Artificial Intelligence (AI) has played a robust and growing role in the world in the past few decades. What most do not realize is that AI presents itself in many forms that impact daily life. One central area AI is proliferating is in healthcare, specifically in diagnostics and treatment management. As much as there is a fear of AI surpassing human tasks and ability, significant research shows how AI can aid in clinical decisions, support human judgment and increase treatment efficiency.
An Increased AI Presence in Healthcare
There are various magnitudes of AI in healthcare. AI often utilizes a web database, allowing doctors and practitioners to access thousands of diagnostic resources. As doctors are deeply educated in their field and current with the latest research, AI greatly increases faster outcomes that can be matched with their clinical knowledge.
Conversely, AI presents many fears of eventually replacing or reducing the need for human physicians, especially in clinical settings. However, recent research and data have shown that it is more likely that this tool will benefit and enhance clinical diagnostics and decision-making rather than reduce the need for clinicians.
Often, a patient can present multiple symptoms that correlate with various conditions by genetic and physical characteristics, which can delay a diagnosis. So, AI benefits a practitioner in terms of efficiency and provides quantitative and qualitative data based on input feedback, improving accuracy in early detection, diagnosis, treatment plan, and outcome prediction.
The ability for AI to “learn” from the data provides the opportunity for improved accuracy based on feedback responses. This feedback includes many back-end database sources and input from practitioners, doctors, and research institutions. The AI systems in healthcare are always working in real-time, which means the data is constantly updating, thus increasing accuracy and relevance.
Assembled data is a compilation of different medical notes, electronic recordings from medical devices, laboratory images, physical examinations, and various demographics. With this compilation of endlessly updated information, practitioners have almost unlimited resources to improve their treatment capabilities.
Implications of AI for the Healthcare Workforce
AI is set to have a major impact on the healthcare workforce. As AI-powered applications become more sophisticated, they will be increasingly important in patient care. This will result in a shift in the way healthcare is delivered, with more emphasis on preventive care and early intervention. This shift will require a different skillset from the healthcare workforce. AI will require healthcare professionals to understand data and analytics better. They will also need to be comfortable working with AI-powered applications.
The impact of AI on the healthcare workforce will be wide-ranging. It is crucial to start preparing now for the changes that are coming. Healthcare organizations should consider how AI can improve patient care and make the healthcare system more efficient. They should also think about how they can retrain their workforce to meet future demands.
The Future of AI in Healthcare
The future of AI in healthcare is exciting. As AI-powered applications become more sophisticated, they will introduce various changes in how healthcare is delivered. There will be a shift from reactive to proactive care, emphasizing preventive care and early intervention.
AI will also change the way healthcare practitioners interact with patients. Instead of one-size-fits-all care, AI will allow them to deliver personalized care tailored to each patient. This will result in better health outcomes and a more efficient healthcare system.
Healthcare providers are just beginning to scratch the surface of what AI can do. As more sophisticated AI-powered applications are developed, even more transformative changes in healthcare will surface. AI’s potential is limitless.
AI provides more targeted diagnostics
With various amounts of healthcare data in the field, AI must efficiently sort through the presented data to “learn” and build a network. Within healthcare data, two types of data can be sorted: unstructured and structured. Structured learning includes three techniques: Machine Learning (ML), a Neural Network System, and Modern Deep Learning. Whereas all unstructured data uses Natural Language Processing (NLP).
Machine Learning techniques (ML)
Machine Learning techniques use analytical algorithms to pull out specific patient traits, including all the information collected in a patient visit with a practitioner. These traits, such as physical exam results, medications, symptoms, basic metrics, disease-specific data, diagnostic imaging, gene expressions, and different laboratory testing, all contribute to the collected structured data.
Through machine learning, patient outcomes can then be determined. In one study, Neural Networking was utilized in a breast cancer diagnostic process sorting from 6,567 genes and paired with texture information input from the subjects’ mammograms. This combination of logged genetic and physical characteristics allowed for a more specific tumor indicator outcome.
Neural Networks & Modern Deep Learning
Supervised learning is the most common type of Machine Learning in a clinical setting. Supervised learning uses the physical traits of the patient, backed with a database of information (in this case, breast cancer genes), to provide a more targeted outcome. Another type of learning used is Modern Deep Learning, considered to go beyond the surface of Machine Learning.
Deep Learning takes the same inputs as Machine Learning but feeds them into a computerized neural network, a hidden layer that further files the information to a more simplified output. This helps practitioners with multiple possible diagnoses to narrow down to one or two outcomes, thus, allowing the practitioner to make a more definitive and concrete conclusion.
Natural Language Processing (NLP)
Natural Language Processing is similar to structured data processes, which focus on all unstructured data in a clinical setting. This data is from clinical notes and documented speech-to-text processing when a practitioner sees a patient. This data includes narratives from physical examinations, laboratory reports, and exam summaries.
Natural Language Processing uses historical databases with disease-relevant keywords aiding in a diagnosis's decision-making process. Using these processes can provide a more accurate and efficient diagnosis for a patient, which in turn saves time for the practitioner, and, more importantly, can speed up the treatment process. The faster, more targeted, and more specific the diagnosis, the sooner a patient can be on the road to recovery.
