Machine learning, a subset of AI, has evolved diagnosis and treatment relatively quickly. It’s the best tool to use when trying to find patterns in sets of data, which the healthcare field is known to have. Machine learning algorithms can process massive amounts of clinical data to make predictions regarding medical outcomes. Data science services help providers improve treatments and reduce costs.
Here are some specific applications for diagnosis and treatment.
The amount of medical imaging that needs review to determine a diagnosis grows larger every day, putting a significant strain on clinicians and causing delays. It’s been a long-standing concern that AI has been part of for decades. The first application of AI in diagnostic imaging dates back to the 1980s. It has advanced substantially since, but the development of algorithms follows the same path as it always has—deep learning.
Machine learning algorithms “learn” from large data sets of what to look for on a CT scan or MRI. As they learn, they become better at it, often as accurate or more than humans.
Healthcare is using this type of analysis for things like:
Greater adoption of machine learning in machine imaging is growing, but it’s a formal process where accuracy is everything. For example, the U.S. Department of Food and Drug Administration will only approve it if it’s right 80-90% of the time. Another example is a study of AI models that learned from MRI-scanned images of brain tumours. The technology classified them correctly 98.56% of the time.
The benefits of applying AI in medical imaging are many. It accelerates review and diagnosis, reduces costs, and finds things the human eye may miss (especially if those eyes are overworked).
Precision medicine has, for the most part, been limited. The costs related to it weren’t accessible to most, but AI ushers in a phase of democratised precision medicine. Every person has a unique genetic makeup, conditions, and medical history. All these things impact the treatment of a disease and its effectiveness.
AI-enabled precision medicine can eliminate the assumptions for physicians. The treatment plan is now at the individual level rather than what works for most people.
Using AI to sequence DNA, for example, has been substantial for leukaemia treatment. It can define the specific variant, which is critical in recommending treatment. These blood cancers are common and have high rates of death. Researchers in the space found AI to be a key factor in the ability to deliver precision medicine for leukaemia patients.
Precision medicine will significantly impact the survivability of cancer or other diseases. It guides physicians to create treatments that have a better chance of working. Overall, this reduces the costs of healthcare delivery as well.
Outreach to patients is often a time-intensive, manual process. Health plans and providers struggle to reach people and support them with their health problems. A lack of participation in one’s own health has adverse effects, such as medication nonadherence, not managing chronic diseases, and unhealthy lifestyles. When people finally realise they need care, they end up in emergency departments, typically the most expensive setting.
This disengagement costs the healthcare ecosystem incredibly, and developing engagement strategies has been challenging. Conversational AI may be the answer. Some companies are using conversational AI to communicate with individuals about health concerns. It’s an easy, convenient way for this interaction.
Taking this concept further can improve outcomes for patients as it could play a role in reminding patients to take their medications, acting as a follow-up after discharge with the care plan, and ensuring patients make their next appointments.
The AI in healthcare conversation also includes automation capabilities. Using healthcare software like EHRs (electronic health records) can be made easier with the help of automation.
By applying intelligent automation to these systems, you can:
Applications like EHR software, mHealth apps and telemedicine solutions benefit all healthcare stakeholders and enable providers to perform more meaningful work with patients. Reduced costs, consolidated data, and more seamless processes also lead to operational efficiency.
Another promising application of AI in healthcare is using it to collect and analyse data from diverse sources to empower public health predictions. This involves a lot of cooperation in sharing data, which isn’t always possible. The key is real-world data, and when data scientists have it, the benefits include:
The next horizon for AI will likely point in this direction, taking into account the lessons learned and the reality of the pandemic.
The healthcare industry relies on technology, and AI is driving it toward the future. Every area of healthcare has much to gain from leveraging AI and machine learning. Explore how our expert team can help.
The breadth of knowledge and understanding that ELEKS has within its walls allows us to leverage that expertise to make superior deliverables for our customers. When you work with ELEKS, you are working with the top 1% of the aptitude and engineering excellence of the whole country.
Right from the start, we really liked ELEKS’ commitment and engagement. They came to us with their best people to try to understand our context, our business idea, and developed the first prototype with us. They were very professional and very customer oriented. I think, without ELEKS it probably would not have been possible to have such a successful product in such a short period of time.
ELEKS has been involved in the development of a number of our consumer-facing websites and mobile applications that allow our customers to easily track their shipments, get the information they need as well as stay in touch with us. We’ve appreciated the level of ELEKS’ expertise, responsiveness and attention to details.