Empowering Medical Professionals in the Age of AI

Introduction:

In today’s rapidly evolving healthcare situation, the integration of artificial intelligence (AI) has become increasingly crucial for medical professionals, public health experts, and epidemiologists in their day-to-day work. This article aims to highlight the need for medical professionals to be abreast with AI, with a specific focus on epidemiology and public health. Many doctors can avail great benefits by learning how to prompt the large language models (LLMs) correctly and this blog will serve as a primer for the same.

What is the Need for Medical Professionals to Be Aware and Utilize AI?

As medical professionals, public health experts, and epidemiologists, staying updated with technological advancements that affect medicine and care of the public is essential to provide optimal care and effectively combat public health challenges. The reduced dependance on manpower to go through the diagnostic test data, slides or radiologic imaging makes AI a helpful ally in public health. By embracing AI, medical professionals can unlock new possibilities, augment their expertise, and deliver personalized and more empathetic care.

Use Cases of AI in Medicine and Epidemiology:

1. Diagnosis and Imaging: AI algorithms can analyze medical images, such as X-rays, CT scans, and MRIs, precisely. These algorithms can detect patterns and anomalies that may be missed by humas, facilitating early and accurate diagnoses. This technology expedites the diagnostic process, reduces human error, and improves patient outcomes. The findings of the AI cannot be treated as final and human intervention is always needed to confirm the findings.
   A case in point id the AI trained on the western population X-rays tends to label even normal radiographs from the Indian population as suspects of TB. This is because of the random opacities in the X-ray that pop-up because of the air pollution that is more prevalent in the subcontinent than in the west.
2. Drug Discovery and Development: AI accelerates the drug discovery process by analyzing vast amounts of data, including scientific literature, clinical trials, and genetic information. Machine learning algorithms can identify potential drug targets, predict drug efficacy, and optimize treatment regimens.
   Presently pharmaceutical companies spend millions of dollars in just getting to a stage where they have a testable molecule on their hands. AI can analyze protein structure and can “think” of molecules that fit the 3D scaffolding and this alone can expedite the drug invention by years and save a lot of resources.
3. Public Health Surveillance: By analyzing real-time data from various sources, including social media, electronic health records, and sensor networks, AI algorithms can identify patterns and detect potential epidemics early on. This early warning system allows public health professionals and epidemiologists to take proactive measures to control the spread of diseases and protect communities before the diseases get a bigger concern.
4. Precision Medicine: AI enables personalized treatment strategies by analyzing patients’ genetic data, medical history, and lifestyle factors. This approach helps identify the most effective treatments for individual patients, minimizing adverse effects and maximizing therapeutic outcomes.

Introduction to Prompt Writing:

Prompt writing is a valuable tool that can enhance critical thinking, problem-solving skills, and creativity among medical professionals and epidemiologists. Prompts can be in the form of open-ended questions or hypothetical scenarios that encourage individuals to explore new perspectives, analyze complex situations, and generate innovative solutions.

Examples and Elaborations on Prompts for Medical Professionals and Epidemiologists:

• Be Clear and Specific: For instance, instead of asking, “Tell me about diseases,” provide a more specific prompt like, “What are the major risk factors and symptoms associated with cardiovascular diseases?”
• Set Expectations: Make it explicit that you’re looking for factual information or want the AI system to explain a concept. This helps guide the response and ensures it focuses on the requested information.
• Ask for Reasoning: If you want justification or reasoning explicitly ask for it. For example, you can prompt with, “Why do you think this treatment option is recommended?”
• Specify Format: If you prefer a specific format for the response, such as a bullet point list, pros and cons analysis, or step-by-step instructions, mention it in the prompt.
• Define Constraints: If there are any limitations or parameters to consider, specify them in the prompt. For instance, if you want the AI system to recommend treatments within a certain budget or consider specific patient demographics.
• Encourage Nuanced Responses: If you’re interested in an in-depth analysis or a balanced perspective on a topic, example prompts would be, “Please discuss both the advantages and limitations of this approach.”
• Use Priming: Providing a brief introduction or background information in the prompt can guide the AI system’s response. Priming can help set the context and ensure the AI system understands the specific angle or perspective you’re seeking.
• Iterate and Refine: If the initial response doesn’t fully meet your expectations, consider iterating on the prompt by providing additional context or asking for clarification.

Conclusion:

In the realm of medicine, AI has emerged as a powerful ally, offering a wide range of applications to medical professionals, public health experts, and epidemiologists. By leveraging AI, doctors can enhance their diagnostic accuracy and treatment outcomes, while epidemiologists can improve disease surveillance and response strategies. Embracing AI and prompt writing can pave the way for a future where healthcare is more precise, accessible, and patient-centric.

References:

1. Topol, E.J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44-56. doi: 10.1038/s41591-018-0300-7
2. Obermeyer, Z., & Emanuel, E.J. (2016). Predicting the future — big data, machine learning, and clinical medicine. The New England Journal of Medicine, 375(13), 1216-1219. doi: 10.1056/NEJMp1606181
3. Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. The New England Journal of Medicine, 380(14), 1347-1358. doi: 10.1056/NEJMra1814259

Chen, J.H., Asch, S.M., & Machine Learning and Prediction in Medicine — Beyond the Peak of Inflated Expectations. The New England Journal of Medicine, 376(26), 2507-2509. doi: 10.1056/NEJMp1702071