications. Scope of review: This review synthesizes literature published between 2019 and 2025 addressing explainability in medical AI. The analysis includes methodological studies, clinical evaluations, human–computer interaction research, and social science investigations related to transparency, trust, accountability, and acceptance of AI systems. Relevant publications were identified through structured searches of PubMed, MEDLINE, Scopus, and Google Scholar using keywords related to explainable AI, interpretability, ethics, trust, and clinical decision support. Findings: XAI methods—including feature attribution, model simplification, counterfactual explanations, and visualization techniques—demonstrate potential to enhance clinician understanding of AI outputs and increase confidence in algorithm-assisted decisions. Evidence suggests that explainability may support diagnostic accuracy, reduce automation bias, and facilitate error detection. However, explainability alone does not ensure trust. Clinical context, user expertise, organizational culture, and regulatory frameworks play critical roles in shaping the adoption and appropriate use of explainable systems. Empirical research addressing patient perspectives remains limited. Conclusions: Explainable AI constitutes an important step toward the responsible and socially acceptable integration of intelligent systems in healthcare. While XAI can enhance transparency and trust, its effectiveness depends on thoughtful design, contextual adaptation to clinical workflows, and alignment with user needs. Further interdisciplinary research is required to standardize explainability approaches, evaluate their real-world impact on clinical outcomes, and address the ethical, legal, and societal challenges associated with medical AI.

BackgroundGlobally, the size and proportion of older adults are growing at an unprecedented rate. Given the rising incidence of health complications among older adults, it is critical to understand opportunities for improving the delivery of health care. Artificial intelligence (AI) is a promising technology that many argue has the potential to improve patient care, particularly by supporting and assisting older adults in diagnosing, managing, and treating health conditions. Although there have been significant studies on AI innovations in health care, there is an overall lack of literature on patient perceptions of the implementation of AI, specifically in older adult populations. This study addresses this gap in research by engaging older adults and focusing on AI and healthy aging.MethodUtilizing an appreciative inquiry framework, the data collection consists of interviews with older adults to develop an understanding of how older adults perceive the use and consideration of AI in Northern Ontario, Canada. We analyze the findings using Braun and Clark's (2022) thematic analysis six‐step approach.ResultThe findings from this study highlight how AI technologies can better support older adults in Northern Ontario. The study highlights identifying older adults' perceptions toward AI in healthcare settings, as well as the perceived barriers and facilitators to AI utilization in Adult Day Centers/Older Adults Centers and Retirement Homes. The study contributes to the development of specialized educational materials or training programs designed to raise awareness and practice, encourage advocacy, improve comfort levels with AI technologies, and ultimately enhance older adult patient care. The findings will be informed to policymakers and stakeholders for adjusting policy reform and analysis. This study shed light on how older adults feel toward AI, how they envision improvements, and what improvements could be made for future health practices using AI technologies.ConclusionOverall, this research could help identify factors contributing to better AI‐enabled care of older adults, and it could shed light on how AI technologies could evolve to better meet the needs of older adults.

The implementation of artificial intelligence (AI) in mental healthcare offers a potential solution to some of the problems associated with the availability, attractiveness, and accessibility of mental healthcare services. However, there are many knowledge gaps regarding how to implement and best use AI to add value to mental healthcare services, providers, and consumers. The aim of this paper is to identify challenges and opportunities for AI use in mental healthcare and to describe key insights from implementation science of potential relevance to understand and facilitate AI implementation in mental healthcare. The paper is based on a selective review of articles concerning AI in mental healthcare and implementation science. Research in implementation science has established the importance of considering and planning for implementation from the start, the progression of implementation through different stages, and the appreciation of determinants at multiple levels. Determinant frameworks and implementation theories have been developed to understand and explain how different determinants impact on implementation. AI research should explore the relevance of these determinants for AI implementation. Implementation strategies to support AI implementation must address determinants specific to AI implementation in mental health. There might also be a need to develop new theoretical approaches or augment and recontextualize existing ones. Implementation outcomes may have to be adapted to be relevant in an AI implementation context. Knowledge derived from implementation science could provide an important starting point for research on implementation of AI in mental healthcare. This field has generated many insights and provides a broad range of theories, frameworks, and concepts that are likely relevant for this research. However, when taking advantage of the existing knowledge basis, it is important to also be explorative and study AI implementation in health and mental healthcare as a new phenomenon in its own right since implementing AI may differ in various ways from implementing evidence-based practices in terms of what implementation determinants, strategies, and outcomes are most relevant.Plain Language Summary: The implementation of artificial intelligence (AI) in mental healthcare offers a potential solution to some of the problems associated with the availability, attractiveness, and accessibility of mental healthcare services. However, there are many knowledge gaps concerning how to implement and best use AI to add value to mental healthcare services, providers, and consumers. This paper is based on a selective review of articles concerning AI in mental healthcare and implementation science, with the aim to identify challenges and opportunities for the use of AI in mental healthcare and describe key insights from implementation science of potential relevance to understand and facilitate AI implementation in mental healthcare. AI offers opportunities for identifying the patients most in need of care or the interventions that might be most appropriate for a given population or individual. AI also offers opportunities for supporting a more reliable diagnosis of psychiatric disorders and ongoing monitoring and tailoring during the course of treatment. However, AI implementation challenges exist at organizational/policy, individual, and technical levels, making it relevant to draw on implementation science knowledge for understanding and facilitating implementation of AI in mental healthcare. Knowledge derived from implementation science could provide an important starting point for research on AI implementation in mental healthcare. This field has generated many insights and provides a broad range of theories, frameworks, and concepts that are likely relevant for this research.

