Artificial Intelligence applications in healthcare: A bibliometric and topic model-based analysis

Background: The rapid advancement of artificial intelligence (AI) in various sectors has revolutionized problem-solving approaches, particularly in healthcare. Developed countries have invested significantly in AI research and applications in healthcare, while low-income countries such as Somalia lag due to various challenges. This study aimed to assess the knowledge, attitudes, and practices (KAP) of AI among healthcare professionals in Somalia and explore their familiarity with AI technologies and practices. Methods: A cross-sectional study was conducted from January 1, 2024, to March 15, 2024, among 441 healthcare professionals in Somalia, using an online questionnaire. The questionnaire assessed the participants' sociodemographic information, knowledge of AI applications in healthcare, attitudes towards AI capabilities, and practical experience with AI in healthcare. Results: Most participants demonstrated good knowledge of AI (67.6%) and a positive attitude towards its potential in healthcare (80.5%). However, a significant gap was observed in the practical application of AI, with 79.1% of the respondents reporting poor practice. The study also found that sociodemographic factors such as age, gender, and income level did not significantly influence knowledge or attitudes towards AI but did affect its practical use. Professionals in certain fields such as midwifery and public health are more likely to use AI in their work. Knowledge and attitude scores were also significant predictors of practice scores. Conclusion: Healthcare professionals in Somalia demonstrate a good understanding and positive attitudes towards AI but encounter challenges in its practical application. This study emphasizes the necessity of an enhanced infrastructure, technical expertise, and data access to fully utilize AI's potential in healthcare. It also highlights the significance of addressing ethical considerations and implementing regulations to ensure responsible use of AI in healthcare. Efforts are needed to translate awareness and receptiveness into effective practice, which could result in a better healthcare system.

The pace of artificial intelligence (AI) in healthcare is changing the mode of physicians to diagnose, treat, and manage patients. This chapter affords an inclusive meta-synthesis of existing research on AI applications in healthcare, focusing on quantitative assessment of outcomes and effectiveness in different areas such as disease identification, treatment planning, and patient management of strengths and weaknesses. It also provides high-level analysis. This review collects results from AI-based technologies with a medical basis, including extensive information. Improvements have also been made in the quality of healthcare. The focus on quantitative measures leads to a clearer understanding of the magnitude of the impact of AI applications compared to conventional medical practices and the statistical significance of that in this development. The chapter concludes by highlighting the future potential of AI in healthcare, emphasizing the importance of multidisciplinary collaboration between AI developers, healthcare professionals, and policymakers. The synthesis of quantitative outcomes suggests that AI has the probability to transform healthcare distribution, particularly in high-volume, data-rich areas such as radiology, genomics, and patient monitoring. The results contribute to the growing body of literature on AI in healthcare and provide useful insights for clinicians, healthcare administrators, and technology developers who want to use AI's potential to improve patient outcomes. Nonetheless, in order to fully reap its aids, matters connected to data quality, incorporation, and beliefs must be taken into deliberation. In precipitate, this meta-synthesis deals with an inclusive, measurable analysis of AI claims in healthcare, highlighting the transformative possibility of AI technologies while recognizing the difficulties that must be overwhelmed to attain extensive adoption.

Artificial intelligence (AI) is widely considered to be the new technical advancement capable of a large-scale modernization of health care. Considering AI's potential impact on the clinician-patient relationship, health care provision, and health care systems more widely, patients and the wider public should be a part of the development, implementation, and embedding of AI applications in health care. Failing to establish patient and public engagement and involvement (PPIE) can limit AI's impact. This study aims to (1) understand patients' and the public's perceived benefits and challenges for AI and (2) clarify how to best conduct PPIE in projects on translating AI into clinical practice, given public perceptions of AI. We conducted this qualitative PPIE focus-group consultation in the United Kingdom. A total of 17 public collaborators representing 7 National Institute of Health and Care Research Applied Research Collaborations across England participated in 1 of 3 web-based semistructured focus group discussions. We explored public collaborators' understandings, experiences, and perceptions of AI applications in health care. Transcripts were coanalyzed iteratively with 2 public coauthors using thematic analysis. We identified 3 primary deductive themes with 7 corresponding inductive subthemes. Primary theme 1, advantages of implementing AI in health care, had 2 subthemes: system improvements and improve quality of patient care and shared decision-making. Primary theme 2, challenges of implementing AI in health care, had 3 subthemes: challenges with security, bias, and access; public misunderstanding of AI; and lack of human touch in care and decision-making. Primary theme 3, recommendations on PPIE for AI in health care, had 2 subthemes: experience, empowerment, and raising awareness; and acknowledging and supporting diversity in PPIE. Patients and the public can bring unique perspectives on the development, implementation, and embedding of AI in health care. Early PPIE is therefore crucial not only to safeguard patients but also to increase the chances of acceptance of AI by the public and the impact AI can make in terms of outcomes.

