Artificial Intelligence in Dermatology: A Systematic Review

Artificial intelligence (AI) promises to change health care, with some studies showing proof of concept of a provider-level performance in various medical specialties. However, there are many barriers to implementing AI, including patient acceptance and understanding of AI. Patients' attitudes toward AI are not well understood. We systematically reviewed the literature on patient and general public attitudes toward clinical AI (either hypothetical or realised), including quantitative, qualitative, and mixed methods original research articles. We searched biomedical and computational databases from Jan 1, 2000, to Sept 28, 2020, and screened 2590 articles, 23 of which met our inclusion criteria. Studies were heterogeneous regarding the study population, study design, and the field and type of AI under study. Six (26%) studies assessed currently available or soon-to-be available AI tools, whereas 17 (74%) assessed hypothetical or broadly defined AI. The quality of the methods of these studies was mixed, with a frequent issue of selection bias. Overall, patients and the general public conveyed positive attitudes toward AI but had many reservations and preferred human supervision. We summarise our findings in six themes: AI concept, AI acceptability, AI relationship with humans, AI development and implementation, AI strengths and benefits, and AI weaknesses and risks. We suggest guidance for future studies, with the goal of supporting the safe, equitable, and patient-centred implementation of clinical AI.

ObjectivesTo investigate the general population’s view on artificial intelligence (AI) in medicine with specific emphasis on 3 areas that have experienced major progress in AI research in the past few years, namely radiology, robotic surgery, and dermatology. MethodsFor this prospective study, the April 2020 Online Longitudinal Internet Studies for the Social Sciences Panel Wave was used. Of the 3117 Longitudinal Internet Studies For The Social Sciences panel members contacted, 2411 completed the full questionnaire (77.4% response rate), after combining data from earlier waves, the final sample size was 1909. A total of 3 scales focusing on trust in the implementation of AI in radiology, robotic surgery, and dermatology were used. Repeated-measures analysis of variance and multivariate analysis of variance was used for comparison. ResultsThe overall means show that respondents have slightly more trust in AI in dermatology than in radiology and surgery. The means show that higher educated males, employed or student, of Western background, and those not admitted to a hospital in the past 12 months have more trust in AI. The trust in AI in radiology, robotic surgery, and dermatology is positively associated with belief in the efficiency of AI and these specific domains were negatively associated with distrust and accountability in AI in general. ConclusionsThe general population is more distrustful of AI in medicine unlike the overall optimistic views posed in the media. The level of trust is dependent on what medical area is subject to scrutiny. Certain demographic characteristics and individuals with a generally positive view on AI and its efficiency are significantly associated with higher levels of trust in AI.

The dynamic increase in the level of digitalization (the introduction of digital technologies) is a significant auxiliary lever in optimizing, improving and implementing innovative developments in the field of healthcare, raising the educational level and increasing the scientific and research potential of clinicians.Modern information and communication technologies are developing very rapidly and continuously, resulting in new interdependencies and changed relationships in related areas of human creative activity. Medicine, as one of the most creative sciences, requires a significant intellectual load for its further development at all levels, from professional training to practical clinical and scientific work.The current understanding of artificial intelligence (AI) and its use in evidence­based medicine requires in­depth analysis. Given that the relevant information is constantly changing and transforming, the ways in which it can be developed require further study regarding the possibilities of applying AI in medicine. Objective — to analyze the effectiveness of the use of modern digital dermatological platforms based on technological algorithms of AI in clinical medicine. Materials and methods. Numerous international documents on the implementation of AI in the field of healthcare, recent changes in state policy documents in the field of healthcare and higher education of Ukraine; international strategies and approaches to the use of basic technologies and platforms of AI in dermatology; modern views on the role and feasibility of using existing artificial cognitive structures were analyzed. Results and discussion. The directions and available tools for significantly increasing the role of digital transformation in clinical medicine, in particular, in dermatology, are analyzed. The relevance of further use of AI technology algorithms and the need for training for their practical use are proven. Conclusions. Artificial intelligence (AI) for the current generation of dermatovenereologists is becoming an increasingly important auxiliary tool in clinical work, offering new opportunities for early diagnosis, improving personalized treatment and regular professional monitoring of skin diseases, as well as optimizing the work of doctors. Thanks to the use of developed machine learning algorithms, deep neural networks and the ability to deeply analyze human skin images, AI allows you to increase the accuracy of diagnostics, in particular, in recognizing melanoma, psoriasis, eczema and other chronic dermatological pathologies. In the conditions of the current martial law in Ukraine and the limitation of specialized medical care in a number of regions, this is especially relevant for real prediction of the risks of both the development and subsequent recurrence of chronic dermatoses and identification of potentially dangerous skin rashes with high accuracy, even in the early stages of their development.The use of various AI and virtual reality algorithms and the expert online system for early diagnosis of malignant skin neoplasms, as well as a number of chronic dermatoses, created with their technical assistance, will allow for timely diagnosis and prescription of rational therapy for the corresponding pathological conditions.

