Use Of Artificial Intelligence In Medicine Research Articles

Attitudes Towards Artificial Intelligence Among Dermatologists Working in Saudi Arabia.

Artificial intelligence (AI) and its applications are among the most discussed modern technologies today. Despite the rapidly expanding use of AI in medicine, and specifically in dermatology, only a few studies have studied the attitude of physicians toward AI. To recognize the attitudes towards AI among dermatologists in the Kingdom of Saudi Arabia. A cross-sectional survey was done among dermatologists in Saudi Arabia. Questionnaires were distributed through several online channels. Overall, 103 dermatologists filled out the survey. The majority saw very strong or strong potential for AI in the automated detection of skin diseases based on dermatological clinical images (50.9%), dermoscopic images (66.6%) and within dermatopathology (66.6%). In regard to results of attitudes towards AI, 56.6% and 52. 8% agreed that AI will revolutionize medicine and dermatology, respectively. However, many of the respondents disagreed that AI will replace physicians (41.5%) and human dermatologists (39.6%) in the future. Age did not impact the overall attitude of dermatologists. Dermatologists in Saudi Arabia showed an optimistic attitude towards AI in dermatology and medicine. However, dermatologists believe that AI will not replace humans in the future.

Building Trust in Medical Use of Artificial Intelligence – The Swarm Learning Principle

ABSTRACTAn avalanche of medical data is starting to be build up. With the digitalisation of medicine and novel approaches such as the omics technologies, we are conquering ever bigger data spaces to be used to describe pathophysiology of diseases, define biomarkers for diagnostic purposes or identify novel drug targets. Utilising this growing lake of medical data will only be possible, if we make use of machine learning, in particular artificial intelligence (AI)-based algorithms. While the technological developments and chances of the data and information sciences are enormous, the use of AI in medicine also bears challenges and many of the current information technologies (IT) do not follow established medical traditions of mentoring, learning together, sharing insights, while preserving patient’s data privacy by patient physician privilege. Other challenges to the medical sector are demands from the scientific community such as “Open Science”, “Open Data”, “Open Access” principles. A major question to be solved is how to guide technological developments in the IT sector to serve well-established medical traditions and processes, yet allow medicine to benefit from the many advantages of state-of-the-art IT. Here, I provide the Swarm Learning (SL) principle as a conceptual framework designed to foster medical standards, processes and traditions. A major difference to current IT solutions is the inherent property of SL to appreciate and acknowledge existing regulations in medicine that have been proven beneficial for patients and medical personal alike for centuries.

Medical Students’ Attitudes Toward AI in Medicine and their Expectations for Medical Education

Objectives Artificial intelligence (AI) is used in a variety of contexts in medicine. This involves the use of algorithms and software that analyze digital information to make diagnoses and suggest adapted therapies. It is unclear, however, what medical students know about AI in medicine, how they evaluate its application, and what they expect from their medical training accordingly. In the study presented here, we aimed at providing answers to these questions. Methods In this survey study, we asked medical students about their assessment of AI in medicine and recorded their ideas and suggestions for considering this topic in medical education. Fifty-eight medical students completed the survey. Results Almost all participants were aware of the use of AI in medicine and had an adequate understanding of it. They perceived AI in medicine to be reliable, trustworthy, and technically competent, but did not have much faith in it. They considered AI in medicine to be rather intelligent but not anthropomorphic. Participants were interested in the opportunities of AI in the medical context and wanted to learn more about it. They indicated that basic AI knowledge should be taught in medical studies, in particular, knowledge about modes of operation, ethics, areas of application, reliability, and possible risks. Conclusions We discuss the implications of these findings for the curricular development in medical education. Medical students need to be equipped with the knowledge and skills to use AI effectively and ethically in their future practice. This includes understanding the limitations and potential biases of AI algorithms by teaching the sensible use of human oversight and continuous monitoring to catch errors in AI algorithms and ensure that final decisions are made by human clinicians.

