#10 AI Innovations Disrupting Healthcare

The healthcare industry is gearing up for the implementation of AI in 2020; revolutionizing the way in which they deliver care. The healthcare industry was gearing up for a decade-long wait, starting in 2018, to see real-life impacts of AI, however, the research has so rapidly advanced that the 10-year-long dream is materializing now!  

A panelist of 12 coined the “Disruptive Dozen”, at the World Medical Innovation Forum, deep-dive into which AI technologies would be ready to be utilized and completely alter clinical care for years to come.  

From radiology to disease and cancer, the opportunity to leverage AI to deliver more efficient and impactful care by bridging gaps in existing healthcare services is a reality we are getting closer to every day.  

Here are 10 exciting medical use cases with AI and ML:  

#1. Medical Imaging  

The scope for ML and AI in radiology is embedded within the radiology community’s adaptability.

1. The American College of Radiology gave its members a free AI platform to build, share and validate AI algorithms  
2. ML alongside imaging could clarify complex imaging  
3. Faster, more accurate detection 

#2. Bridging The Gaps In Mental Healthcare  

Connecting patients with services and providing access  

1. AI-driven apps that offer therapy  
2. Digital behavioral solutions  
3. Smartphone-based cognitive behavioral therapy 
4. Reduces monetary and geographical restrictions on access to clinical healthcare

#3. Identification of Violence And Those at Risk  

Identifying and flagging concerning injuries  

1. Strengthen the position of clinicians and social workers while addressing sensitive issues of underreporting domestic abuse  
2. AI detection of mismatched patient report history  
3. Detect concerning types of fractures  
4. Alerts the appropriate officials when called for  

#4. Real-time Monitoring of Brain Health  

Interpreting and predicting brain health with EEG  

1. AI is stepping up the process of predicting cognitive function and neurological diseases  
2. Detection of patterns that can alert patients to any brain irregularity that could be damaging  
3. AI can give more access and detailed reporting of one’s brain activity

#5. New Approach to Eye Health and Disease  

AI in healthcare is specifically picking up traction with image-centric verticals 

1. AI analyses patterns in image pixels – making ophthalmology an area that AI algorithms that are becoming much more advanced and accurate  
2. Creating tools to detect diseases sooner  
3. AI decision support for people with vision loss  

#6. Acute Stroke Care Revolutionized  

AI for healthcare has the potential to optimize stroke care and the way in which people have access to emergency response 

1. Response time improvement to increase chances of survival from life-long disabilities and death 
2. Automating detection and treatment for personalized stroke care 
3. AI algorithms to ensure there is appropriate care where on-site specialists are not  

#7. Malaria Detection To Be Automated  

Medical expert-level malaria parasite diagnostic 

1. AI will be able to quantify the level of malaria parasites in blood samples  
2. Device-agnostic software that can be used on a smartphone attached to a camera  
3. ML tool that detects malaria will be on par with pathology experts  

#8. At Risk to Suicide and Self-harm Prediction 

Providing support and therapy to individuals by identifying those at risk and connecting them to appropriate resources  

1. NLP to go through public posts and an e-health database to flag words or concepts related to self-harm 
2. AI methodologies can be used to support and therapize at-risk individuals  
3. AI for the healthcare of at-risk people can identify, assist and connect before it reaches a crisis

#9. The flow of Health Data through Information Exchange  

Creating a centralized data platform for information exchange will propel the medical healthcare industry further, faster 

1. Improve the healthcare system with a holistic repository of medical data to gain accurate insights into the gaps and strengths of the system 
2. Having open medical interfaces to merge the silos and gain access to more information  

#10. Augmenting Diagnostics and Decision Making  

Processing an abundance of data points for pathologists to make more accurate and faster decisions 

1. Automating data processes ensures pathologists don’t miss any vital information  
2. Getting the patient the care they need faster  
3. High-volume data funnels for pathologists to decrease the time it takes to come to a prognosis