Ethics of artificial intelligence in dermatology

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.

The use of artificial intelligence (AI) in dermatology is skyrocketing, but a comprehensive overview integrating regulatory, ethical, validation, and clinical issues is lacking. This work aims to review current research, map applicable legal regulations, identify ethical challenges and methods of verifying AI models in dermatology, assess publication trends, compare the most popular neural network architectures and datasets, and identify good practices in creating AI-based applications for dermatological use. A systematic literature review is conducted in accordance with the PRISMA guidelines, utilising Google Scholar, PubMed, Scopus, and Web of Science and employing bibliometric analysis. Since 2016, there has been exponential growth in deep learning research in dermatology, revealing gaps in EU and US regulations and significant differences in model performance across different datasets. The decision-making process in clinical dermatology is analysed, focusing on how AI is augmenting skin imaging techniques such as dermatoscopy and histology. Further demonstration is provided regarding how AI is a valuable tool that supports dermatologists by automatically analysing skin images, enabling faster diagnosis and the more accurate identification of skin lesions. These advances enhance the precision and efficiency of dermatological care, showcasing the potential of AI to revolutionise the speed of diagnosis in modern dermatology, sparking excitement and curiosity. Then, we discuss the regulatory framework for AI in medicine, as well as the ethical issues that may arise. Additionally, this article addresses the critical challenge of ensuring the safety and trustworthiness of AI in dermatology, presenting classic examples of safety issues that can arise during its implementation. The review provides recommendations for regulatory harmonisation, the standardisation of validation metrics, and further research on data explainability and representativeness, which can accelerate the safe implementation of AI in dermatological practice.

The integration of artificial intelligence (AI) in the field of dermatology promises a revolutionary transformation in our approach to this domain. However, the use of AI in dermatology is still in its early stages, and with limited scope of applications. While some of the existing research performed has highlighted the capabilities of AI in diagnosing lesions comparable with those of a dermatologist, the implementation of AI brings about its own ethical challenges that affect both the clinician and the patient. This article aims to explore the ethical issues and prospects surrounding AI implementation in dermatology by drawing insights from the current literature. A qualitative analysis was conducted on articles focusing on AI usage in medical care, with a specific emphasis on dermatology. The analysis unveils several ethical issues that emerge from the implementation of AI in dermatology: the obscure and subjective nature of AI leads to selective bias and unequal treatment based on skin tone differences. It also has the potential to erode patient autonomy by taking on a more paternalistic approach to medicine, increasing the risk of unwarranted harm to patients because of unnecessary biopsies, along with potential misalignment of treatment priorities between AI systems and patients. Issues of accountability arise as AI integration can challenge the epistemic authority and ownership of clinicians over their patients. Finally, the closed-loop system of training algorithms coupled with the lack of an open-access database due to privacy issues further increases the subjectiveness and inaccessibility of each algorithm. While AI’s integration in dermatology promises to streamline the diagnostic process, it also sparks ethical issues that affect the main stakeholders. These findings indicate the need for further development and enhancement of AI systems before they are capable of being implemented. Introduction of a transparent, explainable AI model would alleviate concerns regarding patient autonomy and treatment rationalization. Simultaneously, the establishment of a global open-access database will serve to mitigate selective bias. Furthermore, additional research comparing algorithms would be useful in establishing a standardized validation tool. As it stands, rather than replacing clinicians, AI should be used as a diagnostic adjunct in aiding clinical decisions.

The last years have seen an acceleration in the development and uptake of artificial intelligence (AI) systems by "early adopter" hospitals, caught between the pressures to "perform" and "transform" in a struggling health care system. This transformation has raised concerns among health care providers as their voices and location-specific workflows have often been overlooked, resulting in technologies that fail to integrate meaningfully into routine care and worsen rather than improve care processes. How can positive AI implementation be carried out in health care, aligned with European values? Based on a perspective that spans all stakeholders, we have created EURAID (European Responsible AI Development), a practical, human-centric framework for AI development and implementation based on agreed goals and values. We illustrate this approach through the co-development of a narrow-purpose "in-house" AI system, designed to help bridge the AI implementation gap in real-world clinical settings. This example is then expanded to address the broader challenges associated with complex, multiagent AI systems. By portraying all key stakeholders across the AI development life cycle and highlighting their roles and contributions within the process, real use cases, and methods for achieving iterative consensus, we offer a unique practical approach for safe and fast progress in hospital digital transformation in the AI age.

