How is Artificial Intelligence Changing Medical Sciences
Artificial intelligence is a broad field of computer science focused on developing intelligent computers capable of doing activities that normally require human intelligence. AI applications involve automated visual perception, decision-making, speech recognition, and language translation interfaces. AI is a multidisciplinary field of study.
The term artificial intelligence (AI) is largely acknowledged to have been invented in 1956 when American computer scientist John McCarthy et al. convened the Dartmouth Conference. Prior to that, efforts in the field of AI include Alan Turing's Turing test as a measure of machine intelligence and Dietrich Prinz's chess-playing software.
AI in Medical Science: A Brief History
Great strides have been achieved in the use of artificially intelligent systems in patient diagnosis. For example, in the field of visually oriented specialties such as dermatology, Esteva et al. and Hekler et al. employed clinical imaging data to construct classification models to help clinicians in the detection of skin cancer, skin lesions, and psoriasis. Esteva et al. used 129,450 images to train a deep convolutional neural network (DCNN) model to classify the images into one of two groups: seborrheic keratosis or keratinocyte carcinoma; and benign nevus or malignant melanoma.
They also proved that the DCNN performed on par with 21 board-certified dermatologists. Their study shows that AI systems were able to classify skin cancers with such a degree of proficiency comparable to dermatologists while requiring only a fraction of the time to train the model compared to physicians who spent years in medical school and relied on experience gained through patient diagnosis over decades.
Much research has also been conducted in the fields of AI and patient prognosis. There are various benefits to having such an artificially intelligent model. These include:
• Automated diabetic retinopathy grading leads to enhanced efficiency in diagnosing more patients in less time.
• Providing second opinions to ophthalmologists;
• Identification of diabetic retinopathy in its early stages because of the model's capacity to analyze pictures at the granular level, something a human opthalmologist cannot accomplish;
• A wide range of screening programs are available, lowering access obstacles.
Significant progress has been achieved in using AI systems to drug discovery and providing individualized therapy alternatives. Verge Genomics, for example, focuses on the cost-effective deployment of machine-learning algorithms to evaluate human genetic data and develop medications to battle neurological disorders such as Alzheimer's, Parkinson's, and amyotrophic lateral sclerosis (ALS).
How is AI used in healthcare? Artificially intelligent systems are also being used in the healthcare industry to improve patient care, user experience, and physician assistance by employing AI assistants. Companies have created systems that assist with clinical difficulties, such as:
• The AI technology may scan several scheduling systems across various facilities to determine which doctors are on call and schedule the next available appointment.
• Answering prescription-related queries, such as medicine availability and cost-effective substitute medications
• Assisting clinicians in searching hospital protocols, a list of accessible clinical instruments, and available pharmaceuticals via a mobile application, therefore optimizing hospital workflow.
AI Applications Today
To combat the new coronavirus (COVID-19) pandemic, the most current use of AI in global healthcare is the projection of emergent hotspots utilizing contact tracing and aircraft passenger data. Contact tracing is a disease management strategy government officials use to minimize illness spread. Contact tracing operates by contacting and telling individuals who have been exposed to an individual who has caught the disease and advising them to quarantine to prevent the sickness from spreading further. According to a certain newsroom, tech titans have joined forces to establish a contact tracking platform that will leverage artificial intelligence systems via application programming interfaces, also known as smartphone APIs. Users who wish to enroll on the platform will be able to report their lab findings. The platform will then use location services to contact anyone who may have been in the proximity of the afflicted person.
BlueDot, a Canadian startup, develops outbreak risk software that reduces exposure to infectious illnesses. BlueDot was the first to publish a scientific paper on COVID-19 that precisely forecasted the virus's global spread. Natural language processing (NLP), machine learning (ML), and automated infectious disease surveillance are used by the company to predict future outbreaks by assessing approximately 100,000 news stories from over 65 countries day after day, travel itinerary details and an area's climate, flight paths, temperature, and local livestock.
Conclusion: The Future of Artificial Intelligence in Medical Sciences
Despite the aforementioned limitations, AI is poised to change the healthcare business. AI systems can assist busy doctors in saving time by transcribing notes, inputting and organizing patient data onto portals (such as EPIC), and diagnosing patients, potentially functioning as a second opinion for clinicians. AI systems can also assist patients with follow-up treatment and the availability of prescription medicine alternatives. AI can also remotely diagnose patients, extending medical care to locations outside the world's major metropolitan centers. The future of AI in healthcare is exciting and promising, but there is still much work to be done.
The use of artificially intelligent systems in healthcare for wide public usage is yet largely unexplored. The FDA (US Food and Drug Administration) just recently authorized AliveCor's Kardiaband (in 2017) and Apple's wristwatch series 4 (in 2018) to detect atrial fibrillation. The adoption of a wristwatch is the first step in enabling individuals to gather personal health data and enable timely actions from medical support teams.
Modern technology has several harmful implications for mental health. However, researchers at the University of Southern California (USC), in collaboration with the Defense Advanced Research Projects Agency and the United States Army, discovered that people who have post-traumatic stress disorder (PTSD) and other forms of mental anguish are more open to discussing their concerns with virtual humans than with actual humans due to fear of judgment. This study yielded positive findings for the function of virtual assistants, resulting in the gathering of honest responses from patients, which might help doctors diagnose and treat their patients more properly and with more information.
Most global pharmaceutical firms have spent time and money developing drugs for important diseases such as cancer and cardiovascular disease utilizing AI. On the other hand, model creation for identifying neglected tropical illnesses (malaria and TB) and uncommon diseases is mainly unexplored. The FDA currently offers priority vouchers to firms that discover innovative medicines for certain disorders.
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