Using Artificial Intelligence To Fix Healthcare
The healthcare industry should be using Artificial Intelligence (AI) to a far greater degree than at present, but progress has been painfully slow. The same factors that make the healthcare system so attractive to AI developers – fragmented ornon-existent data repositories,outdated computer systemsanddoctor shortages- are the same things that have stopped AI from providing the gains that should be created.
The healthcare sector also presents unique obstacles for AI: data must flow freely through AI systems to achieve real results, but extracting data from handwritten patient files or PDFs is cumbersome for us, and difficult for AI. Despite technical and operational challenges, new research suggests that thearrival of the tech giants into the industrymay provide the data and the capital required to digitize this fairly untapped market.
Where AI can help now
Severe fragmentation between different branches of healthcare, and life-threatening miscommunication within institutions (in 2016, ~10% of all US deaths were caused by medical errors), presents an opportunity for AI to ease the burden on doctors in more creative, less intrusive ways.Mabuis a humanoid robot developed byCatalia Healthand theAmerican Heart Associationthat helps patients keep on top of at home treatment for congestive heart failure. Acting as a personal health assistant, Mabu asks patients how they are feeling, makes activity suggestions and provides medication reminders. ‘There are key points we make sure Mabu covers,’ says Catalia Health founder Cory Kidd, ‘but the conversation is adaptive to what is going on with that patient at that moment,’ much like a home nurse’s visits might be scripted to a certain degree while relying on some human intuition.
Mabu is a promising step towards integrating AI into the healthcare system without disturbing doctors within facilities – the data Mabu gathers can be fed into Electronic Medical Records (EMRs) via email or text, and ‘daily conversations’ with the device mean that Catalia Health can collect patient information consensually ‘without depending on access to their medical data.’ The implementation of AI throughout healthcare institutions or an entire country will remain a huge task even for data-rich multi-nationals, but solutions like this may help to improve outpatient care andreduce readmission rates for long-term conditionswithout setting foot in a hospital.
The move towards integrating AI with hospitals and healthcare centers is gaining pace. The UK government hasannounced its intentionsto put the UK ‘at the forefront of the use of AI and data in early diagnosis, innovation, prevention and treatment’ by 2030. While this may be ambitious giventhe current status of technological advancementin the NHS, hospitals are working with a wide range of companies to tackle immediate problems on the ground.Chatbots,DeepMind, andvoice biometricsare all being used to alleviate unique problems in the sector, and some companies are taking a different approach to ensure that AI is used to its full extent.
Dr. Shafi Ahmedis a cancer specialist in practice for more than 20 years, and as such is in high demand from people who need his expertise. In a conversation with Steve Dann, who at the time was working in visual effects, Dr. Ahmed related that of the 300 students under his care he was only able to directly train two at a time because the operating theatre could not fit any more people. Dann suggested he filmed an operation using 360° cameras, which enabled Dr. Ahmed to show students exactly what to do without having 300 people breathing down his neck.
This led Steve Dann to foundMedical Realities, and use his skills in CGI and virtual reality to train medical students without making more work for doctors. Following the success of the 360° operation, Dann created a virtual Dr. Ahmed to answer questions for him, which is now used in conjunction with teleconferencing to help Dr.
