Introduction
Imagine a machine assessing, diagnosing, interpreting, and predicting a patient’s health status with near-perfect accuracy – something that once seemed like science fiction. Yet, this is becoming a reality with the advent of artificial intelligence (AI).
Medical diagnosis using AI
Recently, a friend of mine experienced this offhand. When he fell ill and was admitted to a local hospital, he expected a traditional consultation with a doctor. Instead, he was greeted by a sleek, advanced robot. To his surprise, the robot instructed him to undergo what it called “a diagnostic hug.” Moments later, the machine provided a detailed medical report and accurately diagnosed his condition. This experience left him in awe of AI’s groundbreaking potential. We are now witnessing AI’s profound impact across various fields, and healthcare is no exception. In this blog, we will explore the transformative power of artificial intelligence in healthcare diagnosis and envision the future possibilities of this technological marvel.
What is Artificial Intelligence (AI)?
Artificial intelligence (AI) is a technology that enables machines to think and act intelligently, automate tasks, and learn from data to achieve goals. At its core, AI is human-designed intelligence that uses machines to aid in problem-solving and streamlining processes, mimicking human thought processes (Tai, M. C. 2020). Simply put, AI is a machine learning algorithm that simulates human intelligence. It is programmed to act like a human and perform tasks that require intelligence such as learning, adaptation, creativity, reasoning, inference, problem-solving, and decision-making. Through its algorithms, AI can gather and analyze data, patterns, and trends to make scientific, data-driven decisions.
Historical Concept
Evolution and development of AI
The concept of simulating human intelligence with computers was introduced by Alan Turing in 1950. Known as the Turing test, this idea proposed a way to measure a machine’s ability to think like a human. In 1956, John McCarthy coined the term “artificial intelligence” (AI) and described it as the development of intelligent machines (Kaul, V. et al. 2020).
The application of AI in medical diagnostics dates back to the 1970s with the development of the MYCIN backward chaining AI system (Kaul, V. et al., p. 809). MYCIN allowed physicians to input patient information and generate potential bacterial pathogens, subsequently recommending antibiotic treatment based on the generated results. Another early AI system, CASNET, provided personalized glaucoma treatment recommendations, highlighting AI’s potential in medical care (Kaul, V. et al., p. 809). Over the decades, systems like MYCIN and CASNET paved the way for further developments such as EMYCIN, INTERNIST-1, and Dxplain, which laid the groundwork for modern machine learning (ML) and artificial intelligence in medicine (AIM). These early systems, used primarily for disease diagnosis, set the stage for today’s AI applications in medical diagnostics.
How AI Enhances Diagnostic Accuracy
AI enhances diagnostic accuracy by leveraging sophisticated algorithms, machine learning techniques, and extensive datasets to detect patterns, trends, and anomalies that may indicate a medical condition. AI’s ability to collect, organize, and analyze medical data is pivotal in improving diagnostic accuracy. The integration of medical imaging and AI has revolutionized healthcare by contributing to a more scientific approach to diagnosis, significantly improving disease detection, treatment, and patient outcomes.
For instance, medical imaging techniques like CT, MRI, and PET scans provide detailed visual information, while AI analyzes the resulting data to extract valuable insights. Deep learning algorithms recognize complex patterns, enhance diagnostic accuracy, and support decision-making. AI-assisted image analysis also enables early disease detection, improves treatment planning, and personalizes medicine (Pinto-Coelho, 2023).
The Need for AI in Healthcare
With its capacity to provide scientific data that can be leveraged, the advent of machine learning and artificial intelligence in medical diagnosis is timely for several reasons. First, the increasing global population, coupled with a shortage of healthcare professionals, has created a significant demand for efficient healthcare delivery. According to a 2023 report by the Health Resources & Services Administration, approximately 102 million people live in primary care Health Professional Shortage Areas (HPSAs), and 77 million live in dental health HPSAs (https://bhw.hrsa.gov/sites/default/files/bureau-health-workforce/data-research/state-of-the-health-workforce-report-2023.pdf.
AI revolution in healthcare delivery
Additionally, the risks and hazards associated with the healthcare profession have grave consequences. The World Health Organization estimated that between 80,000 and 180,000 health and care workers could have died from COVID-19 between January 2020 and May 2021 - https://www.who.int/news/item/20-10-2021-health-and-care-worker-deaths-during-covid-19.
Second, diagnostic errors can lead to incorrect prescriptions, medication, and management. According to Poalelungi et al. (2023), “The fundamental goal of the diagnosis of a disease lies in determining whether a patient is affected by a disease or not [and the] first step in the diagnostic process involves obtaining a complete medical history and conducting a physical examination” Poalelungi et al. (2023), Errors in diagnosis, such as incorrect medication prescriptions, have resulted in severe consequences, sometimes even fatalities. AI algorithms, however, provide instantaneous scientific data with minimal error margins, aiding in accurate disease diagnosis, predicting patient outcomes, tailoring treatment plans, and improving patient flow, scheduling, and image analysis accuracy in radiology and pathology - doi: 10.3390/bioengineering11040337.
Medical Imaging
One significant application of AI in diagnostics is medical imaging. Medical imaging refers to the techniques used to visualize the body’s internal structures, aiding healthcare professionals in diagnosing, monitoring, and treating medical conditions by providing detailed insights into organs and tissue health. Examples include X-rays, MRIs, and CT scans.
