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Gait speed is increasingly recognized as an important health indicator. However, gait analysis in clinical settings often encounters inconsistencies due to methodological variability and resource constraints. To address these challenges, GaitKeeper uses artificial intelligence (AI) and augmented reality (AR) to standardize gait speed assessments. In laboratory conditions, GaitKeeper demonstrates close alignment with the Vicon system and, in clinical environments, it strongly correlates with the Gaitrite system. The integration of a cloud-based processing platform and robust data security positions GaitKeeper as an accurate, cost-effective, and user-friendly tool for gait assessment in diverse clinical settings.

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Prostate cancer remains a significant global health challenge, characterized by high incidence and substantial morbidity and mortality rates. Early detection is critical for improving patient outcomes, yet current diagnostic methods have limitations in accuracy and reliability. Artificial intelligence (AI) has emerged as a promising tool to address these challenges in prostate cancer care. AI technologies, including machine learning algorithms and advanced imaging techniques, offer potential solutions to enhance diagnostic accuracy, optimize treatment strategies, and personalize patient care. This review explores the current landscape of AI applications in prostate cancer diagnostics, highlighting state-of-the-art tools and their clinical implications. By synthesizing recent advancements and discussing future directions, the review underscores the transformative potential of AI in revolutionizing prostate cancer diagnosis and management. Ultimately, integrating AI into clinical practice can potentially improve outcomes and quality of life for patients affected by prostate cancer.

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Innovative technology-based solutions in mental healthcare promise significant improvements in care quality and clinical outcomes. However, their successful implementation is profoundly influenced by the levels of trust patients hold toward their treatment providers, organizations, and the technology itself. This paper delves into the complexities of building and assessing patient trust within the intensive mental health care context, focusing on inpatient settings. We explore the multifaceted nature of trust, including interpersonal, institutional, and technological trust. We highlight the crucial role of therapeutic trust, which comprises both interpersonal trust between patients and providers, and institutional trust in treatment organizations. The manuscript identifies potential key barriers to trust, from sociocultural background to a patient's psychopathology. Furthermore, it examines the concept of technological trust, emphasizing the influence of digital literacy, socio-economic status, and user experience on patients' acceptance of digital health innovations. By emphasizing the importance of assessing and addressing the state of trust among patients, the overarching goal is to leverage digital innovations to enhance mental healthcare outcomes within intensive mental health settings.

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Background: Artificial Intelligence (AI) holds transformative potential for the healthcare industry, offering innovative solutions for diagnosis, treatment planning, and improving patient outcomes. As AI continues to be integrated into healthcare systems, it promises advancements across various domains. This review explores the diverse applications of AI in healthcare, along with the challenges and limitations that need to be addressed. The aim is to provide a comprehensive overview of AI's impact on healthcare and to identify areas for further development and focus. Main Applications: The review discusses the broad range of AI applications in healthcare. In medical imaging and diagnostics, AI enhances the accuracy and efficiency of diagnostic processes, aiding in early disease detection. AI-powered clinical decision support systems assist healthcare professionals in patient management and decision-making. Predictive analytics using AI enables the prediction of patient outcomes and identification of potential health risks. AI-driven robotic systems have revolutionized surgical procedures, improving precision and outcomes. Virtual assistants and chatbots enhance patient interaction and support, providing timely information and assistance. In the pharmaceutical industry, AI accelerates drug discovery and development by identifying potential drug candidates and predicting their efficacy. Additionally, AI improves administrative efficiency and operational workflows in healthcare, streamlining processes and reducing costs. AI-powered remote monitoring and telehealth solutions expand access to healthcare, particularly in underserved areas. Challenges and Limitations: Despite the significant promise of AI in healthcare, several challenges persist. Ensuring the reliability and consistency of AI-driven outcomes is crucial. Privacy and security concerns must be navigated carefully, particularly in handling sensitive patient data. Ethical considerations, including bias and fairness in AI algorithms, need to be addressed to prevent unintended consequences. Overcoming these challenges is critical for the ethical and successful integration of AI in healthcare. Conclusion: The integration of AI into healthcare is advancing rapidly, offering substantial benefits in improving patient care and operational efficiency. However, addressing the associated challenges is essential to fully realize the transformative potential of AI in healthcare. Future efforts should focus on enhancing the reliability, transparency, and ethical standards of AI technologies to ensure they contribute positively to global health outcomes.

