*I frequently get asked questions about Data Science, so in the interest of helping as many people as possible, I’ve started this blog to answer those questions as simply as possible. This is a robust topic, and if you want a more in-depth discussion, please revisit my blog, where we will be going into greater depth at another time.
AI for healthcare can revitalize the medical field by tapping into data to increase the accuracy of diagnoses and treatment, improve healthcare research and offer personalized healthcare. AI has great potential in the healthcare field to improve patient outcomes and satisfaction all while reducing costs and improving the standard of care. Read on to learn how to leverage AI for healthcare.
AI for healthcare can completely reimagine the medical field, changing the way patients are diagnosed, treated, and monitored in a healthcare setting. AI technology also helps improve healthcare research, increases accuracy of diagnoses and treatments, and also allows for more personalization in healthcare.
Personalized healthcare involves using diagnostic tests to determine the treatments that will work best in an individual patient. By combining data from the tests with a patient’s medical history and other circumstances, physicians can create a targeted, specific plan with a stronger degree of accuracy — and AI helps make this possible.
The potential of AI in personalized healthcare is significant and is already being used in several cases with great success. Interested in learning how to leverage AI for personalized healthcare? This blog will tell you everything you need to know.
Table of Contents
Benefits of Personalized Healthcare
#1. Improved Accuracy in Diagnosis and Treatment
#2. Enhanced Patient Outcomes and Satisfaction
#3. Reduction in Healthcare Costs
Understanding AI in Healthcare
Leveraging AI for Personalized Healthcare
Challenges and Limitations of Using AI for Personalized Healthcare
#2. Ensuring Data Quality and Integrity
#3. Overcoming Barriers to Adoption
Future Opportunities in AI for Healthcare
#1. Advancements in AI Technology
#2. Disease Prevention and Early Detection
Benefits of Personalized Healthcare
With the help of AI technology modernizing and reshaping the medical field, advancements like personalized healthcare are becoming the norm across healthcare facilities around the world. People generally desire personalization wherever and whenever it’s available, but there are several standout benefits to personalized healthcare including:
- Improved Accuracy in Diagnosis and Treatment
- Enhanced Patient Outcomes and Satisfaction
- Reduction in Healthcare Costs
#1. Improved Accuracy in Diagnosis and Treatment
Personalized healthcare allows providers to detect diseases early on and then monitor them over time. This allows for a deeper understanding of the patient, their condition, and their needs, which enables providers to create precise, tailored treatment plans. This prevents the issue of misdiagnoses — which can be quite costly — and prevents wasted time and resources on incorrect treatments, thus making the process more accurate all around.
#2. Enhanced Patient Outcomes and Satisfaction
When care is high quality and personalized, patients will likely be more satisfied and see generally better outcomes than traditional forms of healthcare can offer. This is because you can offer individualized treatments and therapies that evolve along the patient’s recovery process. This increases the likelihood that your patients will comply with the treatment, which will yield better results.
Leaning into personalized healthcare in this way could also even reduce recovery time. Plus, when your patients are happy and responding well to treatment, it can improve the relationship between you as the provider and your patients.
#3. Reduction in Healthcare Costs
The shift to more personalized healthcare allows for providers to put more of a focus on preventative care rather than reactive treatment. This shift helps to reduce healthcare costs as preventative care and maintenance is often less expensive than traditional, reactive care. Think about how much it costs for an emergency room visit, both in terms of time and money.
Understanding AI in Healthcare
To understand AI for healthcare, you need to first understand what AI is. Artificial intelligence, or AI, is technology that simulates human intelligence by using machines, mainly computer systems. There are also two important subsets of AI you should know — machine learning and natural language processing.
Machine learning is the capability of a machine to imitate human behavior by using algorithms to analyze and detect patterns in data. Machine learning allows computers to learn and adapt without having to be given explicit instructions and is the technology behind things like the Netflix recommendation system.
Natural language processing is a branch of AI that gives computers the ability to understand text and spoken words, similar to the way humans can by combining machine learning, computational linguistics, and deep learning models. Together, this tech works to allow the machine to process human language and understand its full meaning.
