Laboratory medicine aggressively employs new technology to enhance clinical decision-making, disease monitoring, and patient safety. The potential for innovation to transform healthcare systems and laboratory medicine is enormous, and it has the potential to equip healthcare professionals with the information and tools they need to enhance the quality of care offered to more patients with the same or fewer resources. Because of recent advancements, data and AI have become increasingly powerful tools.
Data science and AI are already changing how we live as people and how our businesses, markets, and governments operate. Artificial intelligence (AI) comprises a wide variety of approaches to replicating human cognition, ranging from problem-solving and learning to computers mixed with complex mathematical models. AI has the potential to significantly improve patient quality and safety of care by transforming current diagnostic and disease prevention and control methods, making it one of the most promising fields of application for big data and AI.
As the access to massive amounts of data becomes more readily available, the promises and expectations of the big data and AI sectors are rising exponentially. Compared to other medical fields, laboratory medicine has been among the most technologically sophisticated. Since their debuts, automation, electronic result transmission, and electronic reporting have all achieved significant acceptance.
Furthermore, medical laboratories have extensive databases containing test results and quality control outcomes, and often feature advanced quality management systems. As a result, it should be no surprise that laboratory medicine is a model sector for medical innovation in the digital era. On the other hand, cutting-edge data science advances like AI and ML have yet to reach many fields of laboratory medicine. Regardless, the time has come. Researchers now have access to three critical components for enhancing laboratory medicine: learning and training algorithms, computing power to run those algorithms, and vast amounts of data.
Recent COVID-19 outbreaks are thought to have contributed to this spread. Even if its worldwide spread has had terrible public health consequences, the pandemic may be a driving force for technological innovation and artificial intelligence. Because of the growing demand for high-quality care and the diminishing availability of resources, healthcare practitioners must embrace the concept of adaptability to a changing technological environment and overall current practices.
Following the global incident, the European Commission published the Expert Panel on Effective Ways of Investing in Health’s Opinion on how to structure resilient health and social services. The advisory presents a framework for health-system resilience testing. It underlines the components and conditions for capacity building to strengthen health-system resilience, as well as to address the healthcare needs of vulnerable patients during times of crisis. Among the many themes discussed were the necessity of data, data integration, and artificial intelligence in countering unanticipated outbreaks.
Several reports have been published about the successful implementation of AI-based solutions for COVID-19, such as AI-based outbreak monitoring apps, AI-based diagnostic Chabots, AI-based assessment of scholarly publications, AI-based triage utilising text analytics for evaluating probable patients, AI-powered prognosis prediction tools utilising radiology CT scans; and so on.
Today, there are several examples of evidence for treating non-communicable chronic diseases such as cancer or cardiovascular difficulties, as well as the added advantage of big data and AI. Artificial intelligence technologies are designed to increase patient safety, treatment quality, and the speed with which clinicians can treat their patients. There is no question that big data and AI can help with decision-making by improving diagnostic and predictive capabilities. In cancer and cardiovascular medicine fields, artificial intelligence (AI) has shown considerable promise in extracting deep phenotypic information from imaging, laboratory, and other medical device data.
AI provides benefits in various domains, including discovering novel genotypes and phenotypes in existing chronic diseases, improving patient treatment quality, and facilitating cost-effectiveness via reduced readmission and mortality rates. The application of AI-based technology may predict the success or failure of clinical research and the unintended effects of polypharmacy combinations.
Furthermore, “digital twins” and other AI-powered technologies may aid clinical teams in planning clinical trials by connecting eligible patients to relevant studies, enabling clinicians to forecast how a certain illness or treatment would affect patients. Big Data and AI are clear facilitators of personalised medicine via early risk prediction, prevention, and therapeutic action. Laboratory and biological data will be very useful to artificial intelligence systems.
Big data and artificial intelligence have a critical therapeutic impact. They also have the ability to make laboratories more sustainable and efficient by reducing waste, simplifying operations, and allowing for more rational ordering of lab tests. With the value and benefits of big data and AI becoming more palpable, it is more critical than ever to incorporate the technologies into day-to-day operations properly.
There are still numerous challenges to overcome, such as the need to build data ecosystems and architectures that will feed and shape AI, merge and reap the benefits of the upcoming “European Data Space”, include healthcare staff in external validation and demonstrate the generalisation of AI technologies, integrate digital and AI-related material into existing training and education programmes for medical professionals, and create a strong political and legislative framework.
Experts in laboratory medicine and clinical laboratories will play critical roles in this transition, with responsibilities including, at a minimum, the provision of standardised and organised data, advise on the type of data to be used, the integration of multidisciplinary teams for the validation of AI-derived instruments, and the use of these devices to maximise healthcare quality, lab procedures, and outcomes.
The application of data science, big data, and AI in its various applications, from protection and testing to early diagnosis and illness management, may radically improve the healthcare experience for patients at every stage of the continuum. The successful and secure integration of big data and AI technologies, as well as staff training and patient education, will depend largely on diagnostics and clinical laboratory specialists’ skills. International healthcare systems stand to benefit greatly from the careful use of big data and AI technologies.
References available on request.
Abdulaziz M Aljohani is the Operations Administrator, Laboratory Services at Prince Mohammed bin Abdulaziz Hospital in Riyadh, and Najah Almutairi, Medical Technologist I, Pathology & Laboratory Department, Prince Mohammed Bin Abdulaziz Hospital — MNG-HA in Medina.
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This article appears in the latest issue of Omnia Health Magazine. Read the full issue online today.