BACKGROUND Artificial intelligence has been deemed revolutionary in medicine, but very few artificial intelligence solutions have been observed in Danish general practice. General practice in Denmark has an excellent system of digitization to develop and utilize artificial intelligence. However, a lack of involvement of general practitioners in the development of artificial intelligence exists. The perspectives of general practitioners as end users are essential to facilitate the development and implementation of artificial intelligence in general practice. OBJECTIVE This study aimed to characterize the prerequisites that must be met to enable the development and implementation of artificial intelligence in Danish general practice. METHODS This study applied semi-structured interviews and vignettes to gain perspectives on the potential for developing and implementing artificial intelligence among general practitioners. Twelve general practitioners were interviewed, resulting in an exhaustive dataset. The interviews were transcribed, and thematic analysis was conducted to identify the dominant themes throughout the data. RESULTS Four main themes were identified in the data analysis as prerequisites that general practitioners found important to consider when developing and implementing AI in general practice: ‘AI must begin with the low-hanging fruit’, ‘AI must be meaningful in the GP’s work’, ‘The GP-patient relationship must be maintained despite AI’, and ‘AI must be a free, active, and integrated option in the EHR’. CONCLUSIONS The four themes contributing to defining prerequisites for artificial intelligence can potentially lead the first steps of future development and implementation of artificial intelligence in Danish general practice. The participating general practitioners were positive towards developing and implementing artificial intelligence in their clinics, and it would be valuable to consider the defined prerequisites when considering new artificial intelligence tools for general practice.

Artificial Intelligence (AI) has the potential to transform medical delivery by improving patient care and provider experience. Implementing AI into health care is limited in scale, but the projected demand for health care, combined with the shortfall in practitioners, will necessitate the inclusion of AI-based technology in clinical medicine to maintain quality care. AI applications may range from enhancing clinical diagnosis to managing population health through big data. In today’s world, AI scaling in health care is at phase one: AI is either utilized for administrative tasks or imaging. Although the implementation of AI will be difficult, the need for the adoption of AI in the coming years will lead the technology to be a vital aspect of diagnosis and care in and out of the hospital. Dermatology is one medical specialty in which AI applications are in use and in which medical care will evolve. Dermatology has progressed over the years in correspondence with advancements in AI-based technologies such as imaging and medical speech recognition. To better equip future dermatologists, exposure to AI through medical education is necessary for dermatologists to utilize AI effectively. There are hurdles to overcome, but AI is necessary, and it will change health care through effective time management and clinical decision-making. This review, created in collaboration with Precision Pundits, was developed to achieve an understanding of AI in the present-day medical landscape; this project explored the impact AI technology has on dermatology and medical care.

Readiness for Artificial Intelligence in Biobanking

Artificial intelligence in dermatology: The shifting paradigms.

Background: With growth in Artificial Intelligence (AI) adoption, challenges and hurdles are also becoming evident. Organizations implementing AI are challenged to find ways to leverage AI to produce optimum results and benefits for the organization. Understanding other organizations’ AI implementation journeys will help them start and implement AI. By understanding the different facets of AI implementation, they can strategize AI to gain business value. Though several studies have examined AI adoption, there are few studies on how firms implement it. We close this gap by studying AI adoption and implementations in various firms. Method: Using a qualitative approach of semi-structured interviews, we studied twenty global organizations of various sizes that have implemented AI. Results: The study categorizes the results into four major themes – facilitators, barriers, trends, and strategies for implementing AI. Our study reinforces the relevance of the TOE framework and Roger’s DOI theory in studying AI adoption. Organizational factors such as top management support, strategic roadmap, availability of skilled resources, and corporate culture influenced AI adoption. Their lack of data or poor data quality is a primary challenge. The privacy laws concerning data, as well as regulatory bottlenecks, further exacerbate this problem. We also identified and mapped the standard AI implementations to their AI technologies. We found that most of them exploit AI’s image and natural language processing capabilities to automate their processes. Regarding implementation, firms work with partners to obtain customer data and use federated learning. Conclusion: Understanding firms’ AI implementation journey will help us promote further adoption and experimentation. Organizations can identify areas where they can leverage AI to enhance value, prepare themselves for the future, start and proceed with AI implementation efforts and overcome barriers they might encounter.

Emerging From Behind the Workstation.

Focus issue: Artificial intelligence in medical physics.