In the field of medicine, both AI and robotics have a lot of abilities. AI and robots are increasingly a portion of our healthcare ecosystem, just as they are in our day-to-day lives.
What does AI in healthcare mean? Where are we lying in terms of executing AI in the current healthcare system and where will AI take the future of the existing medical industry?
Table of Content
- How does AI complement Healthcare support?
- Use cases of AI in the medical industry
- Future of AI in healthcare
All the conventional medical approaches like Traditional analytics, or clinical decision-making mechanisms, have a variety of detriments when compared to AI. With a lot of data pumping in AI is on the optimal flank as learning algorithms interact with this data regularly, they can evolve more comprehensive and true, giving people unparalleled insights into care procedures, treatment variability, patient outcomes and diagnosis etc.
How does AI complement Healthcare support?
Because of its substantial role in a productive and healthy society, healthcare is one of the most integral areas. The use of AI in healthcare can mean the antagonism between life and death. Doctors, nurses, and other healthcare personnel can boost from artificial intelligence in their regular jobs.
AI in healthcare has the potential to enhance patient health outcomes by enhancing preventative care and quality of life, as well as enabling more precise diagnosis and treatment. AI can assist in the prediction and tracking of the spread of dangerous diseases by assessing data from the government, healthcare, and other sources. It can be a vital tool in the global fight against diseases and pandemics.
Use cases of AI in the medical industry
The following are six areas where AI's potential exceeds that of traditional therapeutic methods.
Keeping Well: AI assists individuals to stay healthy so they don't need to see a doctor as often, if at all. AI and the Internet of Medical Things (IoMT) are already being used in consumer health apps to motivate healthier behavior and assist with proactive management of a healthy lifestyle. It entitles patients to take charge of their health and well-being.
Decision Making: Predictive analytics can help in quick clinical decision-making and actions, as well as to improve treatment by aligning ample health data and with suitable and timely judgements it can prioritize size administrative activities.
Treatment: AI can assist doctors in taking a more holistic strategy for illness management, better-coordinating care plans, and assisting patients in managing and adhering to long-term treatment programs.
Early detection: AI is already being used to diagnose illnesses more specifically and in the initial stages. Such as in cancer, a large percentage of mammograms provide disingenuous findings, resulting in one out of every two healthy women being diagnosed with cancer. AI is allowing mammograms to be reviewed and translated 30 times quicker with 99 percent accuracy, curtailing the need for needless biopsies.
Diagnosis: It enables healthcare companies to use cognitive technology to unlock massive volumes of health data. There are technologies that can evaluate and retain considerably more medical data than any person, including every medical publication, symptom, and case study of therapy and reaction from around the world.
End-of-life care: In elderly people, loneliness is a common occurrence and as they begin to age more, dying illnesses such as dementia, heart failure, and osteoporosis become common. AI has the potential to transform end-of-life care (care of the elderly) by allowing patients to be independent for extended periods, decreasing the need for hospitalization and care facilities. AI, paired with developments in humanoid design, is allowing robots to hold 'conversations' and other social interactions with people to keep aging minds strong.
Future of AI in healthcare-
The most arduous impediment for AI in healthcare is convincing its acceptance in daily clinical practice. Clinicians may eventually gravitate towards activities that require specialized human abilities, such as those that require the highest degree of cognitive function.
Perhaps in the future, the only healthcare practitioners who will miss out on AI's full potential are those who refuse to collaborate with it.
What would a future where AI is integrated with every sector of the healthcare sector look like?
Below are some of the hypotheses that researchers and scholars believe would help the healthcare industry in gaining more trust and in being more reliable.
Mind and Machine unification through brain-computer interfaces: Because of neurological illness and injury to the nervous system, some patients' abilities to talk, move, and connect meaningfully with others can be taken away. Artificial intelligence-assisted brain-computer interfaces (BCIs) may help in restoring such essential experiences to individuals who worry they may be lost forever. We can decode the neurological activities linked with the intended motion of one's hand using a BCI and artificial intelligence. Patients with ALS, strokes, or locked-in syndrome, as well as the 500,000 individuals worldwide who suffer spinal cord injuries each year, might aid extensively from the integration of AI and BCIs.
AI-enabled Radiology Tools: MRI machines, CT scanners, and x-rays provide radiological pictures that provide non-invasive insight into the inner workings of the body. Many diagnostic procedures, however, still rely on actual tissue samples taken through biopsies, which come with dangers such as infection. Experts expect that artificial intelligence will allow the next generation of radiological instruments to be inclusive and factual enough to eliminate the requirement for tissue samples.
Expanding healthcare in formulating and poor regions: In developed countries across the globe, scarcity of qualified healthcare personnel, such as ultrasound technologists and radiologists, can severely limit access to life-saving care. By taking over some of the diagnostic duties that are traditionally handled by humans, artificial intelligence may be able to help mitigate the impacts of the considerable scarcity of competent clinical workers.
Artificial intelligence imaging techniques can scan chest x-rays for symptoms of TB with a degree of accuracy that is frequently equivalent to people. This capacity might be made available to physicians in low-resource locations via an app, eliminating the requirement for a diagnostic radiologist on site.
Advancing the use of Immunotherapy for cancer treatment: Immunotherapy is one of the most promising therapies for cancer treatment. Patients may be able to win against risky tumours by bombing them with their immune system. However, prevailing immunotherapy alternatives merely assist a microscopic percentage of patients, as oncologists still lack a valid and explicit approach for inferring which patients may profit from this treatment.
AI and ML algorithms can synthesize very complex information and open up new avenues for tailoring medicines to a person's genetic composition.
Limiting the threat of antibiotic resistance: Antibiotic resistance is becoming an increasing hazard to people all over the world, as a result of the misuse of these life-saving drugs, superbugs exempt from antibiotics have emerged. Multidrug-resistant infections have the potential to wreak havoc in hospitals, killing hundreds of people every year.
Data from electronic health records can advance the detection of infection trends and the identification of people at risk before they develop symptoms. Using machine learning and AI to drive these analytics can augment their exactitude and give healthcare practitioners with faster, more accurate alerts. AI technologies can meet or exceed expectations in terms of infection management and antibiotic resistance.
Personal health monitoring devices: Almost every customer now has access to gadgets with sensors that may collect useful health information. A surging proportion of health-related data is created on the road, from cell phones with step trackers to wearables that can detect a heartbeat around the clock. Amassing and analyzing this information, via apps and other home monitoring devices, as well as complementing it with information supplied by patients, can provide a peculiar perspective on individual and population health.
Artificial intelligence will be critical in extracting useful information from this huge data source.
Selfies as a Powerful diagnostic tool: Keeping with the theme of leveraging the potential of portable devices, experts feel that pictures acquired from cellphones and other consumer-grade sources will be a valuable addition to clinical quality imaging, particularly in underprivileged communities or developing countries. Every year, the quality of mobile phone cameras improves, and they can now provide photos that can be analyzed by artificial intelligence systems.
Stakeholders, healthcare experts & inventors are all itched about the potential of AI in the healthcare industry. In the coming time, AI will direct a new age of clinical excellence and exciting advancements in inpatient care by powering a new generation of tools and systems that make doctors more aware of subtleties, more efficient when giving treatment, and more likely to get ahead of developing issues.
The options are left to the imagination of the reader. But one thing seems certain: AI will pave the way for a prosperous and healthy future.
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