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The integration of artificial intelligence (AI) and machine learning (ML) into healthcare has enabled remarkable advancements in patient care, diagnosis, and treatment. These cutting-edge technologies have revolutionized the healthcare industry, improving precision, efficiency and personalized care. Early disease detection, precision medicine, advances in medical imaging, virtual assistants and drug discovery are just a few examples of how these technologies are reshaping healthcare practices.
The industry will see further transformative advancements as AI and ML evolve, empowering healthcare professionals and benefiting patients around the world. By adopting these technologies responsibly and ethically, providers and patients can unlock the full potential of AI and ML and shape the future of healthcare.
Lessons from a global pandemic
The COVID-19 pandemic occurred without warning and technology has played a critical role in communication, diagnosis, treatment, data security and epidemiology. Pfizer leveraged AI and ML to create the first vaccines to combat the deadly virus, which were evaluated and authorized for emergency use in less than 12 months. In the future, clinical trials will leverage AI and machine learning with even greater speed and precision to anticipate potential future pandemics.
In July, the Coalition for Epidemic Preparedness Innovations (CEPI) pledged nearly $5 million to a consortium led by the Houston Methodist Research Institute identify emerging viruses. In May, the Food and Drug Administration (FDA) published two articles discussing the potential of AI and ML in drug development and manufacturing. According to the FDA, AI and ML “have the potential to transform how stakeholders develop, manufacture, use, and evaluate therapies.” Ultimately, AI/ML can help deliver safe, effective, and high-quality treatments to patients more quickly.
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Anticipating Health problems
Many healthcare companies are already capitalizing on these techniques to improve their customers’ healthcare. At Johns Hopkins University, an AI system is used to detect a patient’s risk of sepsis faster than traditional methods. “This is the first case where AI is being implemented at the bedside, used by thousands of providers, and we are seeing lives saved,” according to Suchi Saria, founding research director of the Malone Center for Engineering in Johns Hopkins Healthcare.
This technology could also potentially be applied directly beyond the healthcare field. The Apple Watch, for example, can already monitor a person’s heart rate, blood pressure and if the wearer has irregular rhythms. With more significant advances in AI and ML, the watch could also be trained to warn wearers if they are at risk of having a heart attack and tell them to contact their doctor or go to the emergency room.
Additionally, chatbots and virtual assistants will be able to help patients in real time, for example by determining whether a child with a fever needs to take fever medication or whether their symptoms warrant a visit to the emergency room. Datasets created by AI and machine learning models are essential to solving a global pandemic through clinical trials, developing effective vaccines, predicting potential patient problems, providing more effective diagnostics, and improving care to patients.
Setting parameters
One of the attractions of AI and ML models is that they update automatically because they are self-learning. As long as businesses have cloud computing capability, the more data provided and interactions undertaken with AI, the quicker the models will be able to provide more accurate answers.
Initially, data science engineers will need to provide data set parameters to healthcare providers. Using historical data and information from electronic health records (EHRs), training models can be created for an individual with a particular health condition, for example. The models can then determine which medications to use, and the virtual assistants can generate those prescriptions and medications.
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Of course, this also means that these trainings must be delivered in such a way that Health Insurance Portability and Accountability Act (HIPAA) laws are not violated, Patient Privacy Impact Assessment (PIA) is not not violated and that personally identifiable information (PII) is omitted. When training models, it is essential that engineers ensure that they only capture the age, gender, occupation, and health status of patients. This means that it is the responsibility of healthcare providers to ensure that they are not including HIPAA or PIA information in the information provided to engineers.
Allay worries
Some are hesitant to enter this brave new world, and that’s understandable. One of the biggest concerns for healthcare providers is confidentiality. It is important for providers to create training models specific to their organization to ensure that data never leaves their premises. The other main concern is the accuracy of their data. A negatively impacted patient can destroy an organization. Companies should therefore be encouraged to take the time necessary to create their training models. AI can take three to six months to generate and validate accurate results; However, once businesses start seeing these specific results regularly, they will be able to be more confident in the models’ predictions.
The future is now
For patients to adopt this new technology, they still want to know that there is a human element and that they can speak to a doctor or nurse if necessary. Providers, doctors, nurses and research scientists are a necessary part of the equation. The healthcare sector has a direct impact on human beings. This is why it is also important to train the nurses, doctors and clinical researchers alongside the data engineers creating the models so that they have a basic understanding of AI and ML and understand how to properly use the historical data.
The possibilities for AI and ML in industry to make significant advances in improving healthcare are exciting and innovative, offering reduced timelines to conduct research for clinical trials, providing potential assistance and remedies to market faster, providing telemedicine to remote countries and regions, and providing greater accuracy in predicting patient illnesses. The adoption of this rapidly growing technology in the industry is crucial for providers and practitioners, as it creates a future in which pandemics can potentially be avoided, and AI insights can generate preventative methods, enabling people to live longer and healthier lives.
About the authors: Anil Maktala is a solutions architect at Amazon Web Services. A seasoned IT expert, Anil has over 18 years of experience in software development. His career spans a wide range of industry sectors, including healthcare, publishing and insurance. Anil’s impact extends to mentoring and guiding many engineers, fostering their professional growth and achievements. He holds a Bachelor of Technology degree from Jawaharlal Nehru Technological University, India. Connect with Anil on LinkedIn. 
Arundeep Nagaraj is a Lead Solutions Architect at AWS. With over a decade of experience working with global teams, clients, and developers, Arundeep has a proven track record of leading cross-functional teams, managing complex projects, and delivering products that exceed expectations. Connect with Arundeep on LinkedIn And Twitter.
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