Medical imaging in AI diagnosis
Predictive AnalyticsA core element of AI is its predictive analytics capability. Beyond merely analyzing historical data from the body’s organs, tissues, and structures, AI can leverage this data to predict the likelihood of disease conditions such as diabetes, cancer, and liver cirrhosis. It can assess factors such as family history, lifestyle choices, and dietary patterns to provide risk assessment.
Genetic Disorder
AI-powered algorithms can help detect genetic disorders and other genetic conditions, providing a crucial support system for medical professionals in making informed and timely decisions.
Benefits of AI in Health Diagnostics
Diagnostic Accuracy
AI can gather, organize, and analyze vast amounts of data in minutes – a process that might take humans months – thereby reducing human error and enhancing the reliability of diagnoses.
Speed and Efficiency
AI enables the swift, efficient delivery of diagnostic results, accelerating the entire process and allowing healthcare professionals to work more efficiently.
Accessibility
The use of AI-powered diagnostic tools increases access to healthcare delivery. These tools enable medical professionals to attend to more patients efficiently and can be deployed globally, improving healthcare accessibility.
Challenges and Considerations
Data Privacy
AI algorithms rely on data collection and storage, raising concerns about data usage, privacy, and potential breaches. Ensuring that patients’ data is secure, safe, and stored in compliance with data protection regulations is a significant concern when patients submit their information.
Algorithm Bias
Despite their sophistication, AI systems are designed and directed by humans, meaning they can be subject to bias. To ensure all patients benefit equally, AI algorithms must be representative and free from manipulation. Addressing algorithmic bias is crucial for equitable healthcare delivery.
Integration Issues
AI is still in its early stages, particularly in the developing world. Integrating AI into the existing healthcare system is challenging and requires significant investment in technology, training, and workflow. The traditional diagnostic systems, with their inherent limitations, still dominate in many regions.
The Future of AI in Healthcare Diagnosis
With the digitalization of life and advancements in computer technology, the future of artificial intelligence is promising. We can expect an increased collaboration between AI professionals and the medical community, leading to innovations that improve lives and healthcare outcomes. Future developments will likely focus on creating more accurate algorithms that reduce errors and enhance diagnosis. Additionally, AI is expected to deliver more reliable, personalized diagnoses based on individual histories, genetic factors, and health elements. AI algorithms will also improve health monitoring and ensure early detection of conditions like cancer, diabetes, liver cirrhosis, and cardiovascular diseases. Overall, the quality of life is set to increase significantly.
Conclusion
Medical diagnosis is a critical step in healthcare, as it involves probing and investigating to identify health conditions accurately. Mistakes in diagnosis can lead to serious complications or even loss of life. This underscores the need for accurate, reliable, and scientific data in healthcare, which is why medical and laboratory tests are routinely performed. Traditional diagnostic methods, however, are prone to oversight, errors, and misinterpretation. Unlike these methods, AI bridges the gaps with its ability to diagnose, interpret, and even predict – almost entirely eliminating human error in healthcare delivery. AI algorithms can sift through, and analyze vast amounts of data, providing instantaneous, data-driven insights. This capability for accuracy, concise, and precise analysis positions AI as the future of healthcare delivery.
* This is part of a pre-internship blog series on the applications of artificial intelligence in healthcare for Skep Foundation. Wahab Abayomi Omiwole is a blogger, copywriter, and digital marketer dedicated to crafting content that converts. You can also access his portfolio here: abayomiomiwole.blogspot.com
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References
Tai, M. C. (2020). The impact of artificial intelligence on human society and bioethics. Tzu Chi Medical Journal, 32(4), 339 – 343. Retrieved from https://journals.lww.com/tcmj/fulltext/2020/32040/the_impact_of_artificial_intelligence_on_human.5.aspx
Kaul, V., Enslin, S., & Gross, S. A. (2020). History of artificial intelligence in medicine. GIE Journal, 92(4). Retrieved from https://www.giejournal.org/article/S0016-5107(20)34466-7/fulltext https://doi.org/10.1016/j.gie.2020.06.040
Kaul, V., Enslin, S., & Gross, S. A. (2020). History of artificial intelligence in medicine. GIE Journal, 92(4). Retrieved from https://www.giejournal.org/article/S0016-5107(20)34466-7/pdf
Pinto-Coelho, L., (2023). How artificial intelligence is shaping medical imaging technology: A survey of innovations and applications. Journal of Bioengineering, 18;10(12):1435. DOI: 10.3390/bioengineering10121435
HRSA Workforce (2024). State of the US Health Care Workforce, 2023. Retrieved from: https://bhw.hrsa.gov/sites/default/files/bureau-health-workforce/data-research/state-of-the-health-workforce-report-2023.pdf
WHO (2021). Health and care worker deaths during Covid-19. Retrieved from https://www.who.int/news/item/20-10-2021-health-and-care-worker-deaths-during-covid-19
Poalelungi, D.. G., Musat, C. L., Fulga, A., Neagu, M., Neagu, A. I., Piraianu, A. I., & Fulga, I. (2023). Advancing patient care: How artificial intelligence is transforming healthcare. Journal of Personalized Medicine, 13(8): 1214. doi: 10.3390/jpm13081214
Varnosfaderani, S. M., & Forouzanfar, M. (2024). The role of AI in hospitals and clinics: transforming healthcare in the 21st century. Journal of Bioengineering, 11(4): 337. doi: 10.3390/bioengineering11040337
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