scholarly journals Rise of the Machines: The Inevitable Evolution of Medicine and Medical Laboratories Intertwining with Artificial Intelligence—A Narrative Review

Diagnostics ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1399
Author(s):  
Janne Cadamuro
Keyword(s):  
Artificial Intelligence ◽  
Laboratory Medicine ◽  
Narrative Review ◽  
Test Results ◽  
Diagnostic Data ◽  
Medical Laboratories ◽  
Making Sense ◽  
Medical Discipline ◽  
The Masses ◽  
Near Future

Laboratory medicine has evolved from a mainly manual profession, providing few selected test results to a highly automated and standardized medical discipline, generating millions of test results per year. As the next inevitable evolutional step, artificial intelligence (AI) algorithms will need to assist us in structuring and making sense of the masses of diagnostic data collected today. Such systems will be able to connect clinical and diagnostic data and to provide valuable suggestions in diagnosis, prognosis or therapeutic options. They will merge the often so separated worlds of the laboratory and the clinics. When used correctly, it will be a tool, capable of freeing the physicians time so that he/she can refocus on the patient. In this narrative review I therefore aim to provide an overview of what AI is, what applications currently are available in healthcare and in laboratory medicine in particular. I will discuss the challenges and pitfalls of applying AI algorithms and I will elaborate on the question if healthcare workers will be replaced by such systems in the near future.

scholarly journals Rise of the Machines: The Inevitable Evolution of Medicine and Medical Laboratories Intertwining with Artificial Intelligence - A Narrative Review

2021 ◽  
Author(s):  
Janne Cadamuro
Keyword(s):  
Artificial Intelligence ◽  
Laboratory Medicine ◽  
Narrative Review ◽  
Test Results ◽  
Diagnostic Data ◽  
Medical Laboratories ◽  
Making Sense ◽  
Medical Discipline ◽  
The Masses ◽  
Near Future

Laboratory medicine has evolved from a mainly manual profession, providing few selected test results to a highly automated and standardized medical discipline, generating millions of test results per year. As next inevitable evolutional step, artificial intelligence (AI) algorithms will need to assist us in structuring and making sense of the masses of diagnostic data collected today. Such systems will be able to connect clinical and diagnostic data and to provide valuable suggestions in diagnosis, prognosis or therapeutic options. They will merge the often so separated worlds of the laboratory and the clinics. When used correctly, it will be a tool, capable of freeing the physicians time so that he/she can refocus on the patient. In this narrative review I therefore aim to provide an overview of what AI is, what applications currently are available in healthcare and in laboratory medicine in particular. I will discuss the challenges and pitfalls of applying AI algorithms and I will elaborate on the question if healthcare workers will be replaced by such systems in the near future.

scholarly journals Metrological traceability and harmonization of medical tests: a quantum leap forward is needed to keep pace with globalization and stringent IVD-regulations in the 21st century!

2018 ◽  
Vol 56 (10) ◽  
pp. 1598-1602 ◽  
Author(s):  
Christa Cobbaert ◽  
Nico Smit ◽  
Philippe Gillery
Keyword(s):  
21St Century ◽  
Clinical Laboratory ◽  
Metrological Traceability ◽  
Laboratory Medicine ◽  
Test Results ◽  
Medical Test ◽  
Traceability Chain ◽  
Medical Laboratories ◽  
Medical Tests ◽  
Quantum Leap

Abstract In our efforts to advance the profession and practice of clinical laboratory medicine, strong coordination and collaboration are needed more than ever before. At the dawn of the 21st century, medical laboratories are facing many unmet clinical needs, a technological revolution promising a plethora of better biomarkers, financial constraints, a growing scarcity of well-trained laboratory technicians and a sharply increasing number of International Organization for Standardization guidelines and new regulations to which medical laboratories should comply in order to guarantee safety and effectiveness of medical test results. Although this is a global trend, medical laboratories across continents and countries are in distinct phases and experience various situations. A universal underlying requirement for safe and global use of medical test results is the standardization and harmonization of test results. Since two decades and after a number of endeavors on standardization/harmonization of medical tests, it is time to reflect on the effectiveness of the approaches used. To keep laboratory medicine sustainable, viable and affordable, clarification of the promises of metrological traceability of test results for improving sick and health care, realization of formal commitment among all stakeholders of the metrological traceability chain and preparation of a joint and global plan for action are essential prerequisites. Policy makers and regulators should not only overwhelm the diagnostic sector with oversight and regulations but should also create the conditions by establishing a global professional forum for anchoring the metrological traceability concept in the medical test domain. Even so, professional societies should have a strong voice in their (inter-) national governments to negotiate long-lasting public policy commitment and funds for global standardization of medical tests.