• The German population has favorable attitudes toward artificial intelligence (AI). • AI applications in healthcare are rated more positive than AI in the workplace. • Cognitive evaluations of AI are more positive than emotional appraisals. • Prior experiences with AI in Germany are limited. • Demographic differences are generally small. Artificial intelligence (AI) increasingly affects individuals’ private and professional lives. Importantly, both the acceptance and adoption of new AI technologies in society is heavily impacted by the attitudes that people hold; yet, there is currently limited information on how people perceive and intend to use AI at the national and demographic levels. Therefore, this study examined a random sample of 1,098 German adults to assess their attitudes, experiences, and usage intentions regarding AI in work, healthcare, and education. The findings indicated that respondents generally held favorable attitudes towards AI, with AI applications in healthcare receiving more positive evaluations than AI in the context of work. Moreover, cognitive evaluations of AI were more positive than emotional or behavioral appraisals. Prior experiences with AI were, however, limited, particularly in healthcare and education. Demographic differences were generally small. Taken together, these findings demonstrate that, in Germany, AI is currently widely accepted in different domains, although most people have little first-hand experience with it. These insights can inform policymakers and stakeholders who care about the proliferation of AI in society.

Health professionals' perspectives on the use of Artificial Intelligence in healthcare: A systematic review.

Regulation of health technologies must be rigorous, instilling trust among both healthcare providers and patients. This is especially important for the control and supervision of the growing use of artificial intelligence in healthcare. In this commentary on the accompanying piece by Van Laere and colleagues, we set out the scope for applying artificial intelligence in the healthcare sector and outline five key challenges that regulators face in dealing with these modern-day technologies. Addressing these challenges will not be easy. While artificial intelligence applications in healthcare have already made rapid progress and benefitted patients, these applications clearly hold even more potential for future developments. Yet it is vital that the regulatory environment keep up with this fast-evolving space of healthcare in order to anticipate and, to the extent possible, prevent the risks that may arise.

Artificial intelligence (AI) aims to mimic human cognitive functions. It is bringing a paradigm shift to healthcare, powered by increasing availability of healthcare data and rapid progress of analytics techniques....

BackgroundSeveral studies highlight the effects of artificial intelligence (AI) systems on healthcare delivery. AI-based tools may improve prognosis, diagnostics, and care planning. It is believed that AI will be an integral part of healthcare services in the near future and will be incorporated into several aspects of clinical care. Thus, many technology companies and governmental projects have invested in producing AI-based clinical tools and medical applications. Patients can be one of the most important beneficiaries and users of AI-based applications whose perceptions may affect the widespread use of AI-based tools. Patients should be ensured that they will not be harmed by AI-based devices, and instead, they will be benefited by using AI technology for healthcare purposes. Although AI can enhance healthcare outcomes, possible dimensions of concerns and risks should be addressed before its integration with routine clinical care.MethodsWe develop a model mainly based on value perceptions due to the specificity of the healthcare field. This study aims at examining the perceived benefits and risks of AI medical devices with clinical decision support (CDS) features from consumers’ perspectives. We use an online survey to collect data from 307 individuals in the United States.ResultsThe proposed model identifies the sources of motivation and pressure for patients in the development of AI-based devices. The results show that technological, ethical (trust factors), and regulatory concerns significantly contribute to the perceived risks of using AI applications in healthcare. Of the three categories, technological concerns (i.e., performance and communication feature) are found to be the most significant predictors of risk beliefs.ConclusionsThis study sheds more light on factors affecting perceived risks and proposes some recommendations on how to practically reduce these concerns. The findings of this study provide implications for research and practice in the area of AI-based CDS. Regulatory agencies, in cooperation with healthcare institutions, should establish normative standard and evaluation guidelines for the implementation and use of AI in healthcare. Regular audits and ongoing monitoring and reporting systems can be used to continuously evaluate the safety, quality, transparency, and ethical factors of AI-based services.

