Humanizing Artificial Intelligence Creates Better Doctors
Authors: Amina Khalpey, PhD, Ujjawal Kumar, BA, Zain Khalpey, MD, PhD, FACS
Artificial intelligence (AI) has been making significant strides in healthcare, in areas including: improving the accuracy of medical diagnoses, predicting diseases, and identifying treatment options.
While these technological advancements hold great potential to improve outcomes in healthcare and patient care, many people are skeptical about AI, fearing that it will replace human doctors. However, AI can be utilized to assist healthcare professionals rather than replace them, thereby augmenting their abilities, and ultimately making them better clinicians.
In this blog post, we will explore how humanizing AI can achieve this.
The Need For Humanizing AI:
Artificial intelligence algorithms are fundamentally designed to be logical and objective, making them excellent tools for pattern recognition and diagnosis. However, they are not empathetic, compassionate, or understanding, key skills which doctors have/develop, and are essential for good doctor-patient relationships. This lack of emotional intelligence can lead to patients feeling ignored, dehumanized, and ultimately, not getting the care they need.
Humanizing AI can help address these challenges by endowing AI with human-like qualities, such as empathy, compassion, and understanding. It can help create a more personalized approach to patient care, enhance patient engagement, and improve patient outcomes. In the following sections, we will explore how AI can be humanized to make better doctors.
Using Natural Language Processing to Improve Patient Communication:
Communication is a fundamental part of healthcare, and effective communication is key to providing high-quality patient care. However, communicating with patients is not always easy, especially when there are language barriers or patients with limited health literacy, ultimately impairing the quality of patient care that can be delivered. This is where natural language processing (NLP) may have a future role in improving the utility of AI in healthcare.
NLP is a branch of AI that enables machines to interpret and respond to human language. It can be used to analyze patient conversations and provide healthcare professionals with insights into patient needs, preferences, and behaviors. This can help healthcare professionals tailor their communication to the patient’s level of understanding, thereby increasing patient engagement and satisfaction.
For example, an AI-powered chatbot such as OpenAI’s “ChatGPT”, or Alphabet’s “Bard” can be programmed to ask patients about their symptoms, provide basic medical advice, and recommend further treatment options. The chatbot can use NLP to analyze patient responses and provide personalized recommendations based on the patient’s unique needs. This can help reduce the burden on doctors and nurses, freeing up their time to focus on more complex medical issues.
Improving Clinical Decision-Making:
Clinical decision-making is a complex process that requires healthcare professionals to consider a wide range of factors, including patient history, symptoms, test results, and treatment options. AI can assist healthcare professionals by providing them with real-time data and insights that can help them make more informed decisions.
For example, AI algorithms can be trained to analyze medical images, such as X-rays or MRI scans, and provide doctors with an accurate diagnosis. This can help reduce the likelihood of misdiagnosis, which can have serious consequences for patient health. Additionally, AI can be used to predict disease outcomes, identify at-risk patients, and provide personalized treatment recommendations.
However, it is important to note that clinical decision-making is not solely based on objective data. It also involves subjective factors such as patient preferences, values, and beliefs. AI can help healthcare professionals consider these factors by providing them with insights into patient behaviors and attitudes. This can help healthcare professionals make more patient-centered decisions that are tailored to each patient’s unique needs.
Using AI to Support Clinical Trials:
Clinical trials are a critical part of the drug development process. They help researchers understand how new drugs work, identify potential side effects, and determine the optimal dosage. However, clinical trials are expensive, time-consuming, and can be difficult to recruit participants.
AI can provide valuable insights into patient needs and behaviors, which can help researchers overcome several challenges in clinical trials. Here are some ways in which AI can help:
Identifying potential participants: AI algorithms can be used to analyze patient medical records and identify potential participants for clinical trials. By looking at a patient’s medical history, AI can determine whether they meet the eligibility criteria for a particular trial, and help researchers find suitable participants more efficiently.
Predicting treatment response: AI can help predict how patients will respond to a particular treatment based on their medical history, genetic makeup, and other factors. This can help researchers identify which patients are most likely to benefit from a specific treatment, and streamline the recruitment process.
Improving patient engagement: AI can be used to personalize communication with trial participants, improving engagement and retention. For example, AI-powered chatbots can be used to provide participants with information about the trial, answer their questions, and remind them to take medication or attend appointments.
Streamlining data collection: AI can be used to automate data collection and analysis, reducing the time and cost of conducting clinical trials. By analyzing data in real-time, researchers can identify safety issues and adjust the trial protocol as needed.
Enhancing safety monitoring: AI can be used to monitor safety during clinical trials by analyzing adverse events and predicting potential safety issues. This can help researchers identify safety concerns early on and take corrective action before they become serious.
Overall, AI can help researchers overcome several challenges in clinical trials, from identifying potential participants to improving safety monitoring. By providing researchers with valuable insights into patient needs and behaviors, AI can help accelerate the drug development process and bring new treatments to market faster.
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