Terms such as "machine learning," "artificial intelligence," and "deep learning" have become buzzwords in science and technology in recent years. From finance to agriculture, sports to entertainment, and from aerospace to education, these terms have brought about profound changes in our lives and led to the creation of products and services that make our lives easier. How can these life-enhancing and productivity-boosting products and services bring innovations related to health, which is directly tied to a productive and peaceful life? In fact, the future developments in health sciences may depend on the integration of rapidly advancing computer technologies and methodologies into medical applications.
In this blog post, we will assess how machine learning, artificial intelligence, and deep learning will impact efforts to improve our health.
Current Challenges Threatening Public Health
Modern healthcare systems require new solutions due to increasing complexity, demand, and unsustainable costs. Unpredictable diseases like the coronavirus, adverse conditions caused by climate change, air pollution, and food shortages due to population growth, along with long-standing diseases such as cancer and heart disease, which continue to cause millions of deaths, and the need to improve healthcare workers' training and working conditions—all these factors are threatening the sustainability of our healthcare and caregiving systems. These challenges are becoming increasingly complex and require complex interventions.
Throughout history, humanity has developed methods and tools to help overcome adversities and has managed to solve many problems. Artificial intelligence, machine learning, and deep learning can use today's advanced information technology infrastructure to improve our health and even prevent fatal diseases. These technologies, which have already started to prove their effectiveness in preventing environmental and physical factors that cause diseases as well as in treating illnesses, offer a new approach to solving health problems by merging medicine and engineering.
The Importance of Health Data
When we talk about artificial intelligence, the most important topic that comes to mind should be data. Data is useless until it is transformed into valuable information. Data science is the field that applies advanced analytical techniques and scientific principles to extract valuable insights from data. The first step in solving healthcare-related problems is making complex health data meaningful to us. In the healthcare sector, engineers are developing wearable technologies and devices that collect vital data from patients, such as electrocardiograms (ECG), blood pressure, and blood oxygen saturation, while doctors analyze this data to make decisions about patient health. Healthcare organizations have accumulated vast datasets, including health records and images, population data, drug data, and clinical research data. The large amounts of data collected from medical devices and information technology systems over the years are waiting to be processed to develop new treatment and diagnostic methods. Artificial intelligence technologies are highly suitable for analyzing these data and uncovering patterns and insights that humans may not be able to identify on their own.
Artificial intelligence (AI) is a machine intelligence that serves human judgment, and advancements in AI bring scientific data and human judgment closer together. Healthcare services always involve the intersection of scientific data and human judgment. In healthcare, artificial intelligence has become a very important topic because it has been proven to improve diagnosis, treatment, care, and costs. With artificial intelligence and deep learning, healthcare organizations can make better business and clinical decisions and use algorithms to improve the quality of experiences offered to their patients.
AI and related technologies have demonstrated their benefits in various fields, and they have also proven their value in the healthcare sector. Today, artificial intelligence has emerged as a game-changer in the healthcare industry through various applications. Some of these applications can be categorized as follows:
- Medical Imaging
- Patient Risk Identification
- Primary Care / Triage (Patient Prioritization)
- Drug Discovery
Clinical Decision Support Systems
Across the healthcare sector, artificial intelligence is changing the way clinical providers make decisions. Clinical decision support systems play a crucial role in diagnosis, treatment planning, and population health management. Considering the large amounts of data—from genomics, biomarkers, and phenotypic data to health records and distribution systems—the ability to process and analyze these data has become increasingly important. As mentioned, technology is already being used to support decision-making in data-intensive specialties like radiology. In the future, it may even be possible to autonomously perform specific tasks using this technology. The key to the safe and effective integration of AI into healthcare services is rigorous and continuous evaluation. AI-assisted decision-making is already being practically applied in hospitals and doctors' offices worldwide, and systems continue to evolve.
Clinical decision support software also has the potential to reduce administrative burdens on clinicians. Natural language processing technology can help translate clinical notes in electronic health records. AI-supported software, including smart phones and connected medical devices, can provide access to medical images, electronic health data, and even patient activities from multiple sources. This allows clinicians to propose a wider range of diagnostic and treatment options, thus personalizing health outcomes.
The increasing number and intensity of data from multiple sources has made it more difficult to analyze. In fact, this challenge has pushed us to conduct predictive analytics with the help of AI. AI allows clinicians to discover patterns from multiple data sources that will enable them to make better decisions. For example, AI-supported systems can assist nurses in determining the appropriate number of days a patient should stay in the hospital, which can help prevent complications, improve patient satisfaction, and reduce costly readmissions by improving care planning and management.