Artificial intelligence (AI) in health-care is an emerging trend identified as a collection of technologies, programmed to sense, comprehend, act, and learn. Its performance is appreciated for the administrative and clinical healthcare functions and also for research and training purposes. AI includes natural brainpower, instrument learning, deep learning, neural networks, robotics, multiagent organisms, modernization in healthcare, and indistinct reasoning. Health includes healthcare, diagnostics, sanatoriums, and telemedicine, medications, therapeutic utensils and supplies, health assurance, and medical data. Healthcare curriculums are restricted by the unavailability of clinicians and inadequate dimensions, and AI amends these insufficiencies. The importance of AI in India has become a key technology for improving the effectiveness, value, cost, and reach of healthcare. The ethical issues for applying AI in healthcare must be analyzed and standardized. The encounters to the use of AI in health-care were recognized primarily through an analysis of literature, interviews, and roundtable inputs.

Artificial intelligence (AI) is a system that aims to bring human thinking ability to machines and thus solve complex tasks more easily and can continuously improve itself with the data it collects. AI technologies are used in many fields, such as education, media, banking, and the defense industry. In recent years, AI has begun to manifest itself in various fields of health services, from diagnosis to treatment and patient follow-up. AI algorithms can facilitate patients' access to healthcare services, provide remote patient monitoring, shorten the diagnosis process of diseases, provide patient-specific treatment recommendations, or allow physicians to improve their practices. Thus, it can improve patient care and increase patient satisfaction, reduce costs, and speed up healthcare services. Increasing telemedicine applications during the pandemic contributed to the acceleration of the utilization of AI in healthcare, and AI-based algorithms for diagnosing and treating diseases began to be developed rapidly. With the increasing prevalence of wearable technologies and the introduction of electronic health records, there has been a tremendous explosion in individual health data. AI has contributed to health services in collecting and processing this rapidly increasing data. Although AI has a promising future in health, it also brings many ethical problems. AI systems make decisions based on the data they are trained on. If there is not enough data diversity or if biased data is used, AI systems may give inaccurate predictions or learn and reproduce these preconceptions. This manuscript is a literature review examining the impacts of AI in the healthcare sector and discusses the history of AI, AI studies applied for the diagnosis and treatment of diseases, telemedicine and preventive medicine applications, and the disadvantages of AI.

This article aims to examine the philosophy of Artificial Intelligence (AI) in healthcare and present a novel framework that could bridge philosophy, ethics, and leadership to promote the responsible and human-centred integration of AI. Moving beyond efficiency and innovation, it explores the deeper philosophical, moral, and human dimensions of AI's evolving role in care delivery. The proposed framework incorporates teleology, ontology, epistemology, axiology, and ethics to provide a structured foundation for guiding AI development, implementation, and governance through purpose, knowledge, values, and moral action. Grounded in these principles, it highlights the leadership approaches that foster accountability, organizational readiness, and ethical stewardship in AI adoption. These insights inform the development of a framework designed to align AI with human values and to promote compassionate, ethical, and sustainable applications that enhance healthcare outcomes while preserving the essence of human care.