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BACKGROUND: This report evaluates the potential of artificial intelligence (AI) in psychodermatology, emphasizing its ability to enhance diagnostic accuracy, treatment efficacy, and personalized care. Psychodermatology, which explores the connection between mental health and skin disorders, stands to benefit from AI's advanced data analysis and pattern recognition capabilities. MATERIALS AND METHODS: A literature search was conducted on PubMed and Google Scholar, spanning from 2004 to 2024, following PRISMA guidelines. Studies included demonstrated AI's effectiveness in predicting treatment outcomes for body dysmorphic disorder, identifying biomarkers in psoriasis and anxiety disorders, and refining therapeutic strategies. RESULTS: The review identified several studies highlighting AI's role in improving treatment outcomes and diagnostic accuracy in psychodermatology. AI was effective in predicting outcomes for body dysmorphic disorder and identifying biomarkers related to psoriasis and anxiety disorders. However, challenges such as limited dermatologist knowledge, integration difficulties, and ethical concerns regarding patient privacy were noted. CONCLUSION: AI holds significant promise for advancing psychodermatology by improving diagnostic precision, treatment effectiveness, and personalized care. Nonetheless, realizing this potential requires large-scale clinical validation, enhanced dataset diversity, and robust ethical frameworks. Future research should focus on these areas, with interdisciplinary collaboration essential for overcoming current challenges and optimizing patient care in psychodermatology.

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Heart failure (HF) remains a significant burden on global healthcare systems, necessitating innovative approaches for its management. This manuscript critically evaluates the role of remote monitoring and telemedicine in revolutionizing HF care delivery. Drawing upon a synthesis of current literature and clinical practices, it delineates the pivotal benefits, challenges, and personalized strategies associated with these technologies in HF management. The analysis highlights the potential of remote monitoring and telemedicine in facilitating timely interventions, enhancing patient engagement, and optimizing treatment adherence, thereby ameliorating clinical outcomes. However, technical intricacies, regulatory frameworks, and socioeconomic factors pose formidable hurdles to widespread adoption. The manuscript emphasizes the imperative of tailored interventions, leveraging advancements in artificial intelligence and machine learning, to address individual patient needs effectively. Looking forward, sustained innovation, interdisciplinary collaboration, and strategic investment are advocated to realize the transformative potential of remote monitoring and telemedicine in HF management, thereby advancing patient-centric care paradigms and optimizing healthcare resource allocation.

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Background: Ultra-low-field magnetic resonance imaging (ULF-MRI) has emerged as an alternative with several portable clinical applications. This review aims to comprehensively explore its applications, potential limitations, technological advancements, and expert recommendations. Methods: A review of the literature was conducted across medical databases to identify relevant studies. Articles on clinical usage of ULF-MRI were included, and data regarding applications, limitations, and advancements were extracted. A total of 25 articles were included for qualitative analysis. Results: The review reveals ULF-MRI efficacy in intensive care settings and intraoperatively. Technological strides are evident through innovative reconstruction techniques and integration with machine learning approaches. Additional advantages include features such as portability, cost-effectiveness, reduced power requirements, and improved patient comfort. However, alongside these strengths, certain limitations of ULF-MRI were identified, including low signal-to-noise ratio, limited resolution and length of scanning sequences, as well as variety and absence of regulatory-approved contrast-enhanced imaging. Recommendations from experts emphasize optimizing imaging quality, including addressing signal-to-noise ratio (SNR) and resolution, decreasing the length of scan time, and expanding point-of-care magnetic resonance imaging availability. Conclusion: This review summarizes the potential of ULF-MRI. The technology's adaptability in intensive care unit settings and its diverse clinical and surgical applications, while accounting for SNR and resolution limitations, highlight its significance, especially in resource-limited settings. Technological advancements, alongside expert recommendations, pave the way for refining and expanding ULF-MRI's utility. However, adequate training is crucial for widespread utilization.