Potential of artificial intelligence in the risk stratification for and early detection of pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is the third most lethal cancer in the United States, with a 5-year life expectancy of 11%. Most symptoms manifest at an advanced stage of the disease when surgery is no longer appropriate. The dire prognosis of PDAC warrants new strategies to improve the outcomes of patients, and early detection has garnered significant attention. However, early detection of PDAC is most often incidental, emphasizing the importance of developing new early detection screening strategies. Due to the low incidence of the disease in the general population, much of the focus for screening has turned to individuals at high risk of PDAC. This enriches the screening population and balances the risks associated with pancreas interventions. The cancers that are found in these high-risk individuals by MRI and/or EUS screening show favorable 73% 5-year overall survival. Even with the emphasis on screening in enriched high-risk populations, only a minority of incident cancers are detected this way. One strategy to improve early detection outcomes is to integrate artificial intelligence (AI) into biomarker discovery and risk models. This expert review summarizes recent publications that have developed AI algorithms for the applications of risk stratification of PDAC using radiomics and electronic health records. Furthermore, this review illustrates the current uses of radiomics and biomarkers in AI for early detection of PDAC. Finally, various challenges and potential solutions are highlighted regarding the use of AI in medicine for early detection purposes.

Women's perceptions and attitudes to the use of AI in breast cancer screening: a survey in a cancer referral centre.

Although breast cancer screening can benefit from Artificial Intelligence (AI), it is still unknown whether, to which extent or under which conditions, the use of AI is going to be accepted by the general population. The aim of our study is to evaluate what the females who are eligible for breast cancer screening know about AI and how they perceive such innovation. We used a prospective survey consisting of a 11-multiple-choice questionnaire evaluating statistical associations with Chi-Square-test or Fisher-exact-test. Multinomial-logistic-regression was performed on items with more than two response categories. Odds ratio (OR) with 95% CI were computed to estimate the probability of a specific response according to patient's characteristics. In the 800 analysed questionnaires, 51% of respondents confirmed to have knowledge of AI. Of these, 88% expressed a positive opinion about its use in medicine. Non-Italian respondents were associated with the belief of having a deep awareness about AI more often than Italian respondents (OR = 1.91;95% CI[1.10-3.33]). Higher education level was associated with better opinions on the use of AI in medicine (OR = 4.69;95% CI[1.36-16.12]). According to 94% of respondents, the radiologists should always produce their own report on mammograms, whilst 77% agreed that AI should be used as a second reader. Most respondents (52%) considered that both the software developer and the radiologist should be held accountable for AI errors. Most of the females undergoing screening in our Institute approve the introduction of AI, although only as a support to radiologist, and not in substitution thereof. Yet, accountability in case of AI errors is still unsolved. advances in knowledge:This survey may be considered as a pilot-study for the development of large-scale studies to understand females's demands and concerns about AI applications in breast cancer screening.

Explainability and artificial intelligence in medicine

In recent years, improved artificial intelligence (AI) algorithms and access to training data have led to the possibility of AI augmenting or replacing some of the current functions of physicians.1 However, interest from various stakeholders in the use of AI in medicine has not translated to widespread adoption.2 As many experts have stated, one of the key reasons for this restricted uptake is the scarce transparency associated with specific AI algorithms, especially black-box algorithms.3 Clinical medicine, primarily evidence-based medical practice, relies on transparency in decision making.

Legal Urgency on Designing The Legislation for The Use of Artificial Intelligence in Indonesian Medical Practice

Artificial intelligence (AI) offers the potential for a great improvement in patient care, both in diagnose and disease treatment, and a consequential reduction in healthcare costs, a part of opportunities and challenge are ahead. The use of AI in medicine was significantly developed in some countries. Indonesia as a modern country also has a great change in promoting the use of AI. The study aims to propose on designing the legislation for the use of AI in Indonesian medical practices. The method used in this research is normative juridical approaches with descriptive analysis. The data used are primary legal material namely the Indonesian Penal Code and Law No. 36 of 2009 on Health Law. Meanwhile, the secondary legal material used are books, journals, and other legal documents. The results show that designing the new legislation as the guidance and basis for the use of AI shall give a good impact on the development of health services as practices among other countries. Moreover, Health Act 2009 clearly supported the use of advance technology’s product in medicine. Yet, the application of AI facilitates interpretation follows with high accuracy and speed for medical diagnoses.