Laboratory medicine in the 2000s: programmed death or rebirth?

2006 ◽  
Vol 44 (8) ◽  
Author(s):  
Gian Cesare Guidi ◽  
Giuseppe Lippi
Keyword(s):  
Laboratory Medicine ◽  
Diagnostic Efficiency ◽  
Diagnostic Systems ◽  
Laboratory Staff ◽  
Young Professionals ◽  
Medical Laboratories ◽  
Programmed Death ◽  
Invasive Techniques ◽  
Near Future

AbstractChanges have occurred in the organization, complexity and role of medical laboratories in healthcare, requiring a great increase in global productivity and diagnostic efficiency by enrolled professionals to withstand new challenges. Such a radical evolution, which should be very attractive for new generations of professionals, is counterbalanced by an increasing shortage of laboratory vocations worldwide, particularly in community hospital and large reference laboratories, which may lead to a serious crisis in the field of laboratory medicine in the very near future. Some reasons can be highlighted, including the decreased interaction between clinicians and laboratory professionals, centralized testing, and the development of innovative, minimally invasive techniques that can easily be handled without direct control or supervision by laboratory staff. The prospect of a professional decline in laboratory medicine can be offset by increased awareness of the radical changes occurring within clinical laboratories and re-professionalization of laboratory scientists. This will require new resources to attract young professionals, and should include reaffirmation of the role of laboratory consultants and active participation in the development, implementation and monitoring of innovative diagnostic systems. The “patient” appears to be in a serious condition; it is in our hands to let him be reborn.Clin Chem Lab Med 2006;44:913–7.

Quality benchmarking of smartphone laboratory medicine applications: comparison of laboratory medicine specialists’ and non-laboratory medicine professionals’ evaluation

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Snežana Jovičić ◽  
Joanna Siodmiak ◽  
Marta Duque Alcorta ◽  
Maximillian Kittel ◽  
Wytze Oosterhuis ◽  
...  
Keyword(s):  
Information Quality ◽  
Rating Scale ◽  
Laboratory Medicine ◽  
Test Results ◽  
Content Quality ◽  
Laboratory Test Results ◽  
System Usability Scale ◽  
Mobile Health Applications ◽  
Fit For Purpose ◽  
Health Applications

AbstractObjectivesThere are many mobile health applications (apps) now available and some that use in some way laboratory medicine data. Among them, patient-oriented are of the lowest content quality. The aim of this study was to compare the opinions of non-laboratory medicine professionals (NLMP) with those of laboratory medicine specialists (LMS) and define the benchmarks for quality assessment of laboratory medicine apps.MethodsTwenty-five volunteers from six European countries evaluated 16 selected patient-oriented apps. Participants were 20–60 years old, 44% were females, with different educational degrees, and no professional involvement in laboratory medicine. Each participant completed a questionnaire based on the Mobile Application Rating Scale (MARS) and the System Usability Scale, as previously used for rating the app quality by LMS. The responses from the two groups were compared using the Mann-Whitney U test and Spearman correlation.ResultsThe median total score of NLMP app evaluation was 2.73 out of 5 (IQR 0.95) compared to 3.78 (IQR 1.05) by the LMS. All scores were statistically significantly lower in the NLMP group (p<0.05), except for the item Information quality (p=0.1631). The suggested benchmarks for a useful appear: increasing awareness of the importance and delivering an understanding of persons’ own laboratory test results; understandable terminology; easy to use; appropriate graphic design, and trustworthy information.ConclusionsNLMP’ evaluation confirmed the low utility of currently available laboratory medicine apps. A reliable app should contain trustworthy and understandable information. The appearance of an app should be fit for purpose and easy to use.

scholarly journals Artificial Intelligence: The Attitude of the Public and Representatives of Various Industries

2021 ◽  
Vol 14 (8) ◽  
pp. 339
Author(s):  
Tatjana Vasiljeva ◽  
Ilmars Kreituss ◽  
Ilze Lulle
Keyword(s):  
Artificial Intelligence ◽  
Primary Data ◽  
Structured Interviews ◽  
The Public ◽  
Qualitative And Quantitative ◽  
Public Survey ◽  
Significant Difference ◽  
Main Factors ◽  
Negative Factors ◽  
Near Future