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OBJECTIVES: This work presents the development and evaluation of coordn8, a web-based application that streamlines fax processing in outpatient clinics using a "human-in-the-loop" machine learning framework. We demonstrate the effectiveness of the platform at reducing fax processing time and producing accurate machine learning inferences across the tasks of patient identification, document classification, spam classification, and duplicate document detection. METHODS: We deployed coordn8 in 11 outpatient clinics and conducted a time savings analysis by observing users and measuring fax processing event logs. We used statistical methods to evaluate the machine learning components across different datasets to show generalizability. We conducted a time series analysis to show variations in model performance as new clinics were onboarded and to demonstrate our approach to mitigating model drift. RESULTS: Our observation analysis showed a mean reduction in individual fax processing time by 147.5 s, while our event log analysis of over 7000 faxes reinforced this finding. Document classification produced an accuracy of 81.6%, patient identification produced an accuracy of 83.7%, spam classification produced an accuracy of 98.4%, and duplicate document detection produced a precision of 81.0%. Retraining document classification increased accuracy by 10.2%. DISCUSSION: coordn8 significantly decreased fax-processing time and produced accurate machine learning inferences. Our human-in-the-loop framework facilitated the collection of high-quality data necessary for model training. Expanding to new clinics correlated with performance decline, which was mitigated through model retraining. CONCLUSION: Our framework for automating clinical tasks with machine learning offers a template for health systems looking to implement similar technologies.

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Background: Tissue engineering is a multidisciplinary field that combines principles from cell biology, bioengineering, material sciences, medicine and surgery to create functional and viable bioproducts that can be used to repair or replace damaged or diseased tissues in the human body. The complexity of tissue engineering can affect the prospects of efficiently translating scientific discoveries in the field into scalable clinical approaches that could benefit patients. Organizational challenges may play a key role in the clinical translation of tissue engineering for the benefit of patients. Methods: To gain insight into the organizational aspects of tissue engineering that may create impediments to efficient clinical translation, we conducted a retrospective qualitative case study of one tissue engineering multi-site translational project on knee cartilage engineered tissue grafts. We collected qualitative data using a set of different methods: semi-structured interviews, documentary research and audio-visual content analysis. Results: Our study identified various challenges associated to first-in-human trials in tissue engineering particularly related to: logistics and communication; research participant recruitment; clinician and medical student participation; study management; and regulation. Conclusions: While not directly generalizable to other types of advanced therapies or to regenerative medicine in general, our results offer valuable insights into organizational barriers that may prevent efficient clinical translation in the field of tissue engineering.

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BACKGROUND: Recent enhancements in Large Language Models (LLMs) such as ChatGPT have exponentially increased user adoption. These models are accessible on mobile devices and support multimodal interactions, including conversations, code generation, and patient image uploads, broadening their utility in providing healthcare professionals with real-time support for clinical decision-making. Nevertheless, many authors have highlighted serious risks that may arise from the adoption of LLMs, principally related to safety and alignment with ethical guidelines. OBJECTIVE: To address these challenges, we introduce a novel methodological approach designed to assess the specific feasibility of adopting LLMs within a healthcare area, with a focus on clinical nursing, evaluating their performance and thereby directing their choice. Emphasizing LLMs' adherence to scientific advancements, this approach prioritizes safety and care personalization, according to the "Organization for Economic Co-operation and Development" frameworks for responsible AI. Moreover, its dynamic nature is designed to adapt to future evolutions of LLMs. METHOD: Through integrating advanced multidisciplinary knowledge, including Nursing Informatics, and aided by a prospective literature review, seven key domains and specific evaluation items were identified as follows:A Peer Review by experts in Nursing and AI was performed, ensuring scientific rigor and breadth of insights for an essential, reproducible, and coherent methodological approach. By means of a 7-point Likert scale, thresholds are defined in order to classify LLMs as "unusable", "usable with high caution", and "recommended" categories. Nine state of the art LLMs were evaluated using this methodology in clinical oncology nursing decision-making, producing preliminary results. Gemini Advanced, Anthropic Claude 3 and ChatGPT 4 achieved the minimum score of the State of the Art Alignment & Safety domain for classification as "recommended", being also endorsed across all domains. LLAMA 3 70B and ChatGPT 3.5 were classified as "usable with high caution." Others were classified as unusable in this domain. CONCLUSION: The identification of a recommended LLM for a specific healthcare area, combined with its critical, prudent, and integrative use, can support healthcare professionals in decision-making processes.

