AI in Medicine… What Does the Future Hold?

The Impact of AI in Medicine: A Physician’s Perspective

It’s been interesting several months with the release of OpenAI’s ChatGPT and Dalle-2 to the public and the following debates and discussions about artificial intelligence (AI) as a disruptive force across numerous industries. I’ve been testing out ChatGPT to see what it’s capable of (and more importantly, NOT capable of, at least with ChatGPT 4.0)

There is a bit of a learning curve in regard to understanding how to use “prompts” to guide the response from ChatGPT.

So I actually sat down with it and asked it the following question:

‘”Write an article about the potential impact of AI in medicine (pros and cons) from a physician’s perspective. what are the main issues? Will FDA need to approve AI chat bots for patients? Who is responsible for medical errors from AI? Please write in an authoritative tone and analytical writing style.

And here is what ChatGPT came up with (in italics)


Artificial Intelligence (AI) is transforming the world of medicine, offering numerous benefits while also raising concerns. In this article, we will explore the potential impact of AI in medicine from a physician’s perspective, addressing both the pros and cons. We will also discuss the involvement of the FDA in approving AI chatbots and who is responsible for medical errors resulting from AI.

AI in Medicine: Pros

Faster Diagnoses

AI has the potential to revolutionize medical diagnosis by quickly analyzing large volumes of data, including medical images and electronic health records. This can lead to faster diagnoses and better patient outcomes.

The use of AI in diagnostics has shown promising results in various medical fields, including radiology, pathology, and dermatology. Advanced algorithms can identify patterns in medical images, such as X-rays and MRIs, that may be missed by human experts. AI can also analyze large datasets of symptoms, lab results, and patient histories to identify potential conditions more quickly. As a result, physicians can make more accurate and timely diagnoses, leading to improved patient outcomes and reducing the burden on healthcare systems.

Personalized Treatments

AI can help develop personalized treatment plans for patients by analyzing their individual medical history, genetic information, and other factors. This enables physicians to make more informed decisions and deliver targeted therapies.

AI’s ability to analyze vast amounts of data, such as genomic information and biomarkers, allows for more precise identification of individual patient characteristics. This insight, combined with the knowledge of specific disease mechanisms, can aid in designing tailored therapies. In oncology, for example, AI can help identify the most effective treatment options for cancer patients based on their unique genetic profile. Furthermore, AI-driven drug discovery has the potential to develop novel therapeutic agents, ultimately improving the success rates of treatments and reducing side effects.

Enhanced Efficiency

AI can automate routine tasks, such as appointment scheduling and prescription refills, allowing healthcare providers to focus on more complex tasks and improving overall efficiency.

Beyond automating routine tasks, AI can optimize clinical workflows by prioritizing urgent cases and streamlining the allocation of resources. For instance, AI-powered triage systems can assess patient symptoms and severity, enabling healthcare providers to focus on the most critical cases. Additionally, AI can facilitate better collaboration between healthcare professionals by providing real-time patient data and treatment recommendations, fostering more effective decision-making. In the long run, these improvements can lead to reduced wait times, cost savings, and overall better patient experiences.

Reduced Human Error

By assisting physicians in decision-making processes, AI can help minimize human error, leading to more accurate diagnoses and treatments.

While physicians strive to provide accurate and effective care, human error remains an inevitable part of healthcare. AI can serve as a valuable decision support tool, reducing the risk of errors in diagnosis and treatment. For example, AI-powered clinical decision support systems can analyze patient data, cross-reference it with current medical guidelines, and provide evidence-based recommendations to physicians. This assistance can help prevent errors due to fatigue, cognitive biases, or the sheer complexity of certain cases, ultimately improving patient safety and outcomes.

Remote Monitoring

Wearable devices and AI-powered applications can monitor patients remotely, providing real-time data to healthcare providers and enabling early intervention when needed.

Remote patient monitoring using AI can extend beyond wearable devices and applications, enabling more comprehensive and proactive care. For example, AI-powered telehealth platforms can analyze patient-reported data and vital signs to identify potential health concerns before they become critical. This approach allows physicians to intervene earlier and provide timely care, particularly for patients with chronic conditions. Additionally, remote monitoring can improve healthcare access for patients in rural or underserved areas, reducing the need for frequent in-person visits and helping to address health disparities.

Data Management

AI can help manage vast amounts of medical data, making it easier for physicians to access relevant information quickly and stay updated with the latest research.

Effective data management is crucial in the era of big data and precision medicine. AI can help physicians navigate vast medical databases, retrieving relevant information and identifying connections between seemingly disparate pieces of data. For example, natural language processing (NLP) algorithms can analyze and summarize the latest research articles, keeping healthcare providers informed of recent advancements. AI can also identify trends and correlations within electronic health records, helping physicians better understand patient populations and improve clinical decision-making. Ultimately, AI-driven data management can facilitate more efficient and evidence-based care.