While there is significant enthusiasm in the medical community about the use of artificial intelligence (AI) technologies in healthcare, few research studies have sought to assess patient perspectives on these technologies. We conducted 15 focus groups examining patient views of diverse applications of AI in healthcare. Our results indicate that patients have multiple concerns, including concerns related to the safety of AI, threats to patient choice, potential increases in healthcare costs, data-source bias, and data security. We also found that patient acceptance of AI is contingent on mitigating these possible harms. Our results highlight an array of patient concerns that may limit enthusiasm for applications of AI in healthcare. Proactively addressing these concerns is critical for the flourishing of ethical innovation and ensuring the long-term success of AI applications in healthcare.

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.

BackgroundThe integration of artificial intelligence (AI) holds substantial potential to alter diagnostics and treatment in health care settings. However, public attitudes toward AI, including trust and risk perception, are key to its ethical and effective adoption. Despite growing interest, empirical research on the factors shaping public support for AI in health care (particularly in large-scale, representative contexts) remains limited.ObjectiveThis study aimed to investigate public attitudes toward AI in patient health care, focusing on how AI attributes (autonomy, costs, reliability, and transparency) shape perceptions of support, risk, and personalized care. In addition, it examines the moderating role of sociodemographic characteristics (gender, age, educational level, migration background, and subjective health status) in these evaluations. Our study offers novel insights into the relative importance of AI system characteristics for public attitudes and acceptance.MethodsWe conducted a factorial vignette experiment with a probability-based survey of 3030 participants from Germany’s general population. Respondents were presented with hypothetical scenarios involving AI applications in diagnosis and treatment in a hospital setting. Linear regression models assessed the relative influence of AI attributes on the dependent variables (support, risk perception, and personalized care), with additional subgroup analyses to explore heterogeneity by sociodemographic characteristics.ResultsMean values between 4.2 and 4.4 on a 1-7 scale indicate a generally neutral to slightly negative stance toward AI integration in terms of general support, risk perception, and personalized care expectations, with responses spanning the full scale from strong support to strong opposition. Among the 4 dimensions, reliability emerges as the most influential factor (percentage of explained variance [EV] of up to 10.5%). Respondents expect AI to not only prevent errors but also exceed current reliability standards while strongly disapproving of nontraceable systems (transparency is another important factor, percentage of EV of up to 4%). Costs and autonomy play a comparatively minor role (percentage of EVs of up to 1.5% and 1.3%), with preferences favoring collaborative AI systems over autonomous ones, and higher costs generally leading to rejection. Heterogeneity analysis reveals limited sociodemographic differences, with education and migration background influencing attitudes toward transparency and autonomy, and gender differences primarily affecting cost-related perceptions. Overall, attitudes do not substantially differ between AI applications in diagnosis versus treatment.ConclusionsOur study fills a critical research gap by identifying the key factors that shape public trust and acceptance of AI in health care, particularly reliability, transparency, and patient-centered approaches. Our findings provide evidence-based recommendations for policy makers, health care providers, and AI developers to enhance trust and accountability, key concerns often overlooked in system development and real-world applications. The study highlights the need for targeted policy and educational initiatives to support the responsible integration of AI in patient care.