361 The Automation of Doctors and Machines: A Classification for AI in Medicine (ADAM framework)

Abstract Aim The advances in Artificial Intelligence (AI) provide an opportunity to expand the frontier of medicine to improve diagnosis, efficiency, and management. By extension of being able to perform any task that a human could, a machine that meets the requirements of General AI (AGI) possesses the basic necessities to perform as, or at least qualify to become, a doctor. In this emerging field, this article explores the distinctions between doctors and AGI, and the prerequisites for AGI performing as clinicians. With its imminent arrival, it is beneficial to create a framework from which leading institutions can define specific criteria for AGI. Method A normative framework was derived from medical ethical literature and current medical technology. Comparisons were made between current capabilities and the traits of doctors ('doctorhood'). A framework was created that could fulfil current patient and doctor considerations for the use of AI in medicine. Results This Automation of Doctors and Machines (ADAM) framework is set out across 5 levels. As the level progresses, so do the minimum requirements in the core competencies of knowledge, safety, emotion, and independence. Conclusions The development of AI brings with it an exciting era of modern medicine. In order to fully enhance, expand, and regulate this field, the ADAM framework provides a tool to classify its use in medicine. In being able to categorize forms of medical AI, this allows clinicians, patients, and regulators to delineate different forms of AI, and a foundation is created from which governing bodies can set and standardise levels of care.

Artificial Intelligence/Machine Learning in Respiratory Medicine and Potential Role in Asthma and COPD Diagnosis

Artificial intelligence (AI) and machine learning, a subset of AI, are increasingly used in medicine. AI excels at performing well-defined tasks, such as image recognition; for example, classifying skin biopsy lesions, determining diabetic retinopathy severity, and detecting brain tumors. This article provides an overview of the use of AI in medicine and particularly in respiratory medicine, where it is used to evaluate lung cancer images, diagnose fibrotic lung disease, and more recently is being developed to aid the interpretation of pulmonary function tests and the diagnosis of a range of obstructive and restrictive lung diseases. The development and validation of AI algorithms requires large volumes of well-structured data, and the algorithms must work with variable levels of data quality. It is important that clinicians understand how AI can function in the context of heterogeneous conditions such as asthma and chronic obstructive pulmonary disease where diagnostic criteria overlap, how AI use fits into everyday clinical practice, and how issues of patient safety should be addressed. AI has a clear role in providing support for doctors in the clinical workplace, but its relatively recent introduction means that confidence in its use still has to be fully established. Overall, AI is expected to play a key role in aiding clinicians in the diagnosis and management of respiratory diseases in the future, and it will be exciting to see the benefits that arise for patients and doctors from its use in everyday clinical practice.

Artificial Intelligence in the Healthcare System: An Overview.

This chapter aims to present insights into the influence of artificial intelligence (AI) on medicine, public health, and the economy. PubMed and Google Scholar databases were used for the identification and collection of articles with search commands of "artificial intelligence" AND "public health" and "artificial intelligence" AND "medicine". A total of 273 articles specifically handling the issue of artificial intelligence, dating ten years back, in three major medical journals: Science, The Lancet, and The New England Journal of Medicine, were analyzed. Computational power gets stronger by the day, giving us new solutions and possibilities. Current medicine problems like personalized medicine, storage of data, and documentation overload will likely be replaced by AI shortly. The application of AI may also bring substantial benefits to other areas of medicine like the diagnostic and therapeutic processes. The development and spread of AI are inescapable as it lowers healthcare and administrative costs, improves medical efficiency, and predicts and prevents major disease complications. The use of AI in medicine seems destined to carry the day.

Ethics of Artificial Intelligence in Medicine and Ophthalmology.