AI in Medicine: Cons

Privacy Concerns

As AI systems collect and analyze sensitive medical information, concerns about patient privacy and data security arise. Ensuring the protection of patient data is essential.

As AI systems continue to gather and process vast amounts of sensitive patient data, the risk of data breaches and unauthorized access becomes a significant concern. Healthcare providers must ensure that robust security measures are in place to protect patient privacy and comply with regulations such as the Health Insurance Portability and Accountability Act (HIPAA). Additionally, the use of AI in medicine raises questions about patient consent and the ethical use of personal health information. To address these concerns, healthcare institutions must establish clear guidelines for data handling and maintain transparency in their AI applications.

Job Security

While AI can enhance efficiency, there are concerns that it may replace human healthcare providers. Physicians must adapt to these technological changes and develop new skills to remain relevant.

While AI has the potential to enhance healthcare efficiency, it also raises concerns about job displacement for healthcare professionals. The automation of routine tasks may lead to a reduced demand for certain roles, such as medical transcriptionists and administrative staff. However, AI is not expected to replace physicians entirely. Instead, the medical profession will likely evolve, with physicians developing new skills to effectively collaborate with AI systems. Physicians must be prepared to adapt to these changes by embracing continuous learning and integrating AI into their practices to ensure their relevance in the future healthcare landscape.


Despite AI’s potential for reducing human error, it may also lead to misdiagnoses if it is not properly trained or if it encounters rare or complex cases.

Despite the advancements in AI, there remains the potential for misdiagnoses due to the limitations of current algorithms. AI systems are only as effective as the data they are trained on, and if the training data is not diverse or representative, the AI may struggle to diagnose rare or complex cases. Additionally, AI systems are susceptible to overfitting, which can lead to overconfidence in their predictions and incorrect diagnoses. To mitigate these risks, AI systems must be rigorously tested and validated, and physicians must maintain a critical perspective when relying on AI-driven insights, ensuring that they consider the full clinical context when making decisions.

Ethical Dilemmas

AI raises ethical questions, such as the transparency of decision-making processes and the potential for biased algorithms. Physicians must navigate these challenges carefully.

The integration of AI in medicine raises several ethical concerns that physicians must navigate. For instance, algorithmic biases may emerge if AI systems are trained on biased data, leading to discriminatory outcomes. To prevent this, developers must prioritize fairness and transparency in their AI models, and physicians must be aware of potential biases when interpreting AI-generated recommendations. Furthermore, the use of AI can blur the lines of accountability in medical decision-making, raising questions about the autonomy of both patients and physicians. It is crucial that healthcare providers maintain open communication with their patients, addressing any concerns related to AI and ensuring that patients understand the role of AI in their care.

Financial and Technical Barriers

The adoption of AI in healthcare can be hindered by financial and technical barriers, particularly for smaller or resource-limited institutions. The development and implementation of AI systems can be costly, and the ongoing maintenance and updates may strain healthcare budgets. Additionally, the integration of AI into existing healthcare infrastructures may be technically challenging, requiring significant investments in hardware, software, and personnel training. To ensure equitable access to AI-driven healthcare innovations, policymakers and industry leaders must work together to develop strategies that overcome these barriers and foster the widespread adoption of AI in medicine.

The FDA and AI Chatbots

The FDA will likely need to play a role in approving AI chatbots for patient use, as these tools can potentially diagnose and recommend treatments. The regulatory process will be critical in ensuring the safety and effectiveness of AI chatbots in healthcare.

Liability for Medical Errors

Determining liability for medical errors resulting from AI is a complex issue. As AI systems become more integrated into healthcare, it is crucial to establish clear guidelines for liability, including the responsibilities of physicians, AI developers, and other stakeholders.


AI has the potential to significantly impact medicine, offering numerous benefits while also raising concerns. As physicians, we must carefully weigh the pros and cons, adapt to these technological changes, and ensure patient safety and privacy. Moreover, the FDA will need to play a critical role in regulating AI chatbots, and clear liability guidelines must be established

I then asked ChatGPT a couple of follow-up questions:

  1. Should there be a concern about the data sets used in medical AI? Please write in authoritative tone, analytical writing style, English language.
  2. Should these data sets be open source to ensure the integrity of the data used?

