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Bhavya Priyadarshini
Apr 214 min read

AI and Robots in Medicine: Revolutionising Healthcare

Writer: Bhavya Priyadarshini          

Editor: Isabello Io


AI is becoming increasingly capable of performing human-like tasks more efficiently, swiftly, and affordably. The potential for AI and robots in healthcare is enormous. AI and robots are becoming increasingly integrated into our healthcare ecosystem, just as they are in our daily lives.

I've outlined eight ways in which this change is now unfolding below:



Keeping Well

One of the most significant potential benefits of AI is that it can help people stay healthy so they don't need to see a doctor as often. The integration of AI and the Internet of Medical Things (IoMT) in consumer health applications is already benefiting consumers.

The Internet of Medical Things (IoMT) is a network of internet-connected medical devices, hardware, and software, enabling secure communication and rapid data analysis. Its impact on the healthcare market is significant, with analysts predicting it will reach $861.3 billion by 2030. Due to data sensitivity, IoMT requires a robust security infrastructure.

Technology programmes and apps promote better behaviour in individuals and aid in the proactive maintenance of a healthy lifestyle. It gives people control over their health and well-being.

Furthermore, AI improves healthcare providers' capacity to better comprehend the daily routines and requirements of those they care for, allowing them to give greater feedback, advice, and support for remaining healthy.


Early Detection

AI is already being used to diagnose illnesses, such as cancer, more precisely and early on. According to the American Cancer Society, a substantial percentage of mammograms produce misleading findings, with one in every two healthy women being informed they had cancer. For instance, women with dense breasts are more likely to get false-negative results. AI detects, evaluates and translates mammograms with 99% accuracy and is 30 times faster than humans, eliminating the need for confirmation using biopsies, which are invasive procedures requiring anaesthesia.

The explosion of consumer wearables and other medical devices, along with AI, is also being used to manage early-stage heart disease, allowing physicians and other carers to better monitor and diagnose potentially life-threatening events at earlier, more curable stages.


Diagnosis

IBM's Watson for Health enables healthcare organisations to use cognitive technology to access massive volumes of health data and improve diagnoses. Watson can review and retain significantly more medical information than any person, including every medical article, symptom, and case study of therapy and reaction from all across the world.

Merative L.P., formerly IBM Watson Health, is a medical technology company that uses AI, data analytics, and cloud computing to facilitate medical research and healthcare services.

Google's DeepMind Health is collaborating with physicians, academics, and patients to address real-world healthcare issues. The technique blends machine learning and systems neuroscience to create strong general-purpose learning algorithms in neural networks that resemble the human brain.


Decision-Making

Improving treatment necessitates the synchronisation of large health data with appropriate and timely judgements, and predictive analytics may help assist clinical decision-making and actions while also prioritising administrative duties.

Another area in which AI is gaining traction in healthcare is the use of pattern recognition to identify people who are at risk of getting an illness or seeing it deteriorate due to lifestyle, environmental, genetic, or other variables.


Treatment

Aside from scanning health records to help providers identify chronically ill individuals who may be at risk of an adverse episode, AI can assist clinicians in taking a more comprehensive approach to disease management, better coordinating care plans, and assisting patients in better managing and complying with their long-term treatment programmes.

Robots have been utilised in medicine for over three decades. They range from modest laboratory robots to very complicated surgical robots that may either assist a human surgeon or do procedures independently. In addition to surgery, they are utilised in hospitals and labs for repetitive work, rehabilitation, physical therapy, and to help people with long-term diseases. 


End-of-Life Care

We are living far longer than past generations, and when we near the end of life, we are dying in a different and slower manner, from ailments such as dementia, heart failure, and osteoporosis. It is also a period of life marked by loneliness.

Robots have the potential to transform end-of-life care by allowing individuals to live independently for extended periods of time, decreasing the need for hospitalisation and care facilities. AI, paired with developments in humanoid design, is allowing robots to go even farther and engage in 'conversations' and other social engagements with people, helping to keep ageing minds sharp.


Research

The journey from research lab to patient is long and costly. According to the California Biomedical Research Association, a medicine takes an average of 12 years to get from the research lab to the patient. Only five out of every 5,000 medications that begin preclinical research progress to human testing, and only one of these five is ever authorised for human use. Furthermore, on average, a corporation will spend $359 million to bring a new treatment from the research lab to the patient.

Drug discovery is one of AI's more recent uses in healthcare. By directing the newest breakthroughs in AI to streamline the drug discovery and repurposing processes, new products' time to market and prices can be drastically reduced.


Training

AI enables individuals in training to experience lifelike simulations in ways that basic computer-driven algorithms cannot. An AI computer's capacity to draw instantaneously on a massive library of cases means that a trainee's reaction to questions, decisions, or suggestions can challenge in ways that humans cannot. Furthermore, the training system may learn from prior student replies, allowing the challenges to be changed on a continuous basis to fit their learning needs.

And training may take place anywhere; with the power of AI incorporated in a smartphone, fast catch-up sessions following a difficult case in a clinic or while travelling will be available.


Sources

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