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BACKGROUND: Introducing new working methods is common in healthcare organisations. However, implementation of a new method is often suboptimal. This reduces the effectiveness of the innovation and has several other negative effects, for example on staff turnover. The aim of the current study was to implement the ABC method in residential departments for brain injured patients and to assess the quality of the implementation process. The ABC method is a simplified form of behavioural modification based on the concept that behaviour operates on the environment and is maintained by its consequences. METHODS: Four residential departments for brain injured patients introduced the ABC method sequentially as healthcare innovation using a stepped-wedge design. A systematic process evaluation of the implementation was carried out using the framework of Saunders et al. Descriptive statistics were used to analyse the quantitative data; open questions were clustered. RESULTS: The training of the ABC method was well executed and the nursing staff was enthusiastic and sufficiently involved. Important aspects for successful implementation had been addressed (like a detailed implementation plan and implementation meetings). However, facilitators and barriers that were noted were not addressed in a timely manner. This negatively influenced the extent to which the ABC method could be properly learned, implemented, and applied in the short and long term. CONCLUSIONS: The most challenging part of the introduction of this new trained and introduced method in health care was clearly the implementation. To have a successful implementation serious attention is needed to tailor-made evidence-based implementation strategies based on facilitators and barriers that are identified during the implementation process. Bottlenecks in working with the ABC method have to be addressed as soon as possible. This likely requires 'champions' who are trained for the job, next to an organisation's management that facilitates the multidisciplinary teams and provides clarity about policy and agreements regarding the training and implementation of the new method. The current process evaluation and the recommendations may serve as an example for the implementation of new methods in other healthcare organisations.

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The Stanford Biodesign needs-centric framework can guide healthcare innovators to successfully adopt the 'Identify, Invent and Implement' framework and develop new healthcare innovations products to address patients' needs. This scoping review explored the application of the Stanford Biodesign framework for healthcare innovation training and the development of novel healthcare innovative products. Seven electronic databases were searched from their respective inception dates till April 2023: PubMed, Embase, CINAHL, PsycINFO, Web of Science, Scopus, ProQuest Dissertations, and Theses Global. This review was reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Reviews and was guided by the Arksey and O'Malley's scoping review framework. Findings were analyzed using Braun and Clarke's thematic analysis framework. Three themes and eight subthemes were identified from the 26 included articles. The main themes are: (1) Making a mark on healthcare innovation, (2) Secrets behind success, and (3) The next steps. The Stanford Biodesign framework guided healthcare innovation teams to develop new medical products and achieve better patient health outcomes through the induction of training programs and the development of novel products. Training programs adopting the Stanford Biodesign approach were found to be successful in improving trainees' entrepreneurship, innovation, and leadership skills and should continue to be promoted. To aid innovators in commercializing their newly developed medical products, additional support such as securing funds for early start-up companies, involving clinicians and users in product testing and validation, and establishing new guidelines and protocols for the new healthcare products would be needed.

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This comprehensive review explores the transformative impact of artificial intelligence (AI) on hospital management, delving into its applications, challenges, and future trends. Integrating AI in administrative functions, clinical operations, and patient engagement holds significant promise for enhancing efficiency, optimizing resource allocation, and revolutionizing patient care. However, this evolution is accompanied by ethical, legal, and operational considerations that necessitate careful navigation. The review underscores key findings, emphasizing the implications for the future of hospital management. It calls for a proactive approach, urging stakeholders to invest in education, prioritize ethical guidelines, foster collaboration, advocate for thoughtful regulation, and embrace a culture of innovation. The healthcare industry can successfully navigate this transformative era through collective action, ensuring that AI contributes to more effective, accessible, and patient-centered healthcare delivery.

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PURPOSE: In this viewpoint article, the authors recognize the increased focus in health systems on co-design for innovation and change. This article explores the role of leaders and mangers in developing and enhancing a culture of trust in their organizations to enable co-design, with the potential to drive innovation and change in healthcare. DESIGN/METHODOLOGY/APPROACH: Using social science analyses, the authors argue that current co-design literature has limited focus on interactions between senior leaders and managers, and healthcare staff and service users in supporting co-designed innovation and change. The authors draw on social and health science studies of trust to highlight how the value-based co-design process needs to be supported and enhanced. We outline what co-design innovation and change involve in a health system, conceptualize trust and reflect on its importance within the health system, and finally note the role of senior leaders and managers in supporting trust and responsiveness for co-designed innovation and change. FINDINGS: Healthcare needs leaders and managers to embrace co-design that drives innovation now and in the future through people - leading to better healthcare for society at large. As authors we argue that it is now the time to shift our focus on the role of senior managers and leaders to embed co-design into health and social care structures, through creating and nurturing a culture of trust. ORIGINALITY/VALUE: Building public trust in the health system and interpersonal trust within the health system is an ongoing process that relies upon personal behavior of managers and senior leaders, organizational practices within the system, as well as political processes that underpin these practices. By implementing managerial, leadership and individual practices on all levels, senior managers and leaders provide a mechanism to increase both trust and responsiveness for co-design that supports innovation and change in the health system.

