Artificial intelligence is here. It is a tool that can provide tremendous opportunities and challenging obstacles for the care continuum. The major question facing healthcare organizations is how to harness this technology ethically and efficiently.
Whether AI can advance the interests of patients and communities depends on a collective effort to design and implement ethically defensible laws and policies and ethically designed AI technologies. There can also be serious negative consequences if ethical principles and human rights obligations are not prioritized by those who fund, design, regulate or use AI technologies for health.1
As technology continues to progress, regulatory frameworks often struggle to keep pace with these developments, and adequate capacity to implement AI effectively lags behind.2
In this blog, we will discuss how artificial intelligence can be integrated into diagnostics to drive advancements and efficiency in diagnostic technology.
The Role of AI in Enhancing Diagnostics
AI can assist providers in a variety of patient care and intelligent health systems, with techniques ranging from machine learning to deep learning are prevalent in healthcare for disease diagnosis, drug discovery, and patient risk identification. Numerous medical data sources are required to perfectly diagnose diseases using artificial intelligence techniques, such as ultrasound, magnetic resonance imaging, mammography, genomics, and computed tomography scans.3
With the recent AI revolution, medical diagnostics could be improved to revolutionize the field of medical diagnostics by improving the prediction accuracy, speed, and efficiency of the diagnostic process. AI algorithms can analyze medical images and assist healthcare providers in identifying and diagnosing diseases more accurately and quickly. AI can analyze large amounts of patient data, including medical 2D/3D imaging, bio-signals, vital signs, demographic information, medical history, and laboratory test results.4
Benefits of AI in Diagnostics and Laboratory Medicine
AI is being used in laboratory medicine to improve accuracy and efficiency, streamline and improve diagnostic processes, and help in clinical decision‐making. AI developments in laboratory medicine are so important because of its capacity to analyze large amounts of data, spot trends, and produce useful insights swiftly and accurately. Additionally, AI can help interpret laboratory test findings, forecast patient outcomes, and assist in the early diagnosis of diseases by utilizing machine learning algorithms.5
The complex and dynamic processes involved in the development, deployment, use, and maintenance of AI technologies benefit from careful management throughout the medical product life cycle.6
AI developments in laboratory medicine are so important because of its capacity to analyze large amounts of data, spot trends, and produce useful insights swiftly and accurately.
Artificial intelligence can transform present diagnostic, disease preventive and control techniques, dramatically improving patient safety and treatment quality. To enhance workflow and personnel utilization, labs now employ software to automate samples, operation, and outcome management. Simultaneously, sophisticated systems monitor activities to identify bottlenecks and warn of possible problems, such as STAT sample delays or reagent expiry.7
Challenges and Ethical Considerations of Integrating AI
One of the most significant challenges of integrating AI into a healthcare setting is the ethical ramifications of AI and patient care. The use of AI can lead to situations in which decision-making power could be transferred to machines. The autonomy principle requires that AI or other computational systems do not undermine human autonomy. In the context of health care, this means that humans should remain in control of health-care systems and medical decisions. The designers of AI technologies should satisfy regulatory requirements for safety, accuracy and efficacy for well-defined use cases or indications. Measures of quality control in practice and quality improvement in AI over time should be available.8
Additionally, there’s the question of accessibility to the technology. AI technologies should be intelligible or understandable to developers, medical professionals, patients, users and regulators. Two broad approaches to intelligibility are to improve the transparency of AI technology and to make AI technology explainable. Transparency requires that sufficient information be published or documented before the design or deployment of an AI technology and that such information facilitate meaningful public consultation and debate on how the technology is designed and how it should or should not be used.9
SEKISUI’s Commitment to Innovating AI-Powered Diagnostics
Over the last few years, SEKISUI has been involved in cutting edge advances in AI technology designed to improve the physician’s workflow and the patient’s outcomes.
In 2021, SEKISUI KYDEX partnered with Avasure to develop Avasure’s Telesitter Solutions, a virtual patient engagement platform that is able to continuously see, hear, and talk to patients without recording video or sound.10
This year, SEKISUI and Hitachi developed a proof concept for the Marketplace System for Recycled Materials, which provides an online service that matches buyers looking to purchase recycled materials as raw materials and sellers looking to circulate waste as recycled materials, enabling a series of transaction processes.11
With these recent initiatives, SEKISUI is committed to implementing AI in diagnostic innovation to improve accuracy, precision, and scalability.
References
- Ethics and governance of artificial intelligence for health: WHO guidance Executive summary, World Health Organization
- Artificial Intelligence for Health, World Health Organization
- Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda, National Library of Medicine
- Artificial Intelligence for Medical Diagnostics—Existing and Future AI Technology!, National Library of Medicine
- Impacts of the advancement in artificial intelligence on laboratory medicine in low‐ and middle‐income countries: Challenges and recommendations—A literature review, National Library of Medicine
- Artificial Intelligence and Machine Learning in Software as a Medical Device, FDA
- Integrating Artificial Intelligence for Clinical and Laboratory Diagnosis – a Review, National Library of Medicine
- Ethics and governance of artificial intelligence for health: WHO guidance Executive summary, World Health Organization
- Ethics and governance of artificial intelligence for health: WHO guidance Executive summary, World Health Organization
- Innovation Story: Avasure & SEKISUI KYDEX Collaboration, Kydex
- Hitachi and SEKISUI CHEMICAL completed proof of concept for “Marketplace System for Recycled Materials which accelerate the use of recycled materials, SEKISUI CHEMICAL
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