This study examined artificial intelligence (AI) applications for health information service delivery throughout Africa and its subregions. The study aimed to review existing research, identify key AI applications in healthcare, assess their impact in African healthcare, and examine strategies that promote effective, ethical, and equitable integration for sustainable healthcare improvements across the continent. African healthcare systems face obstacles, including restricted access to quality information, shortages of healthcare professionals, and infrastructural limitations. AI presents promising solutions to these challenges. The article comprises a systematic literature review of peer-reviewed research on AI-driven health information service delivery in Africa between 2014 and 2024, examining key AI applications, opportunities, challenges, and ethical considerations associated with AI adoption in healthcare, and their influence on health information service delivery. This review examined African healthcare systems’ preparedness for AI adoption, the importance of digital literacy, and the infrastructure that affects AI dissemination. The study also examined ethical considerations, including data privacy, equity, and the risk of widening health disparities. The findings reveal that AI technologies can enhance the accessibility, precision, and efficiency of health information services across Africa, particularly in underserved areas. The primary obstacles to AI adoption include insufficient digital infrastructure, low digital literacy, and socio-cultural hurdles. This review emphasises addressing ethical concerns to ensure that AI-driven health information systems benefit all populations without exacerbating inequality. The review offers insights for policymakers, healthcare providers, and researchers. Areas for future research and development were reviewed to ensure sustainable and equitable AI integration across Africa’s healthcare systems. The study recommends that stakeholders invest in digital infrastructure, promote digital literacy, and establish ethical guidelines for successful and equitable AI integration into health information service delivery throughout Africa.

BackgroundKnowledge graphs (KGs) play a key role to enable explainable artificial intelligence (AI) applications in healthcare. Constructing clinical knowledge graphs (CKGs) against heterogeneous electronic health records (EHRs) has been desired by the research and healthcare AI communities. From the standardization perspective, community-based standards such as the Fast Healthcare Interoperability Resources (FHIR) and the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) are increasingly used to represent and standardize EHR data for clinical data analytics, however, the potential of such a standard on building CKG has not been well investigated. ObjectiveTo develop and evaluate methods and tools that expose the OMOP CDM-based clinical data repositories into virtual clinical KGs that are compliant with FHIR Resource Description Framework (RDF) specification. MethodsWe developed a system called FHIR-Ontop-OMOP to generate virtual clinical KGs from the OMOP relational databases. We leveraged an OMOP CDM-based Medical Information Mart for Intensive Care (MIMIC-III) data repository to evaluate the FHIR-Ontop-OMOP system in terms of the faithfulness of data transformation and the conformance of the generated CKGs to the FHIR RDF specification. ResultsA beta version of the system has been released. A total of more than 100 data element mappings from 11 OMOP CDM clinical data, health system and vocabulary tables were implemented in the system, covering 11 FHIR resources. The generated virtual CKG from MIMIC-III contains 46,520 instances of FHIR Patient, 716,595 instances of Condition, 1,063,525 instances of Procedure, 24,934,751 instances of MedicationStatement, 365,181,104 instances of Observations, and 4,779,672 instances of CodeableConcept. Patient counts identified by five pairs of SQL (over the MIMIC database) and SPARQL (over the virtual CKG) queries were identical, ensuring the faithfulness of the data transformation. Generated CKG in RDF triples for 100 patients were fully conformant with the FHIR RDF specification. ConclusionThe FHIR-Ontop-OMOP system can expose OMOP database as a FHIR-compliant RDF graph. It provides a meaningful use case demonstrating the potentials that can be enabled by the interoperability between FHIR and OMOP CDM. Generated clinical KGs in FHIR RDF provide a semantic foundation to enable explainable AI applications in healthcare.

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 (AI) is playing an increasingly vital role in transforming the medical field, particularly in areas like medical imaging, clinical decision-making, pathology, and minimally invasive surgery. The rapid growth of medical data and the continuous refinement of machine learning algorithms have propelled AI's integration into healthcare. This study explores the advancements and applications of AI, specifically machine learning algorithms and intelligent medical robots, in enhancing diagnostics, treatment, and healthcare delivery. A comprehensive review of current AI applications in healthcare, including its use in medical imaging, pathology, clinical decision-making, and robotic-assisted surgery, was conducted. AI technologies such as the Da Vinci Surgical Robot and machine learning-based diagnostic tools have significantly improved diagnostic accuracy and the precision of minimally invasive surgeries. AI-driven systems also contributed to better clinical decision support, faster recovery times for patients, and more accurate treatment plans. Overall, AI, through machine learning algorithms and intelligent medical robots, is revolutionizing healthcare by offering promising improvements in diagnostics, surgical precision, and patient care.