Background:This review explores the bioethical implementation of artificial intelligence (AI) in medicine and in ophthalmology. AI, which was first introduced in the 1950s, is defined as “the machine simulation of human mental reasoning, decision making, and behavior”. The increased power of computing, expansion of storage capacity, and compilation of medical big data helped the AI implementation surge in medical practice and research. Ophthalmology is a leading medical specialty in applying AI in screening, diagnosis, and treatment. The first Food and Drug Administration approved autonomous diagnostic system served to diagnose and classify diabetic retinopathy. Other ophthalmic conditions such as age-related macular degeneration, glaucoma, retinopathy of prematurity, and congenital cataract, among others, implemented AI too.Purpose:To review the contemporary literature of the bioethical issues of AI in medicine and ophthalmology, classify ethical issues in medical AI, and suggest possible standardizations of ethical frameworks for AI implementation.Methods:Keywords were searched on Google Scholar and PubMed between October 2019 and April 2020. The results were reviewed, cross-referenced, and summarized. A total of 284 references including articles, books, book chapters, and regulatory reports and statements were reviewed, and those that were relevant were cited in the paper.Results:Most sources that studied the use of AI in medicine explored the ethical aspects. Bioethical challenges of AI implementation in medicine were categorized into 6 main categories. These include machine training ethics, machine accuracy ethics, patient-related ethics, physician-related ethics, shared ethics, and roles of regulators.Conclusions:There are multiple stakeholders in the ethical issues surrounding AI in medicine and ophthalmology. Attention to the various aspects of ethics related to AI is important especially with the expanding use of AI. Solutions of ethical problems are envisioned to be multifactorial

Is my cough COVID-19?

Is my cough COVID-19?

Assessing perspectives on artificial intelligence applications to gastroenterology

Assessing perspectives on artificial intelligence applications to gastroenterology

The Increasing Role of Artificial Intelligence in Health Care: Will Robots Replace Doctors in the Future?

Artificial intelligence (AI) pertains to the ability of computers or computer-controlled machines to perform activities that demand the cognitive function and performance level of the human brain. The use of AI in medicine and health care is growing rapidly, significantly impacting areas such as medical diagnostics, drug development, treatment personalization, supportive health services, genomics, and public health management. AI offers several advantages; however, its rampant rise in health care also raises concerns regarding legal liability, ethics, and data privacy. Technological singularity (TS) is a hypothetical future point in time when AI will surpass human intelligence. If it occurs, TS in health care would imply the replacement of human medical practitioners with AI-guided robots and peripheral systems. Considering the pace at which technological advances are taking place in the arena of AI, and the pace at which AI is being integrated with health care systems, it is not be unreasonable to believe that TS in health care might occur in the near future and that AI-enabled services will profoundly augment the capabilities of doctors, if not completely replace them. There is a need to understand the associated challenges so that we may better prepare the health care system and society to embrace such a change – if it happens.

The impact of the introduction of artificial intelligence in radiology and its potential legal implications in the UK and Ireland.

Artificial intelligence (AI) has been defined as a branch of computer science dealing with the capability and simulation of a machine to imitate intelligent human behaviour. Diagnostic radiology, being a computer-based service, is unsurprisingly at the forefront of the discussion of the use of AI in medicine. There are however differing schools of thought regarding its use; namely, will AI eventually replace the radiologist? Or indeed will it ever be fully capable of replacing radiology as a speciality, but rather be used as an aid to the profession whereby a human’s input will always be required?Furthermore, what will the legal implications of AI in radiology mean to the profession? Who will be liable for missed diagnoses? Is it possible that the introduction of AI to radiology will in fact make the profession busier?

The top 100 most cited articles in medical artificial intelligence: a bibliometric analysis

Background: There is growing interest in the use of artificial intelligence (AI) in medicine. Our objective was to identify and analyse the characteristics of the top 100 most cited articles relating to the use of AI in medicine in order to identify research trends and help direct future research. Methods: A retrospective bibliometric analysis of the 100 most cited articles relating to the use of AI in medicine between the years 1950 and 2019 was performed. Data extracted for analysis included year of publication, authorship, journal title and impact factor, institution, country of origin, article type, keywords, and field of medicine. Results: The number of citations for the top 100 articles ranged from 176 to 1,475, with a median [interquartile range (IQR)] of 238 [205–347]. The median [IQR] number of citations per year was 21 [16–41]. The majority of the top 100 articles [85] were published in the last two decades. Over half of the top 100 articles [55] originated from the United States. There were 60 original research articles featured, with 11 of these clinical studies. The most represented fields were medical informatics [25] and radiology [21]. Oncology is pioneering the clinical integration of AI applications, while cardiovascular medicine is lacking in AI research despite its high disease burden. Conclusions: This study highlights that the current citation classics are largely in the non-clinical, experimental phase and have yet to progress to the clinical, integration phase of medical AI.

