The term “artificial intelligence” (AI) was first used in a conference proposal at Dartmouth College in 1955, and in the early 1970s, applications of AI were widely used in the field of health care.
AI could significantly alter the way that healthcare is delivered by offering fresh and creative approaches to patient assessment, care delivery, administrative procedure simplification, andcannd storage.
Artificial intelligence may one day be used to conduct medical evaluations that previously required human interaction. An artificial intelligence called machine learning (ML) uses a variety of algorithms as a means of programming or “learning.” What enables ML to carry out activities like those performed by humans is its capacity to adapt to experiences and inputs.
Although artificial intelligence (AI) has been widely adopted in the fields of finance and information technology, its uptake in the field of health care has lagged. Health care providers are more reluctant to adopt new technologies when human lives are at risk due to ethical and safety concerns.
Despite this slower integration, experts in the field believe that widespread use of AI will enhance patient care quality and outcomes by reducing human error. Additionally, physicians may be relieved of mundane and repetitive activities, giving them more time to concentrate on complex duties.
However, human variables, including trust, perceived utility, and privacy are crucial for determining whether and when new technologies, like AI in healthcare, are accepted and used.
However, the need for individualized medication, the demand for value-based care, the growth of digital patient health information datasets, and improvements in health care IT are all on the rise.
Patients and healthcare professionals are increasingly accepting AI thanks to the pervasiveness of smartphone apps, extensive internet connectivity, and a shortage of healthcare professionals.
According to projections, the market for artificial intelligence in healthcare would earn $6.9 billion in 2021 and $67.4 billion by 2027, growing at a compound annual growth rate of 46.2% between those years.
The growing concern over health care cost optimization and data management, the rise in public-private alliances, and the accelerated regional budget for the health care sector are the main factors influencing the demand for artificial intelligence in the health care sector.
Innovative AI prediction algorithms are currently being developed to help discover wound patterns that are not immediately apparent or observable to even the most experienced clinicians.
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How we manage wounds today?
More than one-third of patients may never experience a wound resolution, even with expert wound care in hospital-based outpatient wound centers. Effective wound management starts with a thorough patient history and physical (H&P).
The H&P gives the clinician the ability to spot any underlying disease states that are known to contribute to wound chronicity, including diabetes, autoimmune disorders, vascular impairment, inflammatory illnesses, anemia, kidney disease, and cancer. As part of this procedure, testing, lab work, and specialist referrals may be suggested.
Additionally, a thorough wound assessment is carried out to gather parameters specific to the wound to assist the development of a suitable evidence-based treatment plan that optimizes the wound environment to promote wound healing.
There is a published, validated predictive model of wound healing that considers clinical data such as patient age, wound grade, renal impairment, peripheral vascular disease, wound age, size, and overall number of active wounds present.
To assess the possibility for healing in patients with diabetic foot ulcers (DFUs), the Wound Healing Index (WHI) was developed to aid in clinical practice, research analysis, and clinical trials.
The index’s developers acknowledge that clinical data entered into electronic medical records can be incomplete, and that crucial data utilized to determine wound trajectories via the WHI may be absent or incorrect.
Additionally, it is difficult to find straightforward grading techniques to incorporate this data into therapeutic practice. As a result, this instrument has not been widely used by medical professionals. Many wound care practitioners are thinking back on the available tools and assessments and wondering if there is a better method.
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How AI Help Us to Manage Wounds Better?
It is possible to extract pertinent wound data from images using deep learning-based image analysis algorithms, including the location of interest, image cropping that only include the wound, and metrics for the wound periphery’s size evolution.
As was previously mentioned, tracking changes in wound size over time gives clinicians useful information about things like wound closure rate. In the past, clinicians who treat wounds have spent time gathering clinical biomarker and biologic data, such as manually measuring the wounds as previously mentioned, to spot disturbances in healing processes.
These analyses are advanced using ML wound image analysis. AI can discover trends more quickly and notify clinicians of anomalies by reviewing wound photographs and comparing them to other comparable instances in established databanks.
Clinicians are utilizing innovations like Microsoft’s Computer Vision Inner EYE program to diagnose and cure cancers. Patients with chronic wounds have a high rate of readmission and recidivism.
By using patient health data, machine learning (ML) may predict health outcomes and discover underlying illness trends. The ability of doctors to use technology to stop re-ulceration and readmission would result in cost savings, which are crucial in value-based networks.
Wearable technology with noninvasive biosensors that provide sophisticated health assessments and monitor numerous wound parameters offers further prospects. The likelihood of raising patient involvement and procedure adherence about wound care is present.
Furthermore, palliative care patients are frequently discovered too late in the patient’s course, which results in underutilization of resources and a lower quality of life.
Utilizing machine learning (ML) to identify potential non-healing and palliative care patients earlier will support appropriate care utilization, optimize resources (clinicians’ time, wound supplies), improve quality of life, and prevent infection/hospitalization.
Conclusion
It is envisaged that AI will be used in several applications that go beyond wound healing and into other diagnostic fields related to wound care. It will be a great move for wound care to shift from a reactive to a preventative strategy by recommending individualized treatment strategies.
The effectiveness of different AI systems needs to be analyzed in the next phase of research to establish their utility. The paradigm change from curative to preventive medicine depends on AI. By enabling self-care, self-monitoring, and self-diagnosis, AI has a significant revolutionary potential to advance sustainable health care in the wearable and smart clothing sector.
Some of the most alluring aspects of AI include its on-demand accessibility, capacity to boost productivity, and ability to lower the cost of providing healthcare services.
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