AI has been adopted across several different industries and is used by thousands of businesses across the world. It’s become more prevalent since the release of GPT and DALL-E. But how is it used in Healthcare? And what are its capabilities?

Timeline of AI in Healthcare

1970s - MYCIN

In the 1970s, researchers developed a program called MYCIN, which had the ability to identify the bacterial cause of severe infections such as meningitis. It was able to recommend the correct dose of antibiotics to administer to patients based on data such as height and weight. The name was called MYCIN as it followed the pattern of antibiotic naming conventions such as “Clarithromycin” and “Erythromycin”. MYCIN was never used in practice, although this was not due to the reliability of the AI model. It was due to debate surrounding the liability of any misdiagnosis.  

1980s – CADUCEUS and DXplain

In the 1980s, the University of Pittsburgh developed CACDUCEUS, which was an early medicinal knowledge base. It could diagnose up to 1000 different illnesses. The system was built to improve on its predecessor MYCIN. Similarly to MYCIN, CADUCEUS was a rule-based expert system. It was not widely used in medical practice due to concerns with the way it handled complex cases, as it was able to identify one single disease but struggled where more than one disease was present. 

In the mid-80s, work began on DXplain, a clinical decision support system, with the first version released in 1986. DXplain has been used to help assist medical students during training using a formula that ranked potential diagnoses and scored how likely a diagnosis could be. Similarly to CADUCEUS, it was never used in a clinical setting due to a lack of support from clinicians.

2000s – Machine Learning

By the early 2000s, the use of the internet had become much more mainstream, which allowed companies to invest in more applications of AI in healthcare. Machine learning models were used to calculate drug discovery, personalized medicine and predictive analytics. In the 2000s, machine learning models were utilized in Oncology Precision Medicine, which analyzed genetic data from cancer patients to predict the best course of treatment based on genetic mutations so that physicians could match patients with the most effective targeted therapies.

2010s – Deep Learning and Big Data

Advancements in AI saw investment in Deep Learning, which revolutionized how AI could be used in the healthcare industry. AI could now be used to process large data sets like health records and genetic information.  It also became more proficient than humans for some specific tasks. For example, the U.S. FDA approved the first AI device that could diagnose diabetic retinopathy without human interaction. The device, called the IDx-DR, was able to reduce barriers to screening and improve gaps in eye care for people with diabetes by analyzing images of the eye taken with a retinal camera.

How is AI Used in Healthcare Today?

AI has come a long way since the 1970s, and after initial resistance from clinicians, it is now used in clinical practice across the breadth of healthcare practices, including diagnostics, medicine, research, operations, surgery and genetics.

Disease Diagnostics and Image Analysis

AI’s capabilities have evolved to diagnose illnesses and diseases by interpreting data and analyzing images. One of the main benefits of using AI for diagnostics and image analysis is the speed at which AI can interpret large sets of data and the accuracy of identifying any medical anomalies or diagnostic patterns.

AI is trained on millions of images to identify patterns associated with specific medical conditions so that, when using patient data, it can follow the algorithm to interpret medical images like MRIs, CT scans and X-rays and identify minute details that could be easily missed by the human eye. AI can help support clinicians to detect early signs of various conditions such as cancer, motoneuron disease and heart disease. It also has predictive capabilities which can forecast the likelihood of disease progression based on the current data available. 

Integrating AI into diagnostic imaging systems not only benefits patient outcomes from early detection and quicker interventions but also allows cost savings and increased efficiency and helps to reduce the workload of healthcare professionals so that they can focus on patient care.   

Personalized Treatments

Personalized medicine, also known as precision treatments, is used to deliver bespoke medical treatments based on the patient’s needs. It uses vast amounts of data, including patient medical records, genetic information, previous treatments and patients’ lifestyle data to create highly personalized treatments. 

AI can develop predictive modelling by machine learning that allows AI to predict which treatments are most likely to be effective based on data from similar patients. It also reads genetic data to identify patterns in genetic variations that are linked to different diseases as well as how their genetic data affects their response to different medicines, eliminating adverse reactions.

The benefits of AI’s impact on personalized treatments include significant potential in treating cancers that are resistant to standard treatments.

Predictive Analytics

AI is able to analyze data to predict future outcomes by utilizing machine learning and statistical algorithms. It can identify which patients are at risk of chronic diseases by using statistical modelling. It can also predict the likelihood of readmittance of patients within 30 days of discharge so that interventions can be organized to reduce the risks.

Predictive analytics has also been utilized in predicting and tracking disease outbreaks in order to predict the spread of disease and risks of epidemics. In the early stages of the COVID-19 pandemic, AI models were used to predict the spread of the virus and advise lockdown and social distancing measures.

One of the key benefits of using predictive analytics is enabling proactive and preventive healthcare to improve overall public health.

Clinical Trials

Clinical trials have been transformed by the impact of AI. AI is utilized across all aspects of clinical trials, from recruitment to data management. Not only is it used to analyze data from electronic health records to help identify potential participants for clinical trials, but it can also inform outreach activities to target patients who are more likely to consent to taking part in a clinical trial. 

AI can help inform various protocols by simulating the clinical trial before it takes place, enhancing the safety of the clinical trials by monitoring the likelihood of adverse reactions. AI can also be utilized so that researchers can optimize different aspects of the clinical trial.

Working with AI in the Healthcare Industry

Here at Alexander Daniels Global, we conduct direct sourcing through targeted headhunting. Your AI, Software and IoT recruitment needs can be handled by our expert team, placing the right candidates in front of you. 

Alternatively, if you’re a medical professional looking to make a move into the AI, Software or IoT industry, we welcome you to get in touch. Or, browse the career portal to apply for our varied open job vacancies. 

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