Guidelines for Artificial Intelligence in Medicine: Literature Review and Content Analysis of Frameworks (original) (raw)

Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review

npj Digital Medicine, 2022

While the opportunities of ML and AI in healthcare are promising, the growth of complex data-driven prediction models requires careful quality and applicability assessment before they are applied and disseminated in daily practice. This scoping review aimed to identify actionable guidance for those closely involved in AI-based prediction model (AIPM) development, evaluation and implementation including software engineers, data scientists, and healthcare professionals and to identify potential gaps in this guidance. We performed a scoping review of the relevant literature providing guidance or quality criteria regarding the development, evaluation, and implementation of AIPMs using a comprehensive multi-stage screening strategy. PubMed, Web of Science, and the ACM Digital Library were searched, and AI experts were consulted. Topics were extracted from the identified literature and summarized across the six phases at the core of this review: (1) data preparation, (2) AIPM development,...

Exploring the Intersection of Artificial Intelligence and Clinical Healthcare: A Multidisciplinary Review

Diagnostics

Artificial intelligence (AI) plays a more and more important role in our everyday life due to the advantages that it brings when used, such as 24/7 availability, a very low percentage of errors, ability to provide real time insights, or performing a fast analysis. AI is increasingly being used in clinical medical and dental healthcare analyses, with valuable applications, which include disease diagnosis, risk assessment, treatment planning, and drug discovery. This paper presents a narrative literature review of AI use in healthcare from a multi-disciplinary perspective, specifically in the cardiology, allergology, endocrinology, and dental fields. The paper highlights data from recent research and development efforts in AI for healthcare, as well as challenges and limitations associated with AI implementation, such as data privacy and security considerations, along with ethical and legal concerns. The regulation of responsible design, development, and use of AI in healthcare is sti...

Thirty years of artificial intelligence in medicine (AIME) conferences: A review of research themes

Artificial Intelligence in Medicine, 2015

Background: Over the past 30 years, the international conference on Artificial Intelligence in MEdicine (AIME) has been organized at different venues across Europe every 2 years, establishing a forum for scientific exchange and creating an active research community. The Artificial Intelligence in Medicine journal has published theme issues with extended versions of selected AIME papers since 1998. Objectives: To review the history of AIME conferences, investigate its impact on the wider research field, and identify challenges for its future. Methods: We analyzed a total of 122 session titles to create a taxonomy of research themes and topics. We classified all 734 AIME conference papers published between 1985 and 2013 with this taxonomy. We also analyzed the citations to these conference papers and to 55 special issue papers. Results: We identified 30 research topics across 12 themes. AIME was dominated by knowledge engineering research in its first decade, while machine learning and data mining prevailed thereafter. Together these two themes have contributed about 51% of all papers. There have been eight AIME papers that were cited at least 10 times per year since their publication. Conclusions: There has been a major shift from knowledge-based to data-driven methods while the interest for other research themes such as uncertainty management, image and signal processing, and natural language processing has been stable since the early 1990s. AIME papers relating to guidelines and protocols are among the most highly cited.

Artificial Intelligence in Clinical Decision Support: Challenges for Evaluating AI and Practical Implications

Yearbook of Medical Informatics, 2019

Objectives: This paper draws attention to: i) key considerations for evaluating artificial intelligence (AI) enabled clinical decision support; and ii) challenges and practical implications of AI design, development, selection, use, and ongoing surveillance. Method: A narrative review of existing research and evaluation approaches along with expert perspectives drawn from the International Medical Informatics Association (IMIA) Working Group on Technology Assessment and Quality Development in Health Informatics and the European Federation for Medical Informatics (EFMI) Working Group for Assessment of Health Information Systems. Results: There is a rich history and tradition of evaluating AI in healthcare. While evaluators can learn from past efforts, and build on best practice evaluation frameworks and methodologies, questions remain about how to evaluate the safety and effectiveness of AI that dynamically harness vast amounts of genomic, biomarker, phenotype, electronic record, and...

