Artificial intelligence (AI) has triggered a transformation in healthcare in four broad aspects: for patients, healthcare centers, drug discovery and operational ease. Tasks influenced by AI range from simple to complex — from replying to a patient’s query to reading radiology images. Innovations fostered by AI will increase efficiency in healthcare, bring in more effective drugs, and reduce costs and errors in healthcare technology.
The growing data sets of patient-related digital information show a high demand for AI algorithms in the healthcare industry. In 2022, the value of global artificial intelligence in the healthcare market in the United States was pegged at $15.4 billion. From 2023 to 2030, the market is predicted to grow at a compound annual growth rate of 37.5%.
This article discusses how AI improves diagnostics, makes surgeries safer, leads to better education and training, enhances patient monitoring, bolsters tracking and incident management, and promotes drug discovery.
Better medical diagnostics
The first step in medical treatment is to analyze symptoms, medical history and test results to determine the cause of the problem. The process often involves various diagnostic tests, such as blood tests, imaging tests or scans, biopsy procedures and others. AI algorithms can help in medical diagnostics by augmenting the accuracy of disease prediction along with the speed and efficiency of the process.
Processing of data at accelerated speed
Analyzing a massive volume of patient data, which can reach yottabytes (1024 gigabytes) in the United States alone, poses significant challenges in terms of time and effort for any medical system.
To diagnose, a medical practitioner might be required to check vital signs (blood pressure, body temperature, respiration rate, pulse rate), 2D/3D imaging, bio-signals (ECG, EMG, EEG, EHR), medical history and demographic information.
Thanks to image recognition capabilities combined with deep neural networks, AI can accurately process this data and make decisions, leading to precision medicine and treatment optimization.
Reduction in error rate
Large caseloads make the diagnosis of medical documents error-prone. AI algorithms fed with robust data can predict and diagnose diseases faster than human clinical professionals with minimal chances of errors, provided the quality of the data feed is high. At a time when the healthcare system is stretched thin and medical professionals are facing stress, this could be a game changer.
According to a May 2020 report published in the European Journal of Cancer, deep learning outperformed 11 pathologists in breast cancer diagnosis. AI has helped pathologists to reduce their error rate in identifying cancer-positive lymph nodes from 3.4% to 0.5%.
Assistance during surgeries
AI can be used for preparation, guidance and robotic processes in surgeries. Predictive analytics foretells associated risks, reducing the possibility of failure. Prior to surgery, AI can enable a high level of visualization for the surgeon. For example, before going for an orthopedic surgery, the surgeon could watch visualizations of bone segmentation and comprehend angles.
AI-based tools are utilized as intraoperative guidance during minimally invasive surgery to minimize patient trauma. These advanced tools provide real-time assistance and support to surgeons, aiding in precise surgical procedures and reducing the invasiveness of the operation.
Currently, AI in intraoperative guidance has four applications:
- Shape instantiation: real-time 3D reconstruction of the surgical zone.
- Endoscopic navigation: enhancing navigation techniques to move toward a target location.
- Tissue tracking: differentiating organs from the background.
- Augmented reality: bolstering surgeons’ intraoperative vision to access the desired area.
Assisted by AI-powered robots, surgeons can access various intervention frameworks, greatly improving their performance. Robots provide surgeons with better dexterity to operate in small body spaces, as the tiny machines can work accurately around sensitive organs and tissues, decreasing blood loss and the risk of infection. This reduces post-surgery pain, makes surgery less scary and reduces recovery time.
In 2017, the Holland-based Maastricht University Medical Center used an AI-assisted robot to stitch small blood vessels, some as small as .03 millimeters. A surgeon operated the robot, which converted the surgeon’s hand movements into more precise actions and performed the surgery accurately.
Thanks to AI, modern surgery hasn’t just become quicker, but autonomous and less risky. As the databases grow, early detection and diagnosis will be more precise. Already, nanorobots are emerging as a potent tool for diagnosis. AI technology may also be used for efficient remote surgery.
AI brings the ability to analyze colossal amounts of data, enabling the monitoring of individuals in an unprecedented manner. It supports the tracking of patient movements and even the analysis of their biometric data, including facial expressions.
In any medical facility, evaluating vast data logs is challenging. It becomes particularly daunting if the analysis needs to be done manually from a multitude of feeds such as Allscripts, Epic, Cerner, Lawson, etc. The organization ends up spending plenty of additional time and resources on monitoring.
