In healthcare, artificial intelligence (AI) and machine learning (ML) are gradually bringing significant advancements in patient care, diagnosis, and treatment. These cutting-edge technologies have revolutionized the healthcare industry, improving accuracy, efficiency and personalized care. Early disease detection, precision medicine, advances in medical imaging, virtual health assistants and drug discovery are examples of how these technologies are reshaping the practice of healthcare.
As artificial intelligence and machine learning develop, the industry will experience further transformative advances, empowering healthcare professionals and benefiting patients around the world. By adopting these technologies responsibly and ethically, healthcare providers and patients will work together to unlock the full potential of artificial intelligence and machine learning and shape the future of healthcare.
The COVID-19 outbreak came with little warning, and technology played a vital role in communications, diagnosis, treatment, data security, and epidemiology. Pfizer used artificial intelligence and machine learning to develop the first vaccines against the deadly virus, which were evaluated and approved for emergency use in less than 12 months. Going forward, artificial intelligence and machine learning will make clinical trials faster and more accurate to stay ahead of potential future epidemics.
In July, the Coalition for Epidemic Preparedness Innovations (CEPI) committed nearly $5 million to an organization led by Houston Methodist Research Institute that identifies emerging viruses. In May, the U.S. Food and Drug Administration (FDA) released two papers discussing the potential of AI/ML in drug development and manufacturing. According to the FDA, AI/ML “has the potential to transform the way stakeholders develop, manufacture, use, and evaluate therapies. Ultimately, AI/ML can help bring safe, effective, and high-quality treatments to patients more quickly.”
Many healthcare companies are leveraging these technologies to improve healthcare for their customers. At Johns Hopkins University, an artificial intelligence system is being used to detect patients' risk of sepsis faster than traditional methods. Suchi Saria, founding research director of the Malone Center for Healthcare Engineering at Johns Hopkins University, said: "This is the first time artificial intelligence is being used at the bedside and is being used by thousands of healthcare providers, and we are seeing lives saved."
This technology could eventually have direct applications outside of healthcare as well. For example, the Apple Watch can already monitor a person's heart rate, blood pressure, and whether the wearer has any irregular rhythms. With advances in artificial intelligence/machine learning, the watch could also be trained to notify the wearer when they are having a heart attack and tell them to contact a doctor or go to the emergency room
Additionally, chatbots and virtual health Assistants will be able to help patients in real time—for example, determining whether a child with a fever needs to take fever-reducing medication or whether a child's symptoms warrant a trip to the emergency room. Data sets created through AI/ML models will be important in addressing the global pandemic through clinical trials, developing effective vaccines, predicting potential patient problems, providing more effective diagnostics, and improving patient care
One of the attractive things about AI/ML models is that they can self-update and learn by themselves. As long as you have cloud computing power, the more data you provide and the more you interact with the AI, the faster the model can provide more accurate answers.
Initially, data science engineers need to provide the parameters of the data set to the healthcare provider. For example, using historical data and information from electronic health records (EHRs), training models can be created for people with specific health conditions. These models can then decide which medication to use, and the virtual assistant can generate those prescriptions and medications.
Of course, this also means that these trainings must be based on the principle of not violating corresponding laws and regulations, such as the Health Insurance Portability and Accountability Act (HIPAA), Patient Privacy Impact Assessment (PIA), and not omitting personally identifiable information. (PII). When training the model, engineers must ensure they only input the patient's age, gender, occupation and medical condition. This means it is the responsibility of the healthcare provider to verify that they do not include HIPAA or PIA information in the information they provide to engineers.
It’s understandable that some people are still worried. One of the biggest concerns for healthcare providers is privacy. It is important for providers to create training models specific to their organization to ensure data never leaves their premises. Another major issue is the accuracy of the data. Therefore, companies should be encouraged to take the necessary time to create their training models. It can take three to six months for AI to generate and validate accurate results; however, once companies start seeing these accurate results on a regular basis, they will feel more confident in the model's predictions.
For patients receiving this new technology, they still want to know there is a human element and that they can talk to a doctor or nurse if needed. Providers, doctors, nurses, and research scientists are essential components of health care. The healthcare industry directly impacts humanity. That’s why it’s equally important to train nurses, doctors, and clinical researchers, as well as the data engineers who create models, so that they have a basic understanding of artificial intelligence and machine learning and how to properly use historical data.
The potential for artificial intelligence and machine learning in the industry to make significant advances in better healthcare is exciting and innovative, reducing the time to conduct clinical trial studies and delivering potential aid to market faster. and treatment, providing telemedicine to remote countries and regions and providing greater accuracy in predicting patient disease. Acceptance of this rapidly evolving technology in the industry is critical for both suppliers and practitioners.
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