Generative AI in healthcare: an implementation science informed translational path on application, integration and governance.

Artificial intelligence (AI), particularly generative AI, has emerged as a transformative tool in healthcare, with the potential to revolutionize clinical decision-making and improve health outcomes. Generative AI, capable of generating new data such as text and images, holds promise in enhancing patient care, revolutionizing disease diagnosis and expanding treatment options. However, the utility and impact of generative AI in healthcare remain poorly understood, with concerns around ethical and medico-legal implications, integration into healthcare service delivery and workforce utilisation. Also, there is not a clear pathway to implement and integrate generative AI in healthcare delivery. This article aims to provide a comprehensive overview of the use of generative AI in healthcare, focusing on the utility of the technology in healthcare and its translational application highlighting the need for careful planning, execution and management of expectations in adopting generative AI in clinical medicine. Key considerations include factors such as data privacy, security and the irreplaceable role of clinicians' expertise. Frameworks like the technology acceptance model (TAM) and the Non-Adoption, Abandonment, Scale-up, Spread and Sustainability (NASSS) model are considered to promote responsible integration. These frameworks allow anticipating and proactively addressing barriers to adoption, facilitating stakeholder participation and responsibly transitioning care systems to harness generative AI's potential. Generative AI has the potential to transform healthcare through automated systems, enhanced clinical decision-making and democratization of expertise with diagnostic support tools providing timely, personalized suggestions. Generative AI applications across billing, diagnosis, treatment and research can also make healthcare delivery more efficient, equitable and effective. However, integration of generative AI necessitates meticulous change management and risk mitigation strategies. Technological capabilities alone cannot shift complex care ecosystems overnight; rather, structured adoption programs grounded in implementation science are imperative. It is strongly argued in this article that generative AI can usher in tremendous healthcare progress, if introduced responsibly. Strategic adoption based on implementation science, incremental deployment and balanced messaging around opportunities versus limitations helps promote safe, ethical generative AI integration. Extensive real-world piloting and iteration aligned to clinical priorities should drive development. With conscientious governance centred on human wellbeing over technological novelty, generative AI can enhance accessibility, affordability and quality of care. As these models continue advancing rapidly, ongoing reassessment and transparent communication around their strengths and weaknesses remain vital to restoring trust, realizing positive potential and, most importantly, improving patient outcomes.

Reddy S 《Implementation Science》

Challenges and strategies for wide-scale artificial intelligence (AI) deployment in healthcare practices: A perspective for healthcare organizations.

Healthcare organizations have realized that Artificial intelligence (AI) can provide a competitive edge through personalized patient experiences, improved patient outcomes, early diagnosis, augmented clinician capabilities, enhanced operational efficiencies, or improved medical service accessibility. However, deploying AI-driven tools in the healthcare ecosystem could be challenging. This paper categorizes AI applications in healthcare and comprehensively examines the challenges associated with deploying AI in medical practices at scale. As AI continues to make strides in healthcare, its integration presents various challenges, including production timelines, trust generation, privacy concerns, algorithmic biases, and data scarcity. The paper highlights that flawed business models and wrong workflows in healthcare practices cannot be rectified merely by deploying AI-driven tools. Healthcare organizations should re-evaluate root problems such as misaligned financial incentives (e.g., fee-for-service models), dysfunctional medical workflows (e.g., high rates of patient readmissions), poor care coordination between different providers, fragmented electronic health records systems, and inadequate patient education and engagement models in tandem with AI adoption. This study also explores the need for a cultural shift in viewing AI not as a threat but as an enabler that can enhance healthcare delivery and create new employment opportunities while emphasizing the importance of addressing underlying operational issues. The necessity of investments beyond finance is discussed, emphasizing the importance of human capital, continuous learning, and a supportive environment for AI integration. The paper also highlights the crucial role of clear regulations in building trust, ensuring safety, and guiding the ethical use of AI, calling for coherent frameworks addressing transparency, model accuracy, data quality control, liability, and ethics. Furthermore, this paper underscores the importance of advancing AI literacy within academia to prepare future healthcare professionals for an AI-driven landscape. Through careful navigation and proactive measures addressing these challenges, the healthcare community can harness AI's transformative power responsibly and effectively, revolutionizing healthcare delivery and patient care. The paper concludes with a vision and strategic suggestions for the future of healthcare with AI, emphasizing thoughtful, responsible, and innovative engagement as the pathway to realizing its full potential to unlock immense benefits for healthcare organizations, physicians, nurses, and patients while proactively mitigating risks.

Esmaeilzadeh P 《-》

Revolutionizing healthcare: the role of artificial intelligence in clinical practice.