Cost Management
Cost management in healthcare involves more than just reducing costs. Healthcare organizations must balance their spending while ensuring that their primary focus—patient satisfaction—is not compromised. In other words, organizations must reduce costs carefully and strategically while avoiding negative impacts on their ability to provide excellent patient experiences. It is also important to remember that the healthcare sector encompasses more than just hospitals that directly serve patients; pharmaceutical companies, pharmacies, and government agencies regulating healthcare are also part of this sector. Since cost is connected to everything, improvements made in areas such as decision support systems, drug discovery, and patient risk identification will also impact cost management.
Administrative tasks within healthcare organizations make up a significant portion of healthcare costs. By leveraging AI to automate tasks such as insurance pre-authorization, tracking unpaid bills, and record-keeping, healthcare professionals can focus on more complex tasks and save money in the process.
Home Care and Remote Treatment
The growing elderly population occupies a significant portion of healthcare service resources. Elderly care, long-term care, and chronic disease management create daunting and costly healthcare demands.
AI-supported home care systems can help alleviate the staffing crisis in home health services. For example, AI-based conversational devices that can engage in personal conversations with individuals can have a positive impact on the quality of life, independence, and mental health of elderly people living alone.
The need for people to stay at home and receive healthcare during situations like a pandemic has made the concept of home care and remote treatment particularly important. AI can contribute to home care and remote treatment in the following ways:
- Virtual Nurse Assistants:
AI-supported virtual nurse assistants available 24/7 can ask patients if they feel well, whether they have taken their medications, and other important questions. If patients need assistance, they can be directed to their doctors or other resources, services, or relatices without human intervention. Virtual nurse assistants help pateints manage their conditions at home, improving communication between patients, their families, and healthcare organizations to ensure continued treatment in the comfort of their own homes.
AI-supported chatbots are particularly suitale for addressing social isolation in home care environments. These bots, utilizing voice assistants, smart speakers, or advanced natural language processing algorithms, can engage in natural conversation. Digital companions provide companionship and emotional support while monitoring patients’ well-being and emotional state by noting if they are getting enough sleep, experiencing pain, or feeling depressed.
- Remote Patient Monitoring:
AI-supported wearable devices allow patients to receive real-time reminders and interventions to prevent health problems and detect issues before they worsen. These devices continuously capture and analyze patient measurements, enabling clinicians to spot changes in activity and behavior patterns that cloud indicate issues with heart failure, diabetes, chronic obstructive pulmonary disease, or even COVID-19.
- Training of Healthcare Personnel
Healthcare professionals trained with AI-supported systems can benefit from simulations supported by advanced algorithms. In the near future, AI, combined with technologies like augmented reality or virtual reality, will play a significant role in the training of healthcare workers. By creating digital twins of any laboratory or hospital environment, healthcare staff can train in virtual settings that closely resemble real-world scenarios, allowing them to experience rare diseases or surgical procedures in a simulated environment. AI-supported systems that infer behaviors can provide real-time feedback to prepare staff for real-life situations.
In conclusion, artifical intelligence is already being used to detect certain diseases more accurately and at earlier stages. Wearable health technologies and other AI-supported medical devices can be used to monitor early-stage diseases and assist doctors in detecting illnesses at more treatable stages. Data from diagnostic patient records, such as ECG, EEG, or X-ray images, can be analyzed with machine learning, allowing diseases to be detected at very early stages based on subtle changes in the data, and areas of concern can be highlighted for doctors. With this capability, AI-based systems can be used for early diagnosis of many diseases in the near future.
AI-supported technologies can also help clinicians take a more comprehensive approach to disease management, better coordinate care plans, and improve long-term treatment programs and patient adherence.
Robots have been used in medicine for over 30 years, ranging from basic laboratory robots to complex surgical robots that assist surgeons. Robots are used in hospitals and laboratories for repetitive tasks, rehabilitation, physical threapy, and surgical procedures. Advanced AI-supported robots will also be used in more complex surgeries.
The topics discussed here aim to provide a general overview of AI-based healthcare solutions. The possibilities of what can be done with these technologies are entirely up to the imagination of the developers. What we have mentioned is only the tip of the iceberg. Thanks to the developing information technology systems, what we can do has become almost limitless.
As Livewell, we use these technologies to carry out important studies, especially in the prevention and treatment of heart diseases. By integrating the wearable technologies, we are working on with artificial intelligence-based systems, we are developing completely domestic technological solutions that will deeply affect the health services of the future.
Hakan Kahraman - Software Analyst
