The healthcare industry is facing tremendous pressure to innovate and provide better services at a lower cost. In this challenging environment, many healthcare organizations are exploring ways to leverage artificial intelligence (AI) to improve patient care and operational efficiency. AI technology has the potential to drive transformative change in almost every segment of the healthcare industry.
Improving employee wellness, addressing patient check-in wait times, identifying high risk patients, monitoring disease progression and many other processes can all be made more efficient through the use of artificial intelligence. Let’s take a look at how AI is improving healthcare and which areas stand to benefit most from its implementation.
1. AI for Early Diagnosis:
AI is already being used to detect diseases, such as cancer, more accurately and in their early stages. According to the American Cancer Society, a high proportion of mammograms yield false results, leading to 1 in 2 healthy women being told they have cancer. The use of AI is enabling review and translation of mammograms 30 times faster with 99% accuracy, reducing the need for unnecessary biopsies[1].
The proliferation of consumer wearables and other medical devices combined with AI is also being applied to oversee early-stage heart disease, enabling doctors and other caregivers to better monitor and detect potentially life-threatening episodes at earlier, more treatable stages.
2. Improved Decision Making:
Improving care requires the alignment of big health data with appropriate and timely decisions, and predictive analytics can support clinical decision-making and actions as well as prioritise administrative tasks.
Using pattern recognition to identify patients at risk of developing a condition – or seeing it deteriorate due to lifestyle, environmental, genomic, or other factors – is another area where AI is beginning to take hold in healthcare.
3. AI for quick Treatment:
Beyond scanning health records to help providers identify chronically ill individuals who may be at risk of an adverse episode, AI can help clinicians take a more comprehensive approach for disease management, better coordinate care plans and help patients to better manage and comply with their long-term treatment programmes.
Robots have been used in medicine for more than 30 years. They range from simple laboratory robots to highly complex surgical robots that can either aid a human surgeon or execute operations by themselves. In addition to surgery, they’re used in hospitals and labs for repetitive tasks, in rehabilitation, physical therapy and in support of those with long-term conditions.
4. Life Care Protection:
We are living much longer than previous generations, and as we approach the end of life, we are dying in a different and slower way, from conditions like dementia, heart failure and osteoporosis. It is also a phase of life that is often plagued by loneliness.
Robots have the potential to revolutionise end of life care, helping people to remain independent for longer, reducing the need for hospitalisation and care homes. AI combined with the advancements in humanoid design are enabling robots to go even further and have ‘conversations’ and other social interactions with people to keep aging minds sharp.
5. AI for Training:
AI allows those in training to go through naturalistic simulations in a way that simple computer-driven algorithms cannot. The advent of natural speech and the ability of an AI computer to draw instantly on a large database of scenarios, means the response to questions, decisions or advice from a trainee can challenge in a way that a human cannot. And the training programme can learn from previous responses from the trainee, meaning that the challenges can be continually adjusted to meet their learning needs.
And training can be done anywhere; with the power of AI embedded on a smartphone, quick catch up sessions, after a tricky case in a clinic or while travelling, will be possible.
Conclusion:
AI adoption in healthcare continues to have challenges, such as lack of trust in the results delivered by an ML system and the need to meet specific requirements. However, the use of AI in health has already brought multiple benefits to healthcare stakeholders.
By improving workflows and operations, assisting medical and nonmedical staff with repetitive tasks, supporting users in finding faster answers to inquiries, and developing innovative treatments and therapies, patients, payers, researchers and clinicians can all benefit from the use of AI in healthcare.
