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

Mobile-Aware Deep Learning Algorithms for Malaria Parasites and White Blood Cells Localization in Thick Blood Smears

Effective determination of malaria parasitemia is paramount in aiding clinicians to accurately estimate the severity of malaria and guide the response for quality treatment. Microscopy by thick smear blood films is the conventional method for malaria parasitemia determination. Despite its edge over other existing methods of malaria parasitemia determination, it has been critiqued for being laborious, time consuming and equally requires expert knowledge for an efficient manual quantification of the parasitemia. This pauses a big challenge to most low developing countries as they are not only highly endemic but equally low resourced in terms of technical personnel in medical laboratories This study presents an end-to-end deep learning approach to automate the localization and count of P.falciparum parasites and White Blood Cells (WBCs) for effective parasitemia determination. The method involved building computer vision models on a dataset of annotated thick blood smear images. These computer vision models were built based on pre-trained deep learning models including Faster Regional Convolutional Neural Network (Faster R-CNN) and Single Shot Multibox Detector (SSD) models that help process the obtained digital images. To improve model performance due to a limited dataset, data augmentation was applied. Results from the evaluation of our approach showed that it reliably detected and returned a count of parasites and WBCs with good precision and recall. A strong correlation was observed between our model-generated counts and the manual counts done by microscopy experts (posting a spear man correlation of ρ = 0.998 for parasites and ρ = 0.987 for WBCs). Additionally, our proposed SSD model was quantized and deployed on a mobile smartphone-based inference app to detect malaria parasites and WBCs in situ. Our proposed method can be applied to support malaria diagnostics in settings with few trained Microscopy Experts yet constrained with large volume of patients to diagnose

Mobile-Aware Deep Learning Algorithms for Malaria Parasites and White Blood Cells Localization in Thick Blood Smears

Effective determination of malaria parasitemia is paramount in aiding clinicians to accurately estimate the severity of malaria and guide the response for quality treatment. Microscopy by thick smear blood films is the conventional method for malaria parasitemia determination. Despite its edge over other existing methods of malaria parasitemia determination, it has been critiqued for being laborious, time consuming and equally requires expert knowledge for an efficient manual quantification of the parasitemia. This pauses a big challenge to most low developing countries as they are not only highly endemic but equally low resourced in terms of technical personnel in medical laboratories This study presents an end-to-end deep learning approach to automate the localization and count of P.falciparum parasites and White Blood Cells (WBCs) for effective parasitemia determination. The method involved building computer vision models on a dataset of annotated thick blood smear images. These computer vision models were built based on pre-trained deep learning models including Faster Regional Convolutional Neural Network (Faster R-CNN) and Single Shot Multibox Detector (SSD) models that help process the obtained digital images. To improve model performance due to a limited dataset, data augmentation was applied. Results from the evaluation of our approach showed that it reliably detected and returned a count of parasites and WBCs with good precision and recall. A strong correlation was observed between our model-generated counts and the manual counts done by microscopy experts (posting a spear man correlation of ρ = 0.998 for parasites and ρ = 0.987 for WBCs). Additionally, our proposed SSD model was quantized and deployed on a mobile smartphone-based inference app to detect malaria parasites and WBCs in situ. Our proposed method can be applied to support malaria diagnostics in settings with few trained Microscopy Experts yet constrained with large volume of patients to diagnose

A Review on Automated Detection and Assessment of Fruit Damage Using Machine Learning

Automation improves the quality of fruits through quick and accurate detection of pest and disease infections, thus contributing to the country’s economic growth and productivity. Although humans can identify the fruit damage caused by pests and diseases, the methods used are inconsistent, time-consuming, and variable. The surface features of fruits typically observed by consumers who seek their health benefits affect their market value. The issue of pest and disease infections further deteriorates fruits’ quality, becoming a mounting stressor on farmers since they reduce the potential revenue from fruit production, processing, and export. This article reviews various studies on detecting and classifying damages in fruits. Specifically, we review articles where state-of-the-art approaches under segmentation, image processing, machine learning, and deep learning have proved effective in developing automated systems that address hurdles associated with manual methods of assessing damage using visual experiences. This survey reviews thirty-two journal and conference papers from the past thirteen years that were found electronically through Google Scholar, Scopus, IEEE, ScienceDirect, and standard online searches. This survey further presents a detailed discussion of previous research done in the past while emphasizing their strengths and limitations as well as outlining potential future research topics. It also reveals that much as the use of automated detection and classification of fruit damage has yielded promising results in the horticulture industry, more research is still needed with systems required to fully automate the detection and classification processes, especially those that are mobile phone-based towards addressing occlusion challenges

A web-based intelligence platform for diagnosis of malaria in thick blood smear images : A case for a developing country

Malaria is a public health problem which affects developing countries world-wide. Inadequate skilled lab technicians in remote areas of developing countries result in untimely diagnosis of malaria parasites making it hard for effective control of the disease in highly endemic areas. The development of remote systems that can provide fast, accurate and timely diagnosis is thus a necessary innovation. With availability of internet, mobile phones and computers, rapid dissemination and timely reporting of medical image analytics is possible. This study aimed at developing and implementing an automated web-based Malaria diagnostic system for thick blood smear images under light microscopy to identify parasites. We implement an image processing algorithm based on a pre-trained model of Faster Convolutional Neural Network (Faster R-CNN) and then integrate it with web-based technology to allow easy and convenient online identification of parasites by medical practitioners. Experiments carried out on the online system with test images showed that the system could identify pathogens with a mean average precision of 0.9306. The system holds the potential to improve the efficiency and accuracy in malaria diagnosis, especially in remote areas of developing countries that lack adequate skilled labor

Establishing spatially-enabled health registry systems using implicit spatial data pools: case study - Uganda

BACKGROUND: Spatial epidemiological analyses primarily depend on spatially-indexed medical records. Some countries have devised ways of capturing patient-specific spatial details using ZIP codes, postcodes or personal numbers, which are geocoded. However, for most resource-constrained African countries, the absence of a means to capture patient resident location as well as inexistence of spatial data infrastructures makes capturing of patient-level spatial data unattainable.METHODS: This paper proposes and demonstrates a creative low-cost solution to address the issue. The solution is based on using interoperable web services to capture fine-scale locational information from existing "spatial data pools" and link them to the patients' information.RESULTS: Based on a case study in Uganda, the paper presents the idea and develops a prototype for a spatially-enabled health registry system that allows for fine-level spatial epidemiological analyses.CONCLUSION: It has been shown and discussed that the proposed solution is feasible for implementation and the collected spatially-indexed data can be used in spatial epidemiological analyses to identify hotspot areas with elevated disease incidence rates, link health outcomes to environmental exposures, and generally improve healthcare planning and provisioning

Machine Learning for Health: Algorithm Auditing & Quality Control.

Developers proposing new machine learning for health (ML4H) tools often pledge to match or even surpass the performance of existing tools, yet the reality is usually more complicated. Reliable deployment of ML4H to the real world is challenging as examples from diabetic retinopathy or Covid-19 screening show. We envision an integrated framework of algorithm auditing and quality control that provides a path towards the effective and reliable application of ML systems in healthcare. In this editorial, we give a summary of ongoing work towards that vision and announce a call for participation to the special issue  Machine Learning for Health: Algorithm Auditing & Quality Control in this journal to advance the practice of ML4H auditing