Differences in technical and clinical perspectives on AI validation in cancer imaging: mind the gap!

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.

The convergence of healthcare with the Internet of Things (IoT) and Artificial Intelligence (AI) is reshaping medical practice with promising enhanced data-driven insights, automated decision-making, and remote patient monitoring. It has the transformative potential of these technologies to revolutionize diagnosis, treatment, and patient care. This study aims to explore the integration of IoT and AI in healthcare, outlining their applications, benefits, challenges, and potential risks. By synthesizing existing literature, this study aims to provide insights into the current landscape of AI, IoT, and AIoT in healthcare, identify areas for future research and development, and establish a framework for the effective use of AI in health. A comprehensive literature review included indexed databases such as PubMed/Medline, Scopus, and Google Scholar. Key search terms related to IoT, AI, healthcare, and medicine were employed to identify relevant studies. Papers were screened based on their relevance to the specified themes, and eventually, a selected number of papers were methodically chosen for this review. The integration of IoT and AI in healthcare offers significant advancements, including remote patient monitoring, personalized medicine, and operational efficiency. Wearable sensors, cloud-based data storage, and AI-driven algorithms enable real-time data collection, disease diagnosis, and treatment planning. However, challenges such as data privacy, algorithmic bias, and regulatory compliance must be addressed to ensure responsible deployment of these technologies. Integrating IoT and AI in healthcare holds immense promise for improving patient outcomes and optimizing healthcare delivery. Despite challenges such as data privacy concerns and algorithmic biases, the transformative potential of these technologies cannot be overstated. Clear governance frameworks, transparent AI decision-making processes, and ethical considerations are essential to mitigate risks and harness the full benefits of IoT and AI in healthcare.

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.

This study aimed to investigate the knowledge, attitudes, and practice (KAP) of radiologists regarding artificial intelligence (AI) in medical imaging in the southeast of China. This cross-sectional study was conducted among radiologists in the Jiangsu, Zhejiang, and Fujian regions from October to December 2022. A self-administered questionnaire was used to collect demographic data and assess the KAP of participants towards AI in medical imaging. A structural equation model (SEM) was used to analyze the relationships between KAP. The study included 452 valid questionnaires. The mean knowledge score was 9.01±4.87, the attitude score was 48.96±4.90, and 75.22% of participants actively engaged in AI-related practices. Having a master's degree or above (OR=1.877, P=0.024), 5-10 years of radiology experience (OR=3.481, P=0.010), AI diagnosis-related training (OR=2.915, P<0.001), and engaging in AI diagnosis-related research (OR=3.178, P<0.001) were associated with sufficient knowledge. Participants with a junior college degree (OR=2.139, P=0.028), 5-10 years of radiology experience (OR=2.462, P=0.047), and AI diagnosis-related training (OR=2.264, P<0.001) were associated with a positive attitude. Higher knowledge scores (OR=5.240, P<0.001), an associate senior professional title (OR=4.267, P=0.026), 5-10 years of radiology experience (OR=0.344, P=0.044), utilizing AI diagnosis (OR=3.643, P=0.001), and engaging in AI diagnosis-related research (OR=6.382, P<0.001) were associated with proactive practice. The SEM showed that knowledge had a direct effect on attitude (β=0.481, P<0.001) and practice (β=0.412, P<0.001), and attitude had a direct effect on practice (β=0.135, P<0.001). Radiologists in southeastern China hold a favorable outlook on AI-assisted medical imaging, showing solid understanding and enthusiasm for its adoption, despite half lacking relevant training. There is a need for more AI diagnosis-related training, an efficient standardized AI database for medical imaging, and active promotion of AI-assisted imaging in clinical practice. Further research with larger sample sizes and more regions is necessary.

The results of research on the use of artificial intelligence (AI) for medical imaging of the lungs of patients with coronavirus disease 2019 (COVID-19) has been published in various forms. In this study, we reviewed the AI for diagnostic imaging of COVID-19 pneumonia. PubMed, arXiv, medRxiv, and Google scholar were used to search for AI studies. There were 15 studies of COVID-19 that used AI for medical imaging. Of these, 11 studies used AI for computed tomography (CT) and 4 used AI for chest radiography. Eight studies presented independent test data, 5 used disclosed data, and 4 disclosed the AI source codes. The number of datasets ranged from 106 to 5941, with sensitivities ranging from 0.67-1.00 and specificities ranging from 0.81-1.00 for prediction of COVID-19 pneumonia. Four studies with independent test datasets showed a breakdown of the data ratio and reported prediction of COVID-19 pneumonia with sensitivity, specificity, and area under the curve (AUC). These 4 studies showed very high sensitivity, specificity, and AUC, in the range of 0.9-0.98, 0.91-0.96, and 0.96-0.99, respectively.

