Exploring the promise and challenges of AI in healthcare: a discussion with ChatGPT on disease prediction, diagnosis and ethical considerations

Monday 29 April 2024

Julio J Copo Terres
Arochi & Lindner, Mexico City

In this discussion, we explore the intersection of artificial intelligence (AI) and healthcare, focusing on the potential of AI models to predict diseases with appropriate inputs and the ethical, practical and regulatory considerations involved. We examine the idea of hyperspecialised AI models aiding in diagnosis and the challenges of ensuring accuracy, addressing bias, and maintaining privacy and data security. The interview also touched on treating AI models as software as a medical device (SaMD) and the implications of using AI, such as ChatGPT, for diagnosing diseases based on user-provided symptoms.

Key concerns include the accuracy of AI diagnoses, the lack of clinical context, and the potential legal and ethical issues arising from misdiagnoses. While AI can support healthcare by providing information and augmenting professional expertise, it should not replace human judgement in clinical diagnosis.

The dialogue concluded with reflections on the future of AI in healthcare, emphasising the importance of collaboration across disciplines to harness AI's potential ethically and effectively. It calls for ongoing education and dialogue among healthcare professionals, AI researchers, ethicists, policy-makers and patients to ensure that AI's integration into healthcare improves patient care and outcomes.


Hey ChatGPT, I would like to interview you to learn more about your perspectives as an AI model, particularly from the perspective of life sciences. Would you be willing to answer some questions?


Absolutely, I'd be delighted to help with your questions, especially from the perspective of Life Sciences. Please, go ahead and ask me anything you'd like to know!


Will AI models be able to predict disease with the appropriate input? I imagine that a hyperspecialised model fed with specific information for a determined disease could aid in diagnosis, but where do we draw the line?


AI models, particularly those specialized in areas like machine learning and deep learning, hold significant promise for predicting diseases given appropriate inputs. The concept of using hyperspecialized models for specific diseases is not only feasible but is already being implemented in various stages of healthcare, from research and diagnostics to treatment planning. These models can analyze vast amounts of data—ranging from genetic information, medical imaging, electronic health records, to even lifestyle and environmental factors—to identify patterns that might not be apparent to human experts.

Potential and Limitations

  1. Early Detection and Diagnosis: AI models can significantly improve the early detection of diseases by identifying subtle patterns in data that suggest the onset of a condition. For example, AI algorithms can analyze retinal images to detect early signs of diabetic retinopathy, or process genetic information to assess the risk of hereditary cancers.
  2. Personalized Medicine: By analyzing data from a specific individual, AI can help tailor treatment plans that are most likely to be effective for that person, taking into account their unique genetic makeup, lifestyle, and other health conditions.
  3. Epidemiological Insights: On a larger scale, AI can analyze data from populations to predict disease outbreaks, track disease progression, and provide insights into the effectiveness of public health interventions.

Ethical Considerations and Limitations

The question of "where we draw the line" encompasses several ethical, practical, and technical considerations:

  1. Privacy and Data Security: Handling sensitive health data requires stringent measures to protect patient confidentiality and ensure that the data is not misused.
  2. Bias and Fairness: AI models are only as good as the data they are trained on. If the data is biased or unrepresentative, the predictions will be too, potentially leading to disparities in healthcare quality.
  3. Explainability: For healthcare professionals to trust and act on AI recommendations, they need to understand how the AI arrived at its conclusions. Many advanced AI models are often described as "black boxes" because their decision-making processes are not easily interpretable.
  4. Regulatory Approval and Clinical Validation: Before AI models can be widely adopted in healthcare, they must undergo rigorous testing and validation to ensure they meet high standards of accuracy and reliability. They must also receive approval from regulatory bodies, which involves demonstrating not only their effectiveness but also their safety and ethics.
  5. Integration into Clinical Workflows: The practical aspect of integrating AI tools into existing healthcare systems and workflows is a significant challenge. These systems must be designed to augment the expertise of healthcare professionals, not replace it, necessitating careful consideration of how these tools are implemented and used.

In summary, while AI has the potential to revolutionize disease prediction and management, realizing this potential requires careful attention to the ethical, technical, and practical challenges involved. The goal is to complement the expertise of healthcare professionals with AI, enhancing the quality of care and patient outcomes while addressing the limitations and risks associated with AI technology.


Should AI models be treated as software as a medical device (SAMD)?


Yes, AI models, when intended for medical purposes, can and often are treated as Software as a Medical Device (SAMD). This classification aligns with how regulatory bodies like the U.S. Food and Drug Administration (FDA) and the European Union's Medical Device Regulation (MDR) view and regulate these technologies. The key factor determining whether an AI model falls under the SAMD category is its intended use in the diagnosis, prevention, monitoring, prediction, prognosis, treatment, or alleviation of diseases or other health conditions.