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The medical device industry is undergoing substantial transformations, looking to face the increasing pressures on healthcare systems and fundamental shifts in healthcare delivery. There is an ever-growing emphasis on identifying underserved clinical requirements and enhancing industry-academia partnerships to accelerate innovative solutions. In this context, an analysis of the requirements for translation, highlighting support and funding for innovation to transform an idea for a biomaterial device into a commercially available product, is discussed.

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Aims: TeleCheck-AF is a novel remote on-demand mobile health infrastructure around teleconsultations for patients with atrial fibrillation. Social media (SoMe) communication on Twitter contributed to the dissemination of this healthcare innovation by using the hashtag #TeleCheckAF. This study aims to analyse the SoMe network behind #TeleCheckAF and determine the key opinion leaders. Methods and results: Publicly available data on actors and interactions around the hashtag #TeleCheckAF were collected by web scraping from the platform Twitter. With tools based on social network analysis, a social network was created, different communities were identified, and key opinion leaders were determined by their centrality in the network. The SoMe network consisted of 413 086 accounts with 636 502 ties in 22 different communities. A total of 287 accounts that diffused information and/or used the TeleCheck-AF infrastructure were analysed in depth. Those accounts involved users from >15 different countries and multidisciplinary professions. Further, 20 opinion leaders were identified, including four official accounts of societies and associated journals among key opinion leaders. Peaks in #TeleCheckAF tweets were seen after (virtual) conferences and other activities involving national and international cardiology societies. Social network analysis of the TeleCheck-AF Twitter hashtag revealed a wide, multidisciplinary potential reach for the diffusion of a healthcare innovation. Conclusion: Official society SoMe accounts can amplify the dissemination of research findings by featuring abstract presentations during conferences and published manuscripts. This underlines the synergistic effects between traditional and SoMe-based research dissemination strategies for novel healthcare approaches, such as the TeleCheck-AF project.

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Background: Internet-Based Cognitive Behavioral Therapy (iCBT) holds great potential in addressing mental health issues, yet its real-world implementation poses significant challenges. While prior research has predominantly focused on centralized care models, this study explores the implementation of iCBT in the context of decentralized organizational structures within the Swedish primary care setting, where all interventions traditionally are delivered at local Primary Care Centers (PCCs). Aim: This study aims to enhance our understanding of iCBT implementation in primary care and assess the impact of organizational models on the implementation's outcome using the RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance) framework. Method: A mixed-methods research design was employed to identify the factors influencing iCBT implementation across different levels, involving patients, therapists and managers. Data spanning two years was collected and analyzed through thematic analysis and statistical tests. The study encompassed 104 primary care centers, with patient data (n = 1979) sourced from the Swedish National Quality Register for Internet-Based Psychological Treatment (SibeR). Additionally, 53 iCBT therapists and 50 PCC managers completed the Normalization Measure Development Questionnaire, and 15 leaders participated in interviews. Results: Our investigation identified two implementation approaches, one concentrated and one decentralized. Implementation effectiveness was evident through adherence rates suggesting that iCBT is a promising approach for treating mental ill-health in primary care, although challenges were observed concerning patient assessment and therapist drift towards unstructured treatment. Mandatory implementation, along with managerial and organizational support, positively impacted adoption. Results vary in terms of adherence to established protocols, with therapists working in concentrated model showing a significantly higher percentage of registration in the quality register SibeR (X2 (1, N = 2973) = 430.5774, p = 0.001). They also showed significantly higher means in cognitive participation (Z = -2.179, p = 0.029) and in reflective monitoring (Z = -2.548, p = 0.011). Discussion: Overall, the study results demonstrate that iCBT, as a complex and qualitatively different intervention from traditional psychological treatment, can be widely implemented in primary care settings. The study's key finding highlights the substantial advantages of the concentrated organizational model. This model has strengths in sustainability, encourages reflective monitoring among therapists, the use of quality registers, and enforces established protocols. Conclusion: In conclusion, this study significantly contributes to the understanding of the practical aspects associated with the implementation of complex internet interventions, particularly in the context of internet-based cognitive-behavioral therapy (iCBT). The study highlights that effective iCBT integration into primary care requires a multifaceted approach, taking into account organizational models, robust support structures, and a commitment to maintaining quality standards. By emphasizing these factors, our research aims to provide actionable insights that can enhance the practicability and real-world applicability of implementing iCBT in primary care settings.