This paper looks at public and business attitudes towards artificial intelligence, examining the main factors that influence them. The conceptual model is based on the technology–organization–environment (TOE) framework and was tested through analysis of qualitative and quantitative data. Primary data were collected by a public survey with a questionnaire specially developed for the study and by semi-structured interviews with experts in the artificial intelligence field and management representatives from various companies. This study aims to evaluate the current attitudes of the public and employees of various industries towards AI and investigate the factors that affect them. It was discovered that attitude towards AI differs significantly among industries. There is a significant difference in attitude towards AI between employees at organizations with already implemented AI solutions and employees at organizations with no intention to implement them in the near future. The three main factors which have an impact on AI adoption in an organization are top management’s attitude, competition and regulations. After determining the main factors that influence the attitudes of society and companies towards artificial intelligence, recommendations are provided for reducing various negative factors. The authors develop a proposition that justifies the activities needed for successful adoption of innovative technologies.

scholarly journals A Survey on Bias and Fairness in Machine Learning

2021 ◽  
Vol 54 (6) ◽  
pp. 1-35
Author(s):  
Ninareh Mehrabi ◽  
Fred Morstatter ◽  
Nripsuta Saxena ◽  
Kristina Lerman ◽  
Aram Galstyan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Real World ◽  
State Of The Art ◽  
Future Directions ◽  
Discriminatory Behavior ◽  
Real World Applications ◽  
Near Future ◽  
Different Sources

With the widespread use of artificial intelligence (AI) systems and applications in our everyday lives, accounting for fairness has gained significant importance in designing and engineering of such systems. AI systems can be used in many sensitive environments to make important and life-changing decisions; thus, it is crucial to ensure that these decisions do not reflect discriminatory behavior toward certain groups or populations. More recently some work has been developed in traditional machine learning and deep learning that address such challenges in different subdomains. With the commercialization of these systems, researchers are becoming more aware of the biases that these applications can contain and are attempting to address them. In this survey, we investigated different real-world applications that have shown biases in various ways, and we listed different sources of biases that can affect AI applications. We then created a taxonomy for fairness definitions that machine learning researchers have defined to avoid the existing bias in AI systems. In addition to that, we examined different domains and subdomains in AI showing what researchers have observed with regard to unfair outcomes in the state-of-the-art methods and ways they have tried to address them. There are still many future directions and solutions that can be taken to mitigate the problem of bias in AI systems. We are hoping that this survey will motivate researchers to tackle these issues in the near future by observing existing work in their respective fields.

scholarly journals Peacekeeping Conditions for an Artificial Intelligence Society

2019 ◽  
Vol 3 (2) ◽  
pp. 34
Author(s):  
Hiroshi Yamakawa
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Agents ◽  
Human Society ◽  
Common Value ◽  
Goal Management ◽  
Computer Based ◽  
Common Goals ◽  
Near Future ◽  
Advanced System ◽  
Local Value

In a human society with emergent technology, the destructive actions of some pose a danger to the survival of all of humankind, increasing the need to maintain peace by overcoming universal conflicts. However, human society has not yet achieved complete global peacekeeping. Fortunately, a new possibility for peacekeeping among human societies using the appropriate interventions of an advanced system will be available in the near future. To achieve this goal, an artificial intelligence (AI) system must operate continuously and stably (condition 1) and have an intervention method for maintaining peace among human societies based on a common value (condition 2). However, as a premise, it is necessary to have a minimum common value upon which all of human society can agree (condition 3). In this study, an AI system to achieve condition 1 was investigated. This system was designed as a group of distributed intelligent agents (IAs) to ensure robust and rapid operation. Even if common goals are shared among all IAs, each autonomous IA acts on each local value to adapt quickly to each environment that it faces. Thus, conflicts between IAs are inevitable, and this situation sometimes interferes with the achievement of commonly shared goals. Even so, they can maintain peace within their own societies if all the dispersed IAs think that all other IAs aim for socially acceptable goals. However, communication channel problems, comprehension problems, and computational complexity problems are barriers to realization. This problem can be overcome by introducing an appropriate goal-management system in the case of computer-based IAs. Then, an IA society could achieve its goals peacefully, efficiently, and consistently. Therefore, condition 1 will be achievable. In contrast, humans are restricted by their biological nature and tend to interact with others similar to themselves, so the eradication of conflicts is more difficult.

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