Artificial intelligence and robotics that are currently being used in Healthcare

Basics of Artificial intelligence

Artificial intelligence (AI) is becoming more and more part of our everyday lives. The fast expansion of the field has been created through the innovation in microchips. In recent decades microchips, have become smaller in size and larger in processing power. A machine is said to have artificial intelligence if it can interpret data, potentially learn from the data, and use that knowledge to adapt and achieve specific goals.

AI technology is part of self-driving cars, Google search algorithms, and Google maps. Artificial intelligence has infiltrated every industry and has helped to automate some of the processes within them. Artificial intelligence has become a very disruptive technology in healthcare. Although AI technology is not widespread in healthcare, it has great possibilities to improve our current healthcare system. Artificial intelligence has helped help healthcare professionals' jobs become easier.

Accurately reading medical imaging scans and aiding professionals with finding diagnoses

Machines need a lot of data and power to learn as a human does. It is a very complicated process to get a machine to recognise images like they do in the Google Nest cameras. These home cameras can detect to a high likelihood whether there is a threat, pet, or delivery person at your front door. This involves very complicated AI deep learning technology and a vast amount of data to program a machine to be able to detect these things. AI professionals have been experimenting and refining image recognition in the healthcare field also.

There is good evidence that machines can be more accurate at detecting diseases compared to health care professionals in medical imaging (MRI, X-rays, CT, ultrasound etc.). There have been several studies that have compared human healthcare experts and artificially intelligent machines in their accuracy in diagnosing patients. It is found that in some instances machines can score higher in detecting disease and healthy images of patients. "AI and healthcare professionals had similar rates of identifying healthy medical images, at 93% and 91% accuracy, respectively. AI correctly detected the disease in 87% of cases, compared to clinicians' 86% accuracy rate" (Pifer, 2019). Over time these deep learning machine models will become more accurate and can help lower the workload on health care professionals.

Doctors, nurses, and other professionals in the industry feel like they are being overworked. This will continue implications on their lives, our communities, and our healthcare service. The spread of this type of AI will inevitably benefit these medical staff members by having AI to help identify diseases.

Machines with AI have shown a high amount of accuracy when detecting eye diseases, cardiovascular disease, breast cancer, and kidney disease.

Optimising current medical services

AI can be helpful in being able to shorten waiting times and response times for GPs and other healthcare professionals. AI can be able to ask patients their symptoms prior to their visit which can help to streamline their experience of the healthcare service remotely. New Apple and Samsung wearable technology have gained a large amount of popularity in recent times. Companies are excited to bring together this new data, with existing healthcare records that have spanned over decades. This has the capability of making AI better at predicting future health problems that a person could face. This idea seems attainable when combining this information with patients' genetic details also. Healthcare with the support of AI can offer patients more pre-emptive and robust solutions in the future.

Wearable technologies offer new avenues for AI professionals to monitor our health.

AI can also support mental health professions in their respective fields. The World Health Organization has deemed the problem of depression as a global health crisis. The UK has found some solutions involving AI that could benefit the whole world if adopted. Depression and anxiety are complicated diseases, with every person's symptoms being unique. In the UK, the NHS branch "Talking Therapies" uses weekly questionnaires with their patients. This questionnaire allows for their anxiety and depression to be visualised. This allows the practice of therapy to be more effective, while also giving health care professionals a visualisation of their patients' symptoms. This allows the counsellor to see if their clients are improving over time.

Depression and anxiety patients get a questionnaire to help measure for these symptoms each week.

Speech recognition has also improved dramatically in recent years and is used in combination with these statistics to help mental health care professionals further. Speech recognition AI can listen to what the therapist/ counsellor is saying to the patient and then categorises their speech. These speech categories include active listening, review of home practice, teaching new techniques, etc. The AI can notice patterns in the counsellor's sessions with a client and can give recommendations to the counsellor. For example, data gathered from this AI could recommend talking to the patient more about new techniques and keeping more on track with the sessions. In summary, AI will help make these treatments for these patients become more quantifiable and more evidence-based therefore improving the recovery rates for anxiety and depression.

Robot-Assisted Surgery

Robot-assisted machines have been used since 2001. Machines like the "Da Vinci" model has been used to assist surgeons with very intricate and delicate operations since then. The Da Vinci has cameras, tubing, robotic arms that the surgeon can control using a separate console. This machine is used more for keyhole surgery and procedures involving the prostate, bladder, and gynaecological areas. The robot arms have flexible joints similar to a human arm, which are controlled by a surgeon sitting at a computer console using two joysticks and a 3D monitor screen. The use of this machine has been expanding for different procedures and its benefits are being seen around the world. Over 4,500 surgeries have been successfully completed (BBC, 2018). The results show that the patient recovers faster and with fewer complications using this machine in surgery.