AI can be used in a variety of ways across multiple medical disciplines. Some key applications of AI for healthcare include:
- Accurate and Early Diagnoses of Diseases like Cancer
- Customer Service Chatbots
- Virtual Health Assistants
- Targeted, Individual Treatment
- Medical Record Management
- Robot-Assisted Surgery
- Reduction in Dosage and Treatment Errors
- Medical Imaging
- Predictive Analytics
Along with the number of different use cases for AI in healthcare, there are also several different personalized healthcare approaches providers can take when treating patients. Two of which include:
- Precision Medicine
- Genomics
#1. Precision Medicine
Precision medicine is a form of personalized healthcare that takes the variability of a person’s genes, environment, and lifestyle into account when determining the best approach for disease treatment and prevention.
Taking these individual factors into consideration allows doctors to more accurately predict the treatment strategies that will have the biggest impact on a singular patient, or specific groups of people. Also often called personalized medicine, precision medicine can also be used to effectively manage diseases that have been resistant to treatments by taking a more targeted approach.
#2. Genomics
Genomics is the study of a person’s genes or genome, including how those genes interact with each other and the environment. The information gathered can then be used to inform a patient’s medical care, including diagnostic and therapeutic decision-making. It’s an emerging practice that has already made an impact in the oncology, pharmacology, rare and undiagnosed disease and infectious disease fields.
Leveraging AI for Personalized Healthcare
In general, AI is a very powerful tool, but it has the potential to completely revolutionize the healthcare industry, and in a lot of ways, it already has.
For example, AI can be harnessed to analyze vast amounts of healthcare data quicker and more efficiently than humans can. AI can be used to automate time-consuming healthcare tasks like managing claims and transferring medical data, allowing more time for important tasks like research or caring for patients.
Within the healthcare field, you can also use AI-powered algorithms to inform diagnoses and treatment recommendations. AI technology can analyze medical images like X-rays and ultrasounds and help providers identify and diagnose diseases with more accuracy. This is especially useful when it comes to cancer diagnoses and even the early detection of diseases and other ailments. From there, AI can help suggest the best possible treatments based on data gathered about the patient, their condition and individual needs.
AI can also be integrated with wearable devices and telemedicine for remote monitoring and personalized care. Wearable technology, like Apple Watches and Fitbits, collect medical data which AI algorithms can analyze and interpret in real time. This allows healthcare providers to monitor patients remotely and provide more proactive care by keeping an eye on the patient’s vitals and symptoms.
Several companies and medical centers are already using AI for healthcare, reaping the benefits of implementing this technology into their practices and offering more personalized healthcare. Some examples of successful AI for healthcare applications include:
- ai
- PathAI
- Buoy Health
#1. Viz.ai
When it comes to health emergencies, a delay could be the difference between life and death and the Viz.ai platform strives to address this by using AI to help care teams react to emergencies and patient needs quickly.
The Viz.ai platform uses AI to alert providers of potential disease by scanning the hospital’s network of patients. Viz.ai then sends notifications to providers, allowing them to reach out to patients sooner than later to start providing treatment before the condition worsens. Currently, more than 1,300 hospitals are using Viz.ai.
#2. PathAI
PathAI uses machine learning and AI technology to assist in making more accurate diagnoses. Currently, the company’s primary goal is to reduce the rate of error in cancer diagnoses and to help providers develop methods to create more personalized medical treatment plans.
To improve patient outcomes, PathAI leverages millions of data points from a pathology network of 450 pathologists, as well as a library of archived data, to train its AI models to be as accurate as possible. Not only does this practice help improve the accuracy of diagnoses and ensure patients get the care they need quickly, but it can also accelerate drug development.
#3. Buoy Health
Bouy Health is an AI-powered symptom checker that can be used to diagnose and treat illnesses. It’s an AI chatbot patients can talk with to help them make sense of their symptoms and then directs them on the best way to care for themselves, whether they need to seek professional medical care or if it’s best to treat themselves at home.
Bouy Health helps to improve patient outcomes by eliminating barriers to care people may face, making healthcare information more accessible.
Challenges and Limitations of Using AI for Personalized Healthcare
While the benefits of using AI for healthcare are significant, there are certain challenges and limitations to be mindful of. Some of which include:
- Ethical Considerations
- Ensuring Data Quality and Integrity
- Overcoming Barriers to Adoption
#1. Ethical Considerations
Anytime you work with data, there are ethical considerations to keep in mind as it pertains to data privacy and security. To accurately use AI for healthcare, you’ll need a large amount of data, and when this is the case, it may raise concerns about privacy, security, and whether the data is being gathered and used ethically. To prevent any issues, ensure patient data is protected from leaks or unauthorized access and allow patients to have a say in how their data is used.