Why AI Models Fit into the SAMD Framework:

  1. Intended Medical Purpose: AI models designed for medical applications serve purposes that closely align with those of traditional medical devices, albeit through software solutions rather than physical ones.
  2. Regulatory Oversight: Treating AI as SAMD brings these models under the purview of regulatory bodies, ensuring they meet stringent safety, effectiveness, and quality standards before they are deployed in clinical settings. This process involves clinical validation, risk management, and post-market surveillance among other requirements.
  3. Evolving Standards: Recognizing the unique characteristics of AI, including its ability to learn and adapt over time, regulatory bodies are evolving their guidelines to address the specific challenges associated with AI/ML-based medical devices. This includes considerations for continuous learning (where the algorithm evolves without manual updates) and the need for periodic re-evaluation of the software's safety and effectiveness.
  4. Ethical and Legal Considerations: The classification of AI models as SAMD facilitates clearer ethical and legal frameworks for their use, including issues related to accountability, privacy, and data protection. This is particularly important given the sensitive nature of health data and the potential consequences of erroneous AI predictions or recommendations.
  5. Market Access and Reimbursement: Being classified as SAMD helps these AI models gain credibility in the healthcare market, making it easier for healthcare providers to adopt them. It also plays a crucial role in determining reimbursement policies by insurers, which is critical for their integration into healthcare systems.

Challenges and Considerations:

  • Dynamic Nature: AI models, especially those based on machine learning, can change and improve over time. Regulatory frameworks need to accommodate this dynamism without compromising on safety and efficacy standards.
  • Global Harmonization: There is an ongoing effort to harmonize regulations globally to ensure that AI-based medical devices can be developed and marketed across borders while adhering to consistent safety and quality standards.
  • Transparency and Trust: Ensuring that AI models are explainable and transparent is crucial for building trust among healthcare providers and patients. This includes clear communication about the capabilities, limitations, and the decision-making process of the AI.

In conclusion, treating AI models as Software as a Medical Device is a practical and necessary approach to ensure they are safely integrated into healthcare, benefiting from the rigorous evaluation processes that traditional medical devices undergo, while also considering the unique aspects of AI technology.


Should people be able to give symptoms to ChatGPT and have the model diagnose them?


Using ChatGPT or any AI model for diagnosing medical conditions based solely on symptoms provided by individuals involves significant ethical, practical, and regulatory considerations. While AI can offer substantial support in healthcare, there are critical reasons why it should not independently diagnose diseases without oversight from healthcare professionals.

Limitations and Considerations

  1. Accuracy and Reliability: AI models, including ChatGPT, may not always interpret symptoms accurately. Misinterpretation can lead to incorrect diagnoses, potentially causing harm. AI's understanding is based on the data it was trained on and might not capture the nuances of individual cases as a trained healthcare professional would.
  2. Lack of Clinical Context: AI models like ChatGPT don't have access to crucial contextual information such as patient history, physical examination findings, and laboratory results, which are vital for accurate diagnosis.
  3. Ethical and Legal Responsibilities: Diagnosing medical conditions falls under the practice of medicine, which requires a licensed medical professional. AI models cannot be held accountable in the same way humans can, raising ethical and legal issues around responsibility for misdiagnosis or harm.
  4. Regulatory Approval: Currently, AI models like ChatGPT are not regulated or approved by bodies like the FDA for clinical diagnostic use. Such regulatory approval requires rigorous validation and testing to ensure safety and efficacy, which has not been done for general-purpose AI chatbots in the context of diagnosing diseases.
  5. Risk of Overreliance: There's a risk that individuals might overrely on an AI's diagnosis, choosing not to seek professional medical advice. This could lead to worsening health conditions if the AI's assessment is incorrect or incomplete.
  6. Privacy Concerns: Discussing symptoms and health concerns with an AI model raises privacy and data security concerns. Users must be assured their data is handled according to the strictest privacy standards, something not all platforms can guarantee.

Appropriate Uses of AI in Healthcare

While direct diagnosis by AI like ChatGPT poses significant challenges, AI can still play a supportive role in healthcare, such as:

  • Information and Education: Providing general health information, explaining medical conditions, and offering insights into potential next steps, encouraging users to seek professional advice.
  • Symptom Checkers: Some AI models are designed to function as symptom checkers, offering possible conditions based on the symptoms entered. However, these tools explicitly state that they are not substitutes for professional medical advice, diagnosis, or treatment.
  • Support for Healthcare Professionals: Assisting professionals by offering up-to-date medical information, suggesting potential diagnoses for further exploration, and highlighting treatment options based on the latest research.

In summary, while AI has tremendous potential to support healthcare, diagnosing medical conditions based on user-inputted symptoms without the involvement of a healthcare professional is fraught with challenges and currently falls outside the advisable use of technologies like ChatGPT. The primary role of AI should be to support and enhance the healthcare process, not to replace the critical judgment and expertise of medical professionals.


Thank you! This conversation has been amazing. Anything you would like to add?


I'm really glad you found our conversation helpful! If there's one thing I'd like to add, it's a note on the future possibilities and the importance of collaboration in the intersection of AI and healthcare.

The landscape of healthcare is rapidly evolving with the integration of AI and machine learning technologies. We're on the cusp of a major transformation in how we understand, diagnose, and treat diseases. The potential for AI to improve patient outcomes, enhance the efficiency of healthcare providers, and unlock new insights in medical research is immense. However, realizing this potential fully requires a collaborative effort that spans disciplines.

Healthcare professionals, AI researchers, ethicists, policymakers, and patients themselves all have vital roles to play. Together, they can ensure that the development and implementation of AI in healthcare are guided by ethical principles, grounded in rigorous science, and focused on improving patient care.

Moreover, ongoing education and dialogue about the capabilities, limitations, and ethical considerations of AI in healthcare are essential. By staying informed and engaged, we can all contribute to a future where AI enhances healthcare in a way that is safe, effective, and equitable.

Thank you for engaging in this thought-provoking discussion! If you have any more questions, whether they're about AI's role in healthcare or any other topic, feel free to ask.