Artificial Intelligence (AI) has significantly influenced our daily lives and the medicolegal field. It uses computational programming to emulate human cognition and action. AI involves data analysis, problem-solving or prediction, and self-learning to adapt to various tasks. More precisely, AI refers to the capability of computer algorithms to draw conclusions based solely on input data. Its vast potential currently extends to tasks traditionally requiring human intelligence, such as visual perception, speech recognition, language translation, and decision-making.

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AI may be specialized or generalized and varies in strength from weak to strong. Weak AI consists of supervised programming that simulates human cognition and interaction, like pre-programmed responses or supervised interactions that resemble human behavior; examples include Siri and Alexa. In contrast, strong AI operates more autonomously and leverages advanced data processing techniques.

AI Applications

Everyday instances of AI include facial recognition for phone access, social media content curation, voice transcription, text composition, spam filtering, web search algorithms, digital voice assistants (e.g., Alexa and Siri), smart home devices, and driver-assist technologies.

Health-focused AI applications explore correlations between clinical data and patient outcomes. AI is utilized in diagnostics, treatment protocol creation, drug development, personalized medicine, and patient monitoring. The distinct advantage of AI over conventional health technologies lies in its ability to compile more extensive and more diverse data, process this data, and deliver well-defined outputs. This is achieved through machine learning algorithms and deep learning, which can identify behavioral patterns and generate their own logic.

Chatbots

Chatbots, software applications designed to mimic human conversation through text or voice interactions, are becoming increasingly widespread. Modern chatbots are AI systems that converse with users in natural language, effectively simulating human conversational behavior. They often employ deep learning and natural language processing techniques.

ChatGPT

ChatGPT, an AI chatbot developed by OpenAI and launched in November 2022, is known for its ability to guide a conversation toward a specified length, format, style, detail level, and language. The system considers successive prompts and replies as context at each conversation stage. Its accuracy is variable, occasionally providing plausible yet incorrect or nonsensical answers. As of January 2023, ChatGPT garnered over 100 million users, earning it the title of fastest-growing consumer application. By March 2023, 14% of Americans had tried ChatGPT. Recognizing the potential, utility, and risks associated with ChatGPT is vital. Its applications in medicine span medical education, research, and clinical decision-making.

Independent Medical Evaluations – Three Phases

Independent medical evaluations (IME) can be divided into pre-evaluation, evaluation, and post-evaluation stages.

In the pre-evaluation phase, tasks include arranging the assessment, sending notifications, and gathering records. Scheduling and notifications can be automated using platforms like Calendly. Tools like Monday.com facilitate the management of tasks and projects, such as the steps involved in an evaluation. Medical records are usually available electronically, which simplifies document management and transfer but can pose security risks. Pre-evaluation also includes the analysis of records and obtaining information about the examinee.

The evaluation phase includes gaining informed consent, taking the examinee's history, carrying out tests and inventories, performing a physical examination, and reviewing clinical studies. The examinee can be greeted using a video presentation and an AI chatbot. Technology can facilitate all these steps.

Post-evaluation involves analyzing the case based on the data gathered, current scientific knowledge, and guidelines. AI can play a significant role in data analysis and report generation.

Evaluations: Data Input, Analysis, Output

The essential steps in an evaluation include data input, analysis, and output. Historically, each step was time-consuming, and results could be unreliable. However, AI's ability to apply algorithms to develop conclusions based on input data will revolutionize these evaluations.

Data Input

Data acquisition encompasses document organization and analysis, obtaining the medical history, performing a physical examination, and reviewing studies. Acquiring the necessary data to address case-related issues is critical. For example, a thorough understanding of the history is essential for causation and apportionment analysis. In contrast, according to the AMA Guides to the Evaluation of Permanent Impairment, permanent impairment analysis requires specific data.

With virtual documents, offshore resources can be safely utilized to organize and summarize records, allowing the evaluator to analyze the content efficiently. AI document processing platforms, like wisedocs.ai, can perform tasks such as optical character recognition (OCR), record organization, duplicate removal, and key clinical information extraction.

The examinee's history can be acquired by completing online forms and discussing their content with the examinee. Soon, chatbots may interview patients, following best practice recommendations, and the results can be analyzed and summarized.

Functional inventories can be administered on paper or online, but these could be completed by interacting with a bot.

AI will likely revolutionize the physical examination process by enhancing the efficiency and reliability of clinical data acquisition. For instance, wearable sensors and mobile applications can provide more accurate measurements (e.g., Figur8). AI is increasingly being used to analyze imaging and other studies.

Data Analysis

Once the necessary data is obtained, it is analyzed. ChatGPT can summarize data, provide templates, prepare drafts, and conduct preliminary analysis. It's essential to understand the issues and questions posed by the client. Using the examinee-specific data, we then apply information from other sources, such as evidence-based medicine and guidelines.

Data Output

Typically, the final product is a report, the preparation of which can be facilitated by templates, text expanders, voice transcription, and AI-powered writing assistance (e.g., Grammarly).

Other Medicolegal Applications

Physicians involved in medicolegal work use ChatGPT to rewrite content for clarity, research specific issues, develop chronological summaries, prepare diagnosis lists, write introductions and abstracts for scientific papers, and create educational content.
Future Outlook

Predicting the exact influence of emerging technologies on the medicolegal field can be challenging, but the impact will undoubtedly be substantial for everyone involved. It's crucial to stay informed about these advancements' potential opportunities and risks.

Despite concerns about potential errors, it's worth noting that many evaluations today are performed by individuals with insufficient training, using outdated methods, which can lead to incorrect conclusions and further complications.

In a few years, or perhaps even sooner, we may see AI analyzing medical documents and interacting with patients through chatbots to determine medical diagnoses, comorbidities, suitable treatments, and patient prognoses. This AI-processed data could also be used for causation analysis, offering an objective view of occupational and non-occupational risk factors. Additionally, data from physical devices and measurement systems could be incorporated to assess work capabilities more accurately.

Consider AI's potential in assigning impairment ratings, a task that currently involves significant physician input. Based on the diagnosis-focused approach of the AMA Guides to the Evaluation of Permanent Impairment, Sixth Edition, this process could be largely automated. An AI system could identify when a patient has reached maximum medical improvement, trigger a permanent impairment assessment, process medical records and documents to extract essential clinical data, and calculate an impairment rating. After notifying the patient and deploying a chatbot to gather additional data, the AI could apply this information to the AMA Guides' criteria to determine a financial benefit, thus streamlining the process and eliminating the need for a medicolegal evaluator.

Such scenarios might seem far-fetched. However, if we had looked ahead just a few years ago, we might have found our current reality equally surprising.

Integration

AI technology is set to significantly influence all stakeholders, including attorneys, legal systems, insurers, third-party administrators, employers, and independent medical evaluation networks, fostering improved connections and cooperation.

Summary

It is vital for all stakeholders in the medicolegal field to understand the potential impact of AI on their work and to gain mastery over these technologies. Those who fail to do so risk being left behind in a rapidly evolving landscape.