#2. Ensuring Data Quality and Integrity
No AI system is completely perfect, meaning you may face issues in data quality, like missing variables or even bias. AI models can be biased if the data they’re trained on doesn’t fully represent the entirety of the population they’re being used to serve. This could impact the data’s integrity and lead to inaccurate results.
#3. Overcoming Barriers to Adoption
As with anything new, or any big change, some people may be slow or even opposed to adopt the new practice of using AI for healthcare. For many healthcare providers and patients, AI may be a brand-new concept, and they might not understand how it works and what it can and cannot do. This could set you up for unrealistic expectations, mistrust of the technology and a general distaste for implementing the tech.
Future Opportunities in AI for Healthcare
AI for healthcare is already making great strides within the industry, reimaging how providers give care and how patients receive it. But in a lot of ways, this is only the beginning. AI is continuously growing and evolving, taking on new use cases and constantly improving upon itself.
AI will continue to advance over time, offering a bright future full of opportunities for those in the healthcare industry looking to enhance their practices. Opportunities to look forward to include:
- Advancements in AI Technology
- Disease Prevention and Early Detection
- Increased Collaboration
#1. Advancements in AI Technology
As AI technology advances, its capabilities will advance, too. That means that in the future, AI for healthcare could be capable of achieving things that seem like a fantasy in comparison to today’s tech.
For example, in the future, AI could be used to create robotic nurses and even robotic surgeons. Researchers have found that robots can coordinate their actions during high-stakes events — like surgeries — in a similar fashion to humans. Implementing robotic surgeons could lead to improvements in safety during surgery as robots don’t tire or get distracted like humans do.
#2. Disease Prevention and Early Detection
AI for healthcare is already skilled at providing accurate diagnoses and offering early detection of some diseases, but as the tech advances, it’ll become more skilled at predictive care. In the future, doctors will be able to identify and predict which patients may be at high risk for specific conditions, as well as how likely they are to respond to certain treatments.
More advanced AI models could also be used to monitor various factors in a person’s lifestyle, like their diet, medical history, and other key demographics to make predictions on how those environmental factors could impact their future health. This allows providers to construct healthcare plans to prevent disease before it’s too late.
#3. Increased Collaboration
AI for healthcare will likely lead to an increase in collaborative efforts between healthcare providers, researchers, and AI developers. Increased collaboration is a key way to ensure that developments in AI for healthcare are applicable to the field and meet providers’ needs.
Collaboration between developers and providers ensures that the tech in development will be able to easily integrate into the medical workflow with minimal time, effort, and disruption. This can lead to providers having a better understanding of the AI technology, which can lead them to be more trusting of the new tech and more willing to integrate it into their daily practices.
Conclusion
AI for healthcare can completely reimagine the medical field, changing the way patients are diagnosed, treated, and monitored in a healthcare setting. AI can be used to offer personalized healthcare, increasing the accuracy of diagnoses and targeted treatment amongst individuals.
With the help of AI technology, personalized healthcare is becoming more accessible in healthcare facilities around the world. An increase in personalized healthcare with the help of AI can lead to better patient outcomes and satisfaction, as well as reduced costs for both patients and providers.
Several doctors and healthcare facilities are already utilizing AI for healthcare to revitalize the standard of care they offer patients. And while AI for healthcare is already in practice, it’s still in its early days. AI technology will continue advancing and improving, presenting a future of opportunities for growth and development for those who choose to embrace AI for healthcare.
About the Author
Tiffany Perkins-Munn orchestrates aggressive strategies to identify objectives, expose patterns, and implement game-changing solutions with an agility that transcends traditional marketing. As the Head of Data and Analytics for the innovative CDAO organization at J.P. Morgan Chase, her knack involves unraveling complex business problems through operational enhancements, augmented financials, and intuitive recruiting. After over two decades in the industry, she consistently forges robust relationships across the corporate spectrum, becoming one of the Top 10 Finalists in the Merrill Lynch Global Markets Innovation Program.
Dr. Perkins-Munn earned her Ph.D. in Social-Personality Psychology with an interdisciplinary focus on Advanced Quantitative Methods. Her insights are the subject of countless lectures on psychology, statistics, and real-world applications. As a published author, coursework developer, and Dissertation Committee Chair, Tiffany still finds time for family and hobbies. Her non-linear career path has given her an exclusive skill set that is virtually impossible to reproduce in another individual.
