How is Artificial Intelligence Used in the Medical Field?

 How is Artificial Intelligence Used in the Medical Field?

An incredible amount of technology and automation in the medical field is there now. Artificial intelligence in medication and healthcare has been a trending topic in recent years. While there is a sense of great potential in applying AI in medical care, there is also concern about the loss of the “human touch” in such an essential, people-centred profession. As we know that today, medical records are digitized, appointments get scheduled online, and patients register at the health centres or clinics using their phones or computers. The use of technology has increased in all areas of life. The potential for greater use of AI in medicine is not only to reduce manual tasks and free up the doctor’s time, increasing efficiency and productivity, but it also offers us the opportunity to move to “precision medicine.”

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Artificial intelligence in medicine refers to the use of artificial intelligence technology / automated processes in diagnosing and treating patients in need of care. While diagnosis and treatment may seem like simple steps, many other background processes need to take place for a patient to be cared for, including:

  • Data collection through patient interviews and tests.
  • Process and analyze the results
  • Use multiple data sources to get there at an accurate diagnosis
  • Determine a suitable treatment method
  • Preparation and execution of the chosen treatment method.
  • Patient monitoring
  • After-sales service, follow-up appointments, etc.

The argument for greater use of AI in medicine is that much of the above could be automated – automation often means tasks getting performed faster. It also frees up a medical professional’s time when they might be doing other tasks. Doctors spend much more time entering data and office work than talking and interacting with patients. Many doctors feel that data entry and administrative tasks are reducing the doctor-patient time. The goal, therefore, is not to over-automate the medical and healthcare sectors but to deliberately and sensitively identify those areas where automation could free up time and effort. The goal is to create a balance between the effective use of technology and artificial intelligence and human forces and the judgment of trained medical professionals. For many years, the general practice of medicine has been to collect data and make generalizations. Now, we are in an era where large amounts of data can be collected and analyzed quickly, which is increasingly possible to customize treatment based on specific knowledge.

Artificial intelligence in medicine gets divided into two subtypes: virtual and physical. The virtual part ranges from applications like electronic health record systems to neural network-based guidance in healing decisions. The physical part deals with robots that help perform surgeries, smart prosthetics for the disabled, and care for the elderly. The basis of evidence-based medicine is to establish clinical correlations and knowledge by developing associations and models from the existing information database. Traditionally, we used statistical methods to initiate these patterns and associations. Computers learn the art of diagnosing a patient through two general techniques: flowcharts and the database approach.

The flowchart-based approach involves translating the history acquisition process, i.e. a doctor asking a series of questions and then arriving at a probable diagnosis by combining the presented symptom complex. Hence, it requires inputting large amounts of data into machine-based cloud networks, taking into account the wide range of disease symptoms and processes encountered in routine medical practice. The results of this approach are restricted as machines cannot observe the patient and collect data. In contrast, the database approach uses the principle of deep learning or pattern recognition which involves teaching a computer through repetitive algorithms to recognize the appearance of certain symptom groups or some clinical/radiological images.

The use of AI reduces medical costs, results in greater accuracy in diagnosis and better predictions in the treatment plan, and substantial disease prevention. Other future uses of Artificial intelligence include brain-computer interfaces, which are supposed to help people with difficulty moving, speaking, or with a spinal cord injury. They will use artificial intelligence to help these patients move and communicate by decoding neural activations.

Artificial intelligence has led to significant up-gradation in healthcare, such as medical imaging, automated clinical decision making, diagnosis, prognosis, and more. Although AI possesses the ability to transform various fields, it still has limitations and cannot replace a general practitioner. Healthcare is a complicated science subject to legal, ethical, regulatory, economic and social restrictions. To fully implement AI in healthcare, there must be parallel changes in the global environment, with numerous stakeholders, including citizens and society.

What does AI indicate for the future of doctors?

As more studies are published & discussions are held about the future of artificial intelligence and automation, different aspects of the topic emerge, particularly when it comes to something like medicine. There will always be a need for the human element of the doctor’s role in things that technology cannot provide like, judgment, Creativity and empathy, etc.

In conclusion, then, while machines are unlikely to replace or destroy the need for human doctors anytime soon, those already practicing or considering a medical professional should be willing to adapt, learn, and grow alongside other technological advances.

Conclusion:

Artificial intelligence is growing in the public health sector and will have an extensive impact on all aspects of primary care. AI-enabled computing applications will help primary care physicians better identify patients requiring additional care and provide personalized protocols for each individual. General physicians can use AI to take notes, analyze their conversations with patients, and enter required information directly into EHR systems. These apps might help in collecting and analyzing patient data & presenting it to primary care physicians, along with information regarding a patient’s medical needs. The increased use of Artificial intelligence improves productivity and efficiency level and, on the other side, reducing the use of manual labour.

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