Healthcare systems are complex and challenging for all stakeholders, but artificial intelligence (AI) has transformed various fields, including healthcare, with the potential to improve patient care and quality of life. Rapid AI advancements can revolutionize healthcare by integrating it into clinical practice. Reporting AI's role in clinical practice is crucial for successful implementation by equipping healthcare providers with essential knowledge and tools. This review article provides a comprehensive and up-to-date overview of the current state of AI in clinical practice, including its potential applications in disease diagnosis, treatment recommendations, and patient engagement. It also discusses the associated challenges, covering ethical and legal considerations and the need for human expertise. By doing so, it enhances understanding of AI's significance in healthcare and supports healthcare organizations in effectively adopting AI technologies. The current investigation analyzed the use of AI in the healthcare system with a comprehensive review of relevant indexed literature, such as PubMed/Medline, Scopus, and EMBASE, with no time constraints but limited to articles published in English. The focused question explores the impact of applying AI in healthcare settings and the potential outcomes of this application. Integrating AI into healthcare holds excellent potential for improving disease diagnosis, treatment selection, and clinical laboratory testing. AI tools can leverage large datasets and identify patterns to surpass human performance in several healthcare aspects. AI offers increased accuracy, reduced costs, and time savings while minimizing human errors. It can revolutionize personalized medicine, optimize medication dosages, enhance population health management, establish guidelines, provide virtual health assistants, support mental health care, improve patient education, and influence patient-physician trust. AI can be used to diagnose diseases, develop personalized treatment plans, and assist clinicians with decision-making. Rather than simply automating tasks, AI is about developing technologies that can enhance patient care across healthcare settings. However, challenges related to data privacy, bias, and the need for human expertise must be addressed for the responsible and effective implementation of AI in healthcare.

Alowais SA ，Alghamdi SS ，Alsuhebany N ，Alqahtani T ，Alshaya AI ，Almohareb SN ，Aldairem A ，Alrashed M ，Bin Saleh K ，Badreldin HA ，Al Yami MS ，Al Harbi S ，Albekairy AM ... -  《BMC Medical Education》

Understanding the integration of artificial intelligence in healthcare organisations and systems through the NASSS framework: a qualitative study in a leading Canadian academic centre.

Artificial intelligence (AI) technologies are expected to "revolutionise" healthcare. However, despite their promises, their integration within healthcare organisations and systems remains limited. The objective of this study is to explore and understand the systemic challenges and implications of their integration in a leading Canadian academic hospital. Semi-structured interviews were conducted with 29 stakeholders concerned by the integration of a large set of AI technologies within the organisation (e.g., managers, clinicians, researchers, patients, technology providers). Data were collected and analysed using the Non-Adoption, Abandonment, Scale-up, Spread, Sustainability (NASSS) framework. Among enabling factors and conditions, our findings highlight: a supportive organisational culture and leadership leading to a coherent organisational innovation narrative; mutual trust and transparent communication between senior management and frontline teams; the presence of champions, translators, and boundary spanners for AI able to build bridges and trust; and the capacity to attract technical and clinical talents and expertise. Constraints and barriers include: contrasting definitions of the value of AI technologies and ways to measure such value; lack of real-life and context-based evidence; varying patients' digital and health literacy capacities; misalignments between organisational dynamics, clinical and administrative processes, infrastructures, and AI technologies; lack of funding mechanisms covering the implementation, adaptation, and expertise required; challenges arising from practice change, new expertise development, and professional identities; lack of official professional, reimbursement, and insurance guidelines; lack of pre- and post-market approval legal and governance frameworks; diversity of the business and financing models for AI technologies; and misalignments between investors' priorities and the needs and expectations of healthcare organisations and systems. Thanks to the multidimensional NASSS framework, this study provides original insights and a detailed learning base for analysing AI technologies in healthcare from a thorough socio-technical perspective. Our findings highlight the importance of considering the complexity characterising healthcare organisations and systems in current efforts to introduce AI technologies within clinical routines. This study adds to the existing literature and can inform decision-making towards a judicious, responsible, and sustainable integration of these technologies in healthcare organisations and systems.

Alami H ，Lehoux P ，Papoutsi C ，Shaw SE ，Fleet R ，Fortin JP ... -  《BMC HEALTH SERVICES RESEARCH》

Transforming Healthcare with AI: Promises, Pitfalls, and Pathways Forward.

This perspective paper provides a comprehensive examination of artificial intelligence (AI) in healthcare, focusing on its transformative impact on clinical practices, decision-making, and physician-patient relationships. By integrating insights from evidence, research, and real-world examples, it offers a balanced analysis of AI's capabilities and limitations, emphasizing its role in streamlining administrative processes, enhancing patient care, and reducing physician burnout while maintaining a human-centric approach in medicine. The research underscores AI's capacity to augment clinical decision-making and improve patient interactions, but it also highlights the variable impact of AI in different healthcare settings. The need for context-specific adaptations and careful integration of AI technologies into existing healthcare workflows is emphasized to maximize benefits and minimize unintended consequences. Significant attention is given to the implications of AI on the roles and competencies of healthcare professionals. The emergence of AI necessitates new skills in data literacy and technology use, prompting a shift in educational curricula towards digital health and AI training. Ethical considerations are a pivotal aspect of the discussion. The paper explores the challenges posed by data privacy concerns, algorithmic biases, and ensuring equitable access to AI-driven healthcare. It advocates for the development of comprehensive ethical frameworks and ongoing research to guide the responsible use of AI in healthcare. Conclusively, the paper advocates for a balanced approach to AI adoption in healthcare, highlighting the importance of ongoing research, strategic implementation, and the synergistic combination of human expertise with AI technologies for optimal patient care.

Shuaib A 《International Journal of General Medicine》