AI can be integrated into major disease areas
With cardiovascular, neurological disorders, and cancer consistently being the top causes of death, it is imperative that as many resources as possible are being utilized to aid in early detection, diagnosis, and treatment. The implementation of AI benefits early detection by pinpointing any risk alerts a patient may have.
Let’s look at some examples of AI applications in major disease areas:
Early stroke detection
One study involving patients at risk for stroke used AI algorithms based on their presented symptoms and genetic history to place them in an early detection stage. This stage was movement based, where any abnormal physical activity in the patient was documented and would trigger an alert. This trigger alert allowed practitioners to get patients to an MRI/CT scan sooner for a disease evaluation.
The study's early detection alert provided 87.6% accuracy in a diagnosis and prognosis evaluation. That said, the practitioners could implement treatment sooner and predict whether the patient had a higher possibility of future stroke. Likewise, machine learning was used in 48-hour post-stroke patients gaining a perdition accuracy of 70% on whether the patient may have another stroke.
Forecasting kidney disease
Department of Veterans Affairs and DeepMind Health had a major breakthrough in 2019 when they created an AI tool that can predict acute kidney injury up to 48 hours earlier than traditional methods.
Acute kidney disease causes patients to deteriorate very fast and become life-threatening, and it is known to be challenging to detect by clinicians. This new era of acute kidney disease prediction and detection allows healthcare practitioners to detect the risk of renal disease well before it occurs.
Cancer research and treatment
AI has also largely contributed to cancer research and treatment, particularly in radiation therapy. The lack of a digital database in radiation therapy has made research and treatment of cancer challenging.
Therefore, Oncora medical built a platform to assist clinicians in making informed decisions regarding radiation therapy for cancer patients. The platform collects the patient’s medical data, evaluates care quality, optimizes treatments, and provides information on treatment outcomes, data, and imaging.
CloudMedX, a healthcare technology company, developed an AI solution that turns electronic health records into a smart predictive tool, helping clinicians make more accurate decisions. The tool can help doctors catch and treat illnesses before they become life-threatening by analyzing a patient’s medical history and connecting symptoms with chronic diseases or conditions in the patient’s family.
AI can be used in patient engagement and adherence applications
AI is playing an increasingly important role in patient engagement and adherence. Being the last-mile problem in healthcare, it is widely accepted that the more patients participate in their well-being, the better their health outcomes. By providing personalized recommendations and reminders, AI-enabled applications can help patients stay on track with their treatment plans and improve health outcomes.
In addition to increasing patient engagement, AI can also help to identify potential non-adherence issues early on. By analyzing a patient's behavior, AI-powered applications can provide insights that allow care teams to intervene before non-adherence becomes a bigger problem. Using AI to improve patient engagement and adherence, healthcare providers can improve health outcomes and make the healthcare system more efficient.
Barriers to Adoption
Despite the undisputed potential of AI in healthcare, its adoption has been sluggish. Brookings Institute, in a study, identified four barriers to AI adoption in healthcare: data access limitations, algorithmic limitations, misalignment of incentives, and regulatory barriers.
Data access limitations
One of the most significant barriers to AI adoption in healthcare is the lack of data. For AI-powered applications to be effective, they need access to large amounts of data. Unfortunately, many healthcare organizations do not have the necessary data sets. To overcome this barrier, healthcare organizations must start investing in data collection and management.
Algorithms are only as good as the data they are trained on. Some complex algorithms make it challenging for healthcare practitioners to understand how AI reaches specific recommendations.
This can have severe implications in healthcare, where AI is used to make patient care decisions. Trust in the technology is essential, especially considering that doctors and hospitals are held accountable for their decisions based on the AI tool they use.
Misalignment of incentives
The level of AI adoption varies among health systems, depending on the hospital's leadership and each decision-maker's intent. Some physician-led hospitals may be afraid to adopt AI for fear it will replace them, while other administration-led hospitals are more open to its use in non-clinical roles.
Healthcare is a highly regulated industry, but there are no clear guidelines on how AI can be used, and this has led to much uncertainty. Healthcare organizations are also reluctant to share data with AI-powered applications for fear of breaching patient confidentiality. This is a valid concern, but it should not be used as an excuse to prevent AI from being deployed in healthcare.
These barriers can be overcome but require a concerted effort from all stakeholders. Healthcare regulators must develop clear guidelines on how AI can be used in healthcare, while healthcare organizations must address their data privacy and security concerns.
Telehealth: Artificial Intelligence on a Smaller Scale
Although AI is used for high-risk diseases and on a larger scale, telehealth tools are being implemented into patients' homes to help treat and prevent high-risk situations while reducing hospital readmissions.
Telehealth tools allow different metrics to be taken, documented, and processed, much like a more expansive AI machine. This equipment can notify practitioners immediately when a patient reports a high-risk variable.
Early detection, faster diagnostics, and an updated treatment plan reduce time and money for the patient and hospital while getting more immediate care. AI allows practitioners to make more efficient and logical decisions, bettering the care for patients as a whole; in the end, this is the ultimate goal.