AI-backed audit tools help analyze unstructured raw data and identify patterns. The medical facility gets a comprehensive story of patient activities and care flow. Based on proactive monitoring, documentation and cataloging of work are done.
Tracking and incident management
Medical facilities need to comply with regulatory requirements such as The Health Insurance Portability and Accountability Act and the General Data Protection Regulation. Ensuring data traceability and auditability becomes a complex job. AI simplifies the process, improving the transparency of the reporting procedures.
An AI-based system needs to be integrated with the legacy software of the hospital to facilitate simplified, on-the-spot incident logging and sophisticated analysis of root causes. Consistent follow-up on corrective actions helps avoid future incidents.
The automated system notifies all relevant parties and refers to policy documents pertaining to the investigation. It requests notes or comments from the human resources involved, tracks progress, and marks status.
AI-induced ability to analyze extensive information enables researchers to discover how the new molecules work with deadly diseases. These systems can calculate the 3D shape of a protein from amino acid sequences. AI facilitates high-fidelity molecular simulations on computers, saving the prohibitive costs of conventional chemistry methods.
Prediction of drug properties, thanks to AI, helps bypass simulated testing of drug candidates. Key properties of drug molecules include toxicity, bioactivity and physicochemical characteristics. When promising drug compounds are identified, researchers can use AI to rank these molecules for further assessment.
AI is capable of taking drug discovery beyond theoretical drug design. It helps generate synthesis pathways for creating hypothetical drug compounds. AI-led modifications to compounds make manufacturing easier.
Can AI perform a c-section?
Currently, AI cannot perform a cesarean section (c-section) on its own. To successfully deliver a baby, a complicated surgical procedure called a c-section requires incisions in the abdomen and uterus. To protect the health of both the mother and the unborn child, obstetricians and gynecologists require the knowledge, skills and judgment of other qualified healthcare experts.
However, AI has a supportive role to play in aiding medical staff during c-section operations. By evaluating medical images and offering suggestions on the optimal incision site, spotting potential issues, or assisting in visualizing anatomical components, AI-based imaging systems, for instance, can support preoperative planning. During surgery, AI can also potentially be used in real time to support image-guided navigation or issue risk warnings.
It’s vital to remember that while AI can be a great help, it cannot replace educated medical professionals’ technical expertise and ability to make crucial decisions. When performing c-section surgery, ensuring both the mother and baby are safe still requires human judgment and knowledge.
The next generation of radiology tools
MRI machines, CT scanners, and x-rays acquire radiological images of organs, providing healthcare practitioners with non-invasive visibility into the internal functioning of the body. However, in many cases, diagnostic processes still use physical tissue samples that are not just intrusive but might also infect the body part on which the biopsy is conducted.
AI can power the next generation of radiology equipment that is more accurate and has broader applications. Such tools are expected to replace tissue samples to a large extent. This development will bring the diagnostic imaging team closer to the surgeon, the interventional radiologist or the pathologist, aligning their goals.
Education and training
AI can enhance the quality of medical training, particularly surgery, resulting in better clinical results. Mixed reality (MR) headgear, such as Microsoft’s HoloLens, can help medical students comprehend human anatomy with realistic images and holograms.
MR is an emerging technology that combines virtual reality and augmented reality. Surgeons can use MR devices to record surgeries without hampering the process and, later, play the videos to explain complex surgical steps to students.
AI-based medical education eliminates the need for expensive in-person training, facilitating better content distribution and absorption by students. Moreover, the system can be quickly deployed and set up for various types of surgical procedures. Such a training system is simple to implement for administrators and helps learners get easy access to training content.
The transformation of healthcare is underway
AI has revolutionized the way medical care is approached, making a tangible difference. During the COVID-19 pandemic, deep learning techniques combined with AI tools enabled medical practitioners to analyze the segmentation of 3D images from CT scans to detect lung lesions.
Rapidly advancing AI tools help doctors detect diseases earlier, increasing the chances of patients getting completely cured, tracking the progress of infectious diseases in real time, and helping patients get immediate care with chat boxes, which reduce operational costs for medical care centers.
Considering the exponential pace at which AI is advancing, it could completely transform how healthcare is done today. The journey is still in its initial phase, and the coming years could be exciting.