Imagine AI robots assisting in precise surgeries and AI chatbots offering 24/7 mental health support. A glimpse into the future of healthcare, powered by AI
Artificial intelligence in medicine (AIM) has undergone significant evolution in the last five decades. With the introduction of machine learning (ML) and deep learning (DL), AIM applications have diversified, opening avenues for personalised medicine instead of relying solely on algorithm-based approaches. The integration of predictive models enables disease diagnosis, forecasts therapeutic responses, and holds promise for future applications in preventive medicine.
AI was initially introduced in 1950, but early models faced limitations that hindered their broad acceptance and application in medicine. The early 2000s saw significant advancements with the emergence of deep learning, addressing many of these limitations.
Now, with AI systems proficient in analysing intricate algorithms and self-learning, the medical field enters a new era. AI can now be utilised in clinical practice through risk assessment models, enhancing diagnostic precision, and streamlining workflow efficiency.
AI & DL
AI originated with basic “if, then rules” and has evolved over decades to incorporate intricate algorithms, mirroring the capabilities of the human brain. Similar to medical specialties, AI encompasses various subfields like machine learning (ML), deep learning (DL), and computer vision.
Machine learning (ML) involves leveraging distinct features to detect patterns applicable for analysing a specific situation. Subsequently, the machine can “learn” from this information and apply it to future similar scenarios. This predictive tool has the potential to be dynamically applied in clinical decision-making, allowing for personalised patient care rather than adhering to a static algorithm.
Artificial Intelligence in Medicine
As per the IBM reports, with recent strides in computer science and informatics, artificial intelligence (AI) is rapidly becoming an integral component of contemporary healthcare. AI algorithms and other applications driven by AI are actively aiding medical professionals in clinical settings and ongoing research.
Presently, the predominant roles for AI in medical environments include clinical decision support and imaging analysis. Clinical decision support tools assist healthcare providers in making informed decisions regarding treatments, medications, mental health, and other patient requirements by swiftly providing access to pertinent information or research. In the realm of medical imaging, AI tools are deployed to scrutinise CT scans, x-rays, MRIs, and other images, detecting lesions or other findings that a human radiologist might overlook.
AIM during COVID 19
The challenges posed by the COVID-19 pandemic prompted healthcare systems worldwide to initiate field tests of new AI-supported technologies. These include algorithms designed for patient monitoring and AI-powered tools aimed at screening COVID-19 patients, as reported by IBM.
Ongoing research is still collecting data and results from these tests, contributing to the ongoing definition of standards for AI use in medicine. Despite the evolving landscape, opportunities for AI to provide benefits to clinicians, researchers, and patients are progressively expanding. It is increasingly evident that AI will become a fundamental component of the digital health systems that underpin and enhance modern medicine.
Applications of AI in medicine
AI in Disease Detection and Diagnosis
Artificial intelligence plays a crucial role in the realm of disease detection and diagnosis. Unlike humans, AI operates tirelessly, utilizing machine learning models to continuously monitor the vital signs of patients in critical care. It can promptly alert healthcare providers to escalating risk factors, going beyond basic monitoring provided by medical devices like heart monitors. An exemplar is an IBM client who achieved 75% accuracy in detecting severe sepsis among premature babies using a predictive AI model.
Personalised Disease Treatment
The integration of AI holds promise for advancing personalized disease treatment, particularly in the domain of precision medicine. AI models, capable of learning and retaining preferences, have the potential to deliver tailored, real-time recommendations to patients around the clock. This eliminates the need for repetitive information sharing, offering healthcare systems the opportunity to provide continuous access to an AI-powered virtual assistant. Such an assistant can respond to queries based on a patient’s medical history, preferences, and individual needs.
AI in Medical Imaging
AI is assuming a prominent role in revolutionizing medical imaging. Research indicates that AI, driven by artificial neural networks, can match the effectiveness of human radiologists in detecting signs of breast cancer and other conditions. Beyond aiding in early disease detection, AI simplifies the management of the copious medical images handled by clinicians. By identifying critical elements in a patient’s history, AI presents relevant images, making the entire process more manageable.
Clinical Trial Efficiency
Clinical trials often involve a considerable amount of time spent assigning medical codes to patient outcomes and updating datasets. AI steps in to expedite this process by providing faster and more intelligent searches for medical codes. Notably, two IBM Watson Health clients reported a reduction of over 70% in medical code searches with the implementation of AI.
Accelerated Drug Development
AI stands as a transformative force in the prolonged and costly process of drug development. It contributes by creating superior drug designs and identifying promising new drug combinations. Through AI, many of the significant challenges posed by big data in the life sciences industry can be overcome, potentially streamlining drug development processes.
