5 Future Applications of AI in Healthcare and Medical Fields
Technology in Healthcare has endless opportunities to deploy more precise, efficient and impactful interventions at critical points during a patient's care. AI offers a number of advantages over traditional analytics and clinical decisions in medical care. These advantages come mainly from self-learning algorithms that become more precise and accurate as they interact with practical training data, allowing humans to gain unprecedented insights into diagnostics care processes, treatment variability and patient outcomes.
Healthcare and Life Sciences companies are already employing AI to enhance their services. AI is usually used for diagnosis and treatment recommendations, patient engagement and adherence or administration activities. It has been stated that AI can perform even better than humans and can prevent large-scale automation of Healthcare.
5 Potential Uses of AI in HealthCare
1 - Mind-to-Machine connections with Brain-Computer Interfaces (BCI)
There has been a ton of research regarding creating interfaces between computers and the human brain, without the need for keyboards, mice and monitors. This might sound like something out of a science-fiction movie, but there are already several physical demonstrations of this technology.
Neurological diseases and trauma sometimes damage the nervous system, taking away the ability to speak, move or even interact meaningfully with other people and environments. This research brings hope to people suffering from such conditions, offering the possibility to restore neurological functions aided by Brain-Computer Interfaces.
Furthermore, by integrating AI with BCI solutions, patients may even be able to communicate and interact like a healthy person. BCIs can also improve the quality of life for ALS patients and people suffering from strokes, spinal cord injuries and other immobilizing conditions.
2 - Medical Diagnosis and Treatment
Immunotherapy is one of the most promising treatments for cancer, which works by using the body’s own immune system to fight back the malignancies, which indirectly beats the stubborn tumors. Research has shown that not all patients respond to immunotherapy. The only way to know if it works on a patient is by applying it and observing the results. In cases where the patient does not respond to this treatment, it results in a waste of time and money, limiting their options further.
With AI and Machine Learning algorithms, it is possible to synthesize highly complex databases that will illuminate new options for targeting therapies to individuals based on their unique genetic makeup. By using this technology, patients will have insights into how their body might respond to various treatments and what treatment holds the best possible chances for recovery - before actually undergoing the treatment.
Although rule-based systems for diagnosis are widely used, including at the NHS in the UK, they lack the precision of advanced algorithmic systems based on Machine Learning. Most of these rule-based clinical decision support systems are always difficult to maintain as medical knowledge depends on thousands of variables if not more. Some Healthcare Startups are now developing AI-backed systems that identify the patient most at risk as well as those most likely to respond to treatment protocols. Each of these could provide decision support to clinicians seeking to find the best diagnosis and treatment of patients and eventually save lives.
3 - Using Voice to replace Electronic Health Records
EHRs have been crucial in the healthcare journey towards digitalization, but it was obviously backed by problems associated with cognitive overload, endless documentation and user burnout.
EHR developers are now moving towards AI to create more intuitive interfaces and automate some of the routine processes that consume so much of users' time. Studies say that clinicians spend a majority of their time in the clinical documentation process, order entry and sorting through the in-basket. With the help of voice recognition and dictation it has improved the clinical documentation process.
AI and Machine Learning can record clinical sessions and can then be used for future information retrieval.
Just like the voice commands on Siri and Alexa, we can easily incorporate clinical voice assistants to use this embedded intelligence for order entry, prescriptions and other key functions. The AI can help in other routine activities like to process the request from the inbox medications refills and result notifications. It may also help to prioritize the tasks that truly require the clinicians attention making it easier for them to devote even more of their attention to patients.
4 - Creating more precise analytics for pathology images
70% of all the decisions in healthcare are based on pathology, and 75% of all the data in Electronic Health Records is from pathology. It is therefore important that the data we are provided is accurate in order to get the right diagnosis. That’s what digital pathology and AI have the opportunity to deliver.
Analytics that can drill down to the pixel level on extremely large digital images identify nuances that may escape the human eye. This development or implementation will take us to the stage where we can do a better job of assessing whether a cancer is going to progress rapidly or slowly and how that might change. The patients will be treated based on algorithms rather than clinical staging. AI will enable the next generations of radiology tools that are accurate and detailed enough to replace the need for tissue samples in some cases, experts predict.
AI can also improve productivity by identifying features of interest in slides before a human clinician reviews the data. The AI screens through slides and directs the clinician to the important areas of interest to quickly assess what’s important and what’s not. This will directly increase the efficiency of pathologists and the number of patients that can receive their consultation.
5 - Monitoring Health with Wearables
AI needs a ton of data. We are already surrounded by devices with sensors that track and store health parameters. From smartphones with step trackers to wearables that can track heartbeats around the clock and sleep monitors, we are keeping tabs on a lot of our health functions.
Therefore collecting and analyzing the data gathered by clinical tests as well as Apps and sensor devices can offer a unique perspective into individual and population health.
AI will play an important role in extracting actionable insights from this large and varied treasure of data and finding patterns or anomalies. A challenge here could be getting patients comfortable to share this possibly intimate information as continuous monitoring will only add up the burdens. Naturally, we all trust our physicians more than the big companies like Facebook, when it comes to data sharing.
The wearables data will have a major impact because by collecting granular data over a continuous period, there’s a great likelihood that the data will help clinicians take better care of patients.
Developing AI and using it for clinical decision support, risk scoring and early alerting is one of the most promising areas of development for this revolutionary approach to data analysis. By the employment of a new generation of tools and systems that make clinicians more aware of nuances, more efficient when delivery care, and more likely to get ahead of developing problems, AI will usher in a new era of clinical quality and exciting breakthroughs in patient care.
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