Evaluation of a classroom-based medical imaging artificial intelligence educational intervention in Ghana: A pre-test/post-test study design.

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.

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.

The integration of artificial intelligence (AI) in healthcare presents significant promise to enhance clinical procedures and patient outcomes. This research examines the setting, methodology, conclusions, and issues associated with AI in healthcare. The swift proliferation of digital health data, encompassing medical imaging and clinical records, has generated substantial prospects for AI applications. Artificial intelligence methodologies, including machine learning, natural language processing, and computer vision, facilitate the derivation of significant insights from intricate datasets, hence improving clinical decision-making. A thorough literature review examines the practical applications of AI, encompassing its roles in medical diagnostics, treatment planning, and patient outcome prediction. The report also examines ethical issues, data protection, and legal frameworks, which are crucial for the responsible application of AI in healthcare. The results illustrate AI's capacity to enhance diagnostic precision, facilitate administrative efficiency, and optimise resource distribution, resulting in tailored therapies and improved healthcare administration. Nonetheless, obstacles persist, such as data integrity, algorithm transparency, and ethical considerations, which must be resolved to guarantee the secure and efficient deployment of AI. Continuous research, cooperation between healthcare and AI experts, and the establishment of comprehensive regulatory frameworks are essential for optimising the advantages of AI while minimising hazards. This research highlights AI's capacity to transform healthcare, stressing the necessity for a multidisciplinary strategy to effectively harness its benefits and tackle the associated ethical and regulatory dilemmas.

This article presents a mapping review of the literature concerning the ethics of artificial intelligence (AI) in health care. The goal of this review is to summarise current debates and identify open questions for future research. Five literature databases were searched (Scopus, Google Scholar, Philpapers, Web of Science, Pub Med), in April 2019, to support the following research question: “how can the primary ethical risks presented by AI-health be categorised, and what issues must policymakers, regulators and developers consider in order to be ‘ethically mindful?’”. A series of screening stages were carried out—for example, removing articles that focused on digital health in general (e.g. data sharing, data access, data privacy, surveillance/nudging, consent, ownership of health data, evidence of efficacy)—yielding a total of 156 papers that were included in the review.We find that ethical issues can be (a) epistemic, related to misguided, inconclusive or inscrutable evidence; (b) normative, related to unfair outcomes and transformative effectives; or (c) related to traceability. We further find that these ethical issues arise at six levels of abstraction: individual, interpersonal, group, institutional, and societal or sectoral. Finally, we outline a number of considerations for policymakers and regulators, mapping these to existing literature, and categorising each as epistemic, normative or traceability-related and at the relevant level of abstraction. This article contributes to the debate on AI in health care by offering a comprehensive analysis of the relevant literature, focusing on the ethical implications for individuals, interpersonal relationships, groups, institutions, societies and the health sector as a whole. Our goal is to inform policymakers, regulators and developers of what they must consider if they are to enable health and care systems to capitalise on the dual advantage of ethical AI; maximising the opportunities to cut costs, improve care, and improve the efficiency of health and care systems, whilst proactively avoiding the potential harms. We argue that if action is not swiftly taken in this regard, a new ‘AI winter’ could occur due to chilling effects related to a loss of public trust in the benefits of AI for health care.

The use of Artificial Intelligence (AI) in healthcare is growing quickly and offers big improvements in medical diagnostics, treatment planning, and patient care. However, people often don't trust AI systems, which prevents them from being widely used. This article looks at both the philosophical and practical issues of trust in healthcare AI systems. First, we provide an overview of the current state of AI in healthcare. Then, we review existing research on trust in technology. Based on our findings, we identify three main factors that affect trust in AI: Technology-Related Factors (transparency, reliability, safety), Healthcare Context Factors (how well AI fits into healthcare settings, proper training for professionals), and Individual User Factors (user experience and attitudes toward AI). Our results show that continuous human oversight, strong regulations, and ethical considerations are essential. Addressing these areas is key to making sure AI systems in healthcare are reliable, transparent, and trusted by both healthcare professionals and patients.

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.

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 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.