In the rapidly evolving health care landscape, the integration of artificial intelligence (AI) has emerged as a groundbreaking force, reshaping the way medical professionals care for patients. Although the use of this advanced technology is evolving, there are several cases where it has propelled the field to increased precision, efficiency, and personalized care.
The impact of AI in healthcare, while still only just beginning, is broad – from aiding in diagnostics to powering innovative treatments. Keep reading to discover a handful of ways the use of AI in medicine is catalyzing change throughout the field.
10 ways AI is revolutionizing medical care
When incorporating AI into various functions of medical care, the goal is not to construct autonomous agents that can replace our healthcare practitioners. Rather, the aim is to implement tools that help doctors improve their processes, become more efficient in their work, and provide more personal, individual care.
In the midst of the nation’s widespread physician shortage, the ability to expedite processes and even delegate tasks to AI-powered tools and platforms has started to allow medical practitioners to cover more ground in day-to-day work.
The following innovations showcase how AI is transforming health care. Using AI in medicine is showing it can make it more efficient, accurate, and often, more personalized.
1. Diagnostic assistance
AI systems have been used to analyze medical images, such as those collected via X-rays or MRI scans. These tools help in diagnosing conditions like cancer, retinal diseases, pneumonia, and more.
You can also find examples of AI diagnostic assistance in cardiology, where deep learning algorithms are used to diagnose heart attacks in much the same way that cardiologists would. In other cases, AI networks have been trained with clinical images to provide assistance with dermatological diagnoses, accurately classifying skin lesions.
Studies have demonstrated AI’s ability to meet or exceed the performance of human experts in its diagnostic abilities, excelling in both accuracy and speed.
2. Drug discovery
The process of developing a drug from concept all the way to its eventual implementation in clinical settings is a very long, complex, and expensive journey. And the COVID-19 pandemic has greatly emphasized the need for an expedited drug discovery process.
To help speed up development and prevent failures in the drug discovery pipeline, AI is being used to analyze medical data. By using AI, researches can use this tool to analyze vast amounts of biological and chemical data to identify potential drug candidates. It accelerates the process of finding new treatments, reducing the time and cost involved in this important research.
3. Virtual health assistants
Over the past decade, Health care organizations have begun implementing the use of virtual health care assistants and chatbots to improve the quality of patient care and enhance engagement. These digital health care capabilities are now leveraging AI and machine learning to improve health management tools that help patients gain access to valuable resources and maintain their overall wellness.
Chatbots and virtual health assistants powered by AI work to provide preliminary medical advice, answer patient queries, schedule appointments, and offer basic healthcare guidance. These digital health care assistants can also be used to handle routine and repetitive tasks like scheduling appointments, sending out reminders, and managing emails.
4. Personalized medicine
AI can be used to analyze genetic, clinical, and lifestyle data to create personalized treatment plans for patients. It also helps in predicting how patients might respond to different medications or therapies, optimizing treatment outcomes.
By helping physicians create customized treatment plans to reflect an individual’s unique medical and lifestyle profile, AI-powered personalized medicine paves the way for more effective treatment of common conditions, such as heart disease and cancer — or even rarer diseases like cystic fibrosis. This approach can lead to earlier diagnosis, prevention, and better treatment overall.
5. Robot-assisted surgery
Another emerging use of AI in medical care is its ability to help enhance surgical decision-making before, after, and even during surgical procedures. This aspect of AI is accomplished by integrating information from a range of different data sources, such as surgical guidelines and research insights.
In some cases, surgical robots equipped with AI capabilities can even assist surgeons in performing actual procedures with greater precision. The minimally invasive options enabled by robotic-assisted surgery offer many benefits to patients, including shorter hospital stays, earlier recovery, and less pain overall.
6. Medical education and training
Medical schools have begun integrating different AI tools into their Doctor of Medicine (MD) programs. AI can be used in medicial education at three levels:
- Learner-oriented AI encompasses tools students use to receive and understand new information.
- Instructor-oriented AI enables instructors to reduce their workload, gain insights about students, and incorporate new innovations into their classrooms.
- Institution-oriented AI helps inform decisions related to managing and administrating schools and programs.
In practice, incorporating AI tools in medical education includes the use of AI-driven platforms that can adapt to individual learning styles, pace, and needs. Students can also experience AI-powered simulations and virtual reality environments, which enable them to practice surgical procedures, diagnose illnesses, or engage in clinical scenarios in a safe and controlled setting.
AI can also contribute to curriculum enhancement, helping medical educators in designing dynamic, up-to-date curricula by identifying areas for improvement, integrating new findings, and adjusting teaching methodologies based on data-driven insights.
7. Clinical trial optimization
There are several ways AI helps streamline the clinical trials process that is important for medical research at large. AI algorithms analyze vast amounts of patient data to identify suitable candidates for clinical trials based on specific criteria. This expedites the recruitment process, reducing time and costs.
AI models are also used to predict patient responses to treatments or interventions, helping to design more targeted trials, optimizing protocols, and improving the chances of success. The use of AI enables adaptive trial designs by continuously analyzing incoming data. This flexibility allows for real-time adjustments, enhancing strategies based on interim results.
There are also major benefits to using AI systems for risk prediction and safety monitoring in clinical trials. These tools enable a proactive approach within the clinical trials process, identifying potential adverse events or risks early on, ensuring participant well-being and compliance with safety standards.
8. Natural Language Processing (NLP) for health care records
AI-driven NLP systems have been found to have a transformative impact on how we utilize health care records. By analyzing and extracting valuable information from unstructured medical records, these systems can greatly improve the efficiency of coding, billing, and data management.
AI-NLP systems help convert textual data into structured, usable information for analysis. They also help automate billing and coding processes, improving accuracy, reducing errors, and expediting administrative tasks. AI can even aid in clinical decision-making by providing practitioners with critical data points, flagging potential issues, and suggesting possible treatment options.
9. Genomics
The use of AI has revolutionized genomics by enhancing the analysis, interpretation, and application of biological data. AI algorithms help in sequencing genomes faster and more accurately than human experts, identifying patterns, mutations, and variations in DNA sequences — work that assists clinicians in understanding genetic diseases and traits.
Machine learning algorithms can predict disease risks based on an individual’s genetic makeup, while also predicting how a person might respond to certain drugs or therapies. This newfound ability to tailor treatment plans based on a patient’s genetic predisposition is transformative in the field of genomics.
10. Mental health support
AI-powered mental health platforms and chatbots can offer support and guidance to individuals who are experiencing mental health challenges. These tools use NLP to interact with users, offering resources, coping strategies, and sometimes even therapeutic conversations. AI tools have even been designed to analyze speech patterns or text to detect emotional cues, providing early intervention or suggesting professional help when needed.
The use of AI in mental health support helps improve access to care, aids in early detection, and paves the way for personalized treatment.
Keep up with what’s next in medicine
The fusion of AI into healthcare is creating a shift in healthcare with the potential to enhance outcomes, reduce disparities in treatment, and develop more efficient, patient-centered care.
Earn your MD and you too can be on the cutting edge of new lifesaving technologies. If you’re looking to make a positive impact in the healthcare field, you can start your journey at St. George’s University.