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OBJECTIVES: This work aims to improve the quality of care provided to patients by equipping caregivers with comprehensive set of problem-solving tools and competencies. This is achieved through the development of a customized health design process that incorporates both human-centric and data-centric tools. BACKGROUND: To meet the growing complexity of today's clinical practice, caregivers need to be empowered with the tools and competencies necessary to address the multifaceted challenges they encounter. This has emphasized the need to broaden the traditional role of caregivers as evidence-based practitioners to include being healthcare problem-solvers and innovators who utilize their creative and critical thinking skills. METHOD: While design thinking (DT) is a popular methodology that fosters caregivers' empathy and creativity, it does not provide tools for evaluating the quality of obtained solutions. To address this gap, a problem-solving process that combines DT and data-centric tools of the Lean Six Sigma method was developed in this work. RESULTS: The evaluation of this customized design process was based on targeted competencies derived from the six aims of healthcare. The potential benefits are then highlighted through mapping the possible outputs of every phase with the targeted set of caregivers' skills. Additionally, an implementation plan was outlined for a local hospital, showcasing the potential impact this process can have in empowering caregivers with the necessary competencies to create effective and innovative solutions for care delivery. CONCLUSION: Overall, This unique approach has the potential to contribute to the ongoing effort to transform healthcare into an efficient system that meets the needs of both patients and caregivers.

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Background: The burden of communicable, non-communicable diseases and reproductive maternal, newborn, child & adolescent health in India, reflects the necessity to develop tailored solutions. The plethora of MedTech innovations has provided healthcare facilities with more effective, affordable and accessible healthcare for people across the country. However, in spite of the Make-in-India scheme in the country, the indigenously developed healthcare technology is far from making an impact on the healthcare system. Objective: To present a roadmap for MedTech innovations for their successful deployment into the public healthcare system. Methodology: In addition to the literature review, recommendations were included from several stakeholders such as innovators, manufacturers, policymakers, subject matter experts, funding organizations, State health officials etc. Results and conclusion: The journey of healthcare innovation from need identification to ideation, to prototyping and validation has paved the way towards the de novo design that caters to unmet needs. Innovations at the advanced technology readiness level (TRL 7/8 and above) demand a holistic and multidisciplinary approach which includes clinical validation, regulatory approval and Health technology assessment. The deployment of healthcare technology into the public healthcare system must consider resources (e.g., time, staff, budget, investment policies), ethical concerns (privacy, security, regulations, ownership), governance (policy, accountability, responsibility etc.), and Skills (capabilities, culture, etc.). The technologies are considered for field trials before the uptake in the public health system. Technology can be a key tool in achieving Universal Health Coverage but its use has to be strategic, judicious, and cognizant of issues around privacy and patient rights.

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This review explores recent advancements and applications of 3D printing in healthcare, with a focus on personalized medicine, tissue engineering, and medical device production. It also assesses economic, environmental, and ethical considerations. In our review of the literature, we employed a comprehensive search strategy, utilizing well-known databases like PubMed and Google Scholar. Our chosen keywords encompassed essential topics, including 3D printing, personalized medicine, nanotechnology, and related areas. We first screened article titles and abstracts and then conducted a detailed examination of selected articles without imposing any date limitations. The articles selected for inclusion, comprising research studies, clinical investigations, and expert opinions, underwent a meticulous quality assessment. This methodology ensured the incorporation of high-quality sources, contributing to a robust exploration of the role of 3D printing in the realm of healthcare. The review highlights 3D printing's potential in healthcare, including customized drug delivery systems, patient-specific implants, prosthetics, and biofabrication of organs. These innovations have significantly improved patient outcomes. Integration of nanotechnology has enhanced drug delivery precision and biocompatibility. 3D printing also demonstrates cost-effectiveness and sustainability through optimized material usage and recycling. The healthcare sector has witnessed remarkable progress through 3D printing, promoting a patient-centric approach. From personalized implants to radiation shielding and drug delivery systems, 3D printing offers tailored solutions. Its transformative applications, coupled with economic viability and sustainability, have the potential to revolutionize healthcare. Addressing material biocompatibility, standardization, and ethical concerns is essential for responsible adoption.

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