A Review of Clinical Research Incorporating Artificial Intelligence Analyses

2019

Artificial Intelligence is increasingly being used in medical research and certain clinical practice areas. This article examines selected literature to identify common themes with an idea to understanding the impact, concerns and opportunities afforded by such technology. Such research is markedly different from established research methodologies and so is challenging to assess and evaluate. The complexity of the analytics and applied statistics makes it difficult for many to understand and yet conclusions drawn can have a significant impact on clinical practice and policy, funding and recording practices. The key themes identified are those of accuracy, population bias, database limitations, costand time-savings, and the ability to use artificial intelligence to predict future events or outcomes. In addition, certain dangers are highlighted and a few recommendations made.

AI in Medical Research Applications & Considerations -

CSA , 2024

Artificial intelligence (AI) has revolutionized medical research by leveraging machine learning (ML), particularly deep learning (DL), to enhance various facets of healthcare. This paper explores the profound impact of AI across several domains within medical research. This study investigates AI's applications in drug discovery, encompassing de novo drug design, retrosynthesis, reaction prediction, and protein engineering. It also delves into AI's role in diagnosis, treatment, and personalized medicine, focusing on the concept of a Cognitive Digital Twin (CDT). This innovative approach holds promise for predicting treatment outcomes, identifying health risks, and enabling proactive interventions. AI's ethical and legal challenges in medical research are also critically examined. Our opinion is that recent advancements in AI, particularly in deep learning, have significantly bolstered these applications. Emerging methodologies in AI are poised to address complex challenges in drug discovery. Moreover, integrating open data sharing and collaborative model development is pivotal for advancing AI-driven drug discovery . The paper also underscores the ethical and 1 legal considerations accompanying AI's rapid integration into medical research, urging prioritization of these critical issues. We conclude that AI continues to evolve with the potential to transform medical research by enhancing deep learning capabilities and fostering interdisciplinary collaboration. AI promises to drive drug discovery, personalized medicine, and healthcare delivery breakthroughs.

Promise and Provisos of Artificial Intelligence and Machine Learning in Healthcare

Journal of Healthcare Leadership

Artificial Intelligence (AI) and Machine Learning (ML) promise to transform all facets of medicine. Expected changes include more effective clinical triage, enhanced accuracy of diagnostic interpretations, improved therapeutic interventions, augmented workflow algorithms, streamlined data collection and processing, more precise disease prognostication, newer pharmacotherapies, and ameliorated genome interpretation. However, many caveats remain. Reliability of input data, interpretation of output data, data proprietorship, consumer privacy, and liability issues due to potential for data breaches will all have to be addressed. Of equal concern will be decreased human interaction in clinical care, patient satisfaction, affordability, and skepticism regarding cost-benefit. This descriptive literature-based treatise expounds on the promise and provisos associated with the anticipated import of AI and ML into all domains of medicine and healthcare in the very near future.

Artificial intelligence in medicine: the challenges ahead

1996

Abstract The modern study of artificial intelligence in medicine (AIM) is 25years old. Throughout this period, the field has attracted many of the best computer scientists, and their work represents a remarkable achievement. However, AIM has not been successful-if success is judged as making an impact on the practice of medicine. Much recent work in AIM has been focused inward, addressing problems that are at the crossroads of the parent disciplines of medicine and artificial intelligence.

Developing, implementing and governing artificial intelligence in medicine: a step-by-step approach to prevent an artificial intelligence winter

BMJ Health Care Inform, 2022

ObjectiveAlthough the role of artificial intelligence (AI) in medicine is increasingly studied, most patients do not benefit because the majority of AI models remain in the testing and prototyping environment. The development and implementation trajectory of clinical AI models are complex and a structured overview is missing. We therefore propose a step-by-step overview to enhance clinicians’ understanding and to promote quality of medical AI research.MethodsWe summarised key elements (such as current guidelines, challenges, regulatory documents and good practices) that are needed to develop and safely implement AI in medicine.ConclusionThis overview complements other frameworks in a way that it is accessible to stakeholders without prior AI knowledge and as such provides a step-by-step approach incorporating all the key elements and current guidelines that are essential for implementation, and can thereby help to move AI from bytes to bedside.