Yes, there should be a legitimate concern about the datasets used in medical AI. Ensuring the quality, diversity, and representativeness of the data used to train AI systems is essential for their effectiveness and fairness. Several factors contribute to these concerns:

  1. Data Quality: The accuracy and reliability of the data used to train AI systems are crucial for their performance. Inaccurate or incomplete data can lead to incorrect predictions, which may negatively impact patient outcomes. Healthcare providers and AI developers must ensure that the datasets used are of high quality and derived from reliable sources.
  2. Data Diversity: Diverse datasets are necessary to ensure that AI systems can recognize and diagnose a wide range of medical conditions across different populations. Datasets lacking diversity may result in AI systems that are biased towards specific demographic groups, leading to disparities in diagnosis and treatment. Ensuring that datasets include diverse patient populations, including different age groups, genders, ethnicities, and socioeconomic backgrounds, is essential for the equitable performance of AI in medicine.
  3. Data Representativeness: AI systems must be trained on representative datasets to generalize effectively across various medical scenarios. If a dataset is not representative of the broader patient population or the full spectrum of medical conditions, AI systems may struggle to perform well in real-world settings. Developers must ensure that their training data accurately reflects the target population and the range of medical conditions that the AI system is intended to address.
  4. Data Privacy and Security: The use of sensitive patient data in medical AI raises concerns about privacy and security. Ensuring that patient data is anonymized, encrypted, and securely stored is crucial to protect patient privacy and comply with data protection regulations. Furthermore, establishing clear guidelines for data handling and maintaining transparency in AI applications can help address patient concerns about the ethical use of their personal health information.

In conclusion, the datasets used in medical AI play a critical role in determining the effectiveness, fairness, and safety of these systems. Healthcare providers and AI developers must carefully consider the quality, diversity, representativeness, and security of the data they use to train AI systems to ensure their responsible and ethical application in medicine.

While making medical datasets open source has the potential to improve the integrity and quality of the data used in AI systems, there are several factors to consider when evaluating the merits of open sourcing such datasets.

Pros of Open Source Medical Datasets:

  1. Transparency: Open source datasets allow for greater transparency, enabling researchers and developers to scrutinize and validate the data used in AI systems. This can help identify errors, biases, or inconsistencies in the data, ultimately improving the performance and fairness of AI applications.
  2. Collaboration: Open source datasets facilitate collaboration among researchers, healthcare providers, and AI developers. By pooling resources and expertise, the medical community can work together to improve data quality, enhance AI algorithms, and drive innovation in the field.
  3. Accessibility: Making medical datasets open source can reduce barriers to entry for researchers and AI developers, particularly those from underrepresented or resource-limited backgrounds. This can help democratize access to valuable data and promote a more diverse and inclusive AI research community.

Cons of Open Source Medical Datasets:

  1. Data Privacy and Security: The primary concern with making medical datasets open source is the potential risk to patient privacy and data security. Sharing sensitive patient information in an open format can make it vulnerable to unauthorized access and misuse. To mitigate this risk, strict anonymization and de-identification protocols must be in place, and data sharing agreements may be necessary to ensure ethical data use.
  2. Data Quality Control: Ensuring data quality in open source datasets can be challenging, as multiple contributors may introduce errors or inconsistencies. Rigorous quality control processes must be established to maintain the integrity of open source medical datasets.
  3. Intellectual Property and Legal Issues: Open sourcing medical datasets may raise intellectual property concerns and legal issues, particularly when data is derived from proprietary sources or involves complex data sharing agreements. Navigating these challenges can be time-consuming and may deter some organizations from sharing their datasets openly.

In conclusion, while open sourcing medical datasets can offer numerous benefits in terms of transparency, collaboration, and accessibility, it also raises concerns about data privacy, security, and quality control. Balancing these factors is crucial to ensure the responsible and ethical use of medical data in AI development. A potential compromise could involve creating well-curated and controlled data repositories, with strict access controls and rigorous ethical guidelines, to facilitate responsible data sharing and collaboration in the medical AI community.

It’s going to be an interesting time in medicine (and other professions). I have seen some red-flags in my first month of using ChatGPT that do concern me. One alarming example is that in another “chat” I was asking about medical studies that supported a certain medical treatment. While the response in general seemed appropriate, two of the studies that ChatGPT listed in it’s response DID NOT EXIST…

Yep, ChatGPT “made up” or maybe “confabulated” about two medical studies that it referenced in its answer. Granted, its a natural language processing program and it does that part surprisingly well, but there has to be a healthy concern about wrong information being propagated.

About the Author

John Martinez, M.D.

Primary Care Sports Medicine physician with an engineering background interested in digital health and technology solutions in medicine.

Assistant Medical Director, Ironman Triathlon World Championships (Kona)
Medical Director, USA Triathlon Paralympic Team
Medical Director, USA Blind Soccer and Goalball

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