AI in Healthcare
AI and its applications in healthcare organizations have been discussed for many years. Biomedical problems can be credited as the pioneering use of AI as far back as the early 1970s. And they have come a long way from that by ensuring they change the dynamics of the healthcare industry through cost saving, improvement of patients’ results and increased effectiveness.
What is AI Healthcare?
The branch of AI commonly applied in health care comprises of algorithms such as machine learning, natural language processing, deep learning among others in analysis of medical data. It helps in diagnosing diseases, in treatments, in discovering new drugs, in patients’ observation, and in managing different overall tasks. AI systems can use different forms of patient data like patient data, medical records, Images , and Genomic data to gain information, make decisions, predict results, and recommend treatment a patient needs.
The History of Artificial Intelligence in Healthcare
Most of the terms most commonly used in reference to AI were coined as far back as the summer of 1955 in a proposal to hold a conference at Dartmouth College. Now, the emerging of AI applications was not introduced to the healthcare domain until early 1970s where due to research and development MYCIN, an AI program that aims at finding treatment of blood infections were developed. Subsequently with the growing research of AI, the American Association for Artificial Intelligence was formed in 1979 which now is called the Association for the Advancement of Artificial Intelligence (AAAI).
In the course of the 1980 s and 1990 s, the elaboration of new AI systems had an important role in promoting different medical innovations. These AI technologies facilitated:
- Increased intensity of data flow and usage; this warrants faster and more efficient analysis of medical data.
- Specialized accuracy and support in operations, with the employment of AI technologies to increase the accuracy and facilitate surgeries.
- Bioinformatics DBA begins with intensive comparative analysis and information mapping of biological databases using AI’s strong computational capabilities for the systematic scanning and analysis for understanding of biological information.
- General use of EHRs and incorporation of AI to facilitate the use of electronic data to enhance patient care.
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Use Cases
Pros and Cons of AI Healthcare
- Real-time, Precise data
- Cost -efficient care
- Specialization for the Patients
- Better clinical assessment of patient’s condition and decision making on the best line of treatment to be administered.
- Refines the operations and functioning of different programs.
- For data analysis and diagnosis enhancement purposes depending on the current findings on Electronic Health Record.
- Higher productivity and reduced staff pressure in performing their tasks.
- Dependence and the delegation of administrative duties and activities with the aid of artificial intelligence or AI.
- Training issues (It must be noted that training involves the use of data sets that has to be very meticulously selected).
- Despite everything still requires the input of a human.
- Another factor that risks to be produced by his leadership style is unemployment.
- Increased security risk
- Inaccuracies could occur.
- Increased accuracy in the medical processes that are carried out and in the diagnosis given.
- More availability in delivering healthcare services and quickness.
- Wave medical interventions based on specific peculiarities of a given patient and his or her requirements.
- Using the data you collect to predict potential health risks and or a diseases progression.
- Offering the best advisory and decision-making services to the healthcare practitioners.
- Simplifying processes and increasing the efficiency of processes for greater output.
- Efficient solutions on how one can use their resources or avoid using a lot of money.
- Security of the collected data and the processing of personal information being utilized in artificial intelligence systems.
- Lack of clarity and ambiguity on how and why decisions are made by the different models used in AI.
- The types of data, approaches, and training and developing of the AI implementations.
- Lack of standard for AI proper use, or a playbook that defines how AI should be managed, deployed, and put to good application.
- The case of ethical issues and concerns of the AI development and implementation.