Future-ai:International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

A generic heart diseases prediction and application of genetic algorithms in healthcare systems: Genetic algorithm and machine learning algorithm approaches

In this paper we present a cardiovascular diseases prediction which is referred to as heart diseases. A detail review and application of genetic algorithms in healthcare systems including machine learning algorithms were evaluated. The cardiovascular disease involves narrowed or blocked blood vessels that can lead to a heart attack, angina or stroke, and other heart failures such as muscle, valves or rhythm, which is one of the largest causes of morbidity and mortality among the world population. According to our analysis and results obtained between 85-89 percent of ages greater than 40 years were seriously affected by cardiovascular diseases which provides an imperative result in regards to the previous Ebola outbreak in West Africa in 2014-2016 and the current COVID-19 pandemic of which the aging population were more affected. Our results also indicate, the higher the generation the better prediction and performance and more complex the algorithm. However, with GA and ML approaches are useful to predict the output from the existing data and to match-up the probability computation against the cardiovascular diseases’ dataset

Antibiotic use among hospitalised patients in Sierra Leone: a national point prevalence survey using the WHO survey methodology

Objective Inappropriate use of antibiotics is a major driver of antibiotic resistance. A few studies conducted in Africa have documented that about half of hospitalised patients who receive antibiotics should not have received them. A few hospital-based studies that have been conducted in Sierra Leone have documented a high usage of antibiotics in hospitals. Therefore, we conducted a nationwide point prevalence survey on antibiotic use among hospitalised patients in Sierra Leone.Design We conducted a hospital-based, cross-sectional survey on the use of antibiotics using the WHO point prevalence survey methodology.Setting The study was conducted in 26 public and private hospitals that are providing inpatient healthcare services.Participants All patients admitted to paediatric and adult inpatient wards before or at 08:00 on the survey date were enrolled.Outcome measures Prevalence of antibiotic use, antibiotics Access, Watch and Reserve (AWaRe) categorisation, indication for antibiotic use prevalence and proportion of bacteria culture done.Results Of the 1198 patient records reviewed, 883 (73.7%, 95% CI 71.1% to 76.2%) were on antibiotics. Antibiotic use was highest in the paediatric wards (306, 85.7%), followed by medical wards (158, 71.2%), surgical wards (146, 69.5%), mixed wards (97, 68.8%) and lowest in the obstetrics and gynaecology wards (176, 65.7%). The most widely prescribed antibiotics were metronidazole (404, 22.2%), ceftriaxone (373, 20.5%), ampicillin (337, 18.5%), gentamicin (221, 12.1%) and amoxicillin (90, 5.0%). Blood culture was only done for one patient and antibiotic treatments were given empirically. The most common indication for antibiotic use was community-acquired infection (484, 51.9%) followed by surgical prophylaxis (222, 23.8%).Conclusion There was high usage of antibiotics in hospitals in Sierra Leone as the majority of patients admitted received an antibiotic. This has the potential to increase the burden of antibiotic resistance in the country. We, therefore, recommend the establishment of hospital antimicrobial stewardship programmes according to the WHO core components