Does artificial intelligence (AI) constitute an opportunity or a threat to the future of medicine as we know it?

By far the greatest danger of Artificial Intelligence is that people conclude too early that they understand it. Eliezer Yudkowsky, Machine Intelligence Research Institute1 Artifical intelligence (AI) is the future and is already part of our everyday life. Be it voice recognition assistance with Apple’s Siri or Amazon’s Alexa, or algorithms that filter your spam emails, recommend a film to you on Netflix, screen your bank account transactions for fraud, or get your auto-pilot flight smoothly to your next holiday destination, chances are you have already experienced AI. Machine ‘deep learning’ takes place in a multi-layered ‘black box’ of deep neural networks, where algorithms are not defined by task-specific rules, but are able to evolve and self-learn using pattern recognition and trial and error. Thus, AI can approach problems as a doctor progressing through their training does: by learning rules from data. However, by having the capacity to analyse massive amounts of data, algorithms are able to find correlations that the human mind cannot. For some, however, the use of AI in medicine remains a troubling concept. Science fiction is littered with examples of AI running amok at the expense of humanity. While the use of AI in medicine does not evoke exactly the same kind of Orwellian concerns as it might in, for example, national defence, there are still important issues such as privacy, data protection, even the straightforward need for simple human contact to consider. Doctors are expected to temper knowledge with compassion and understanding. These are characteristics which some fear may be lost in any AI driven system, where patients may find their rights are, at best, an afterthought …

AI in Health: State of the Art, Challenges, and Future Directions.

Summary Introduction : Artificial intelligence (AI) technologies continue to attract interest from a broad range of disciplines in recent years, including health. The increase in computer hardware and software applications in medicine, as well as digitization of health-related data together fuel progress in the development and use of AI in medicine. This progress provides new opportunities and challenges, as well as directions for the future of AI in health. Objective : The goals of this survey are to review the current state of AI in health, along with opportunities, challenges, and practical implications. This review highlights recent developments over the past five years and directions for the future. Methods : Publications over the past five years reporting the use of AI in health in clinical and biomedical informatics journals, as well as computer science conferences, were selected according to Google Scholar citations. Publications were then categorized into five different classes, according to the type of data analyzed. Results: The major data types identified were multi-omics, clinical, behavioral, environmental and pharmaceutical research and development (R&D) data. The current state of AI related to each data type is described, followed by associated challenges and practical implications that have emerged over the last several years. Opportunities and future directions based on these advances are discussed. Conclusion : Technologies have enabled the development of AI-assisted approaches to healthcare. However, there remain challenges. Work is currently underway to address multi-modal data integration, balancing quantitative algorithm performance and qualitative model interpretability, protection of model security, federated learning, and model bias.

前言: 人工智能與未來醫學: 超越技術主義

LANGUAGE NOTE | Document text in Chinese; abstract in English only.The essays in this issue indicate that in reflecting on the use of artificial intelligence (AI) in medicine, society should go beyond the technical level. While it is important to determine both the benefits and the risks of AI systems for health care, it is also necessary to go deeper by exploring how technology frames human lives. Only in this way can we move beyond the trap of technologicalism, which holds not only that science and technology are the main driving forces in our lives, but also that humans should follow these forces and adjust their values and views accordingly. The essays in this issue suggest that humans should instead rely on their full set of values (which are not and should never be determined by technology) to determine and regulate the use of AI in medicine. This mission requires profound philosophical and ethical reflection.DOWNLOAD HISTORY | This article has been downloaded 76 times in Digital Commons before migrating into this platform.