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How Can AI Assist a Writer or Editor? By John R. Patrick

This week, I read an interesting interview in HealthcareITNews titled “Siemens Healthineers digital chief on what ChatGPT and other AI might mean for healthcare”. I decided to try an experiment. The interview was approximately 1,500 words. I used chatGPT and, after pasting the entire article into the input field, I asked the AI to “summarize the following into an article no longer than 1000 words”. The AI responded in1-2 seconds with exactly what I asked for. It converted the interview into an article with an introduction and gave it the title “AI in Healthcare: The Quest for Trust and Adoption”. The title was completely different than the original interview.

Next, I typed into the input field, “summarize it further to 750 words with a three sentence conclusion”. A second later, the AI responded with a new title, introduction, paragraph titles, and a conclusion. The article produced by the AI is below.

The experiment showed what I expected, awesome speed and intelligence to read, analyze, and write a brand-new article meeting my specifications. The experiment also raises some questions. Is the newly created article completely accurate? Would the conversion from an interview to an article constitute plagiarism? What impact would the capabilities of the experiment/demonstration have on people who write and edit for a living?

I have talked to quite a few physicians and healthcare administrators about AI. They all see the possible risks to patient safety and quality. They see the need for establishment of guiderails and regulation. They also see great potential for reduced time spent on paperwork. I asked chatGPT AI about this. It said various studies and surveys show physicians spend a significant amount of their time on paperwork and administrative tasks. While the specific percentage may vary depending on the specialty and healthcare setting, it is estimated physicians spend approximately 27% to 37% of their time on administrative tasks, including paperwork, documentation, and electronic health record (EHR) management.    

Navigating the Path to AI Adoption in Healthcare by chatGPT AI


Artificial intelligence (AI) has the potential to revolutionize healthcare, but concerns about trust and slow adoption hinder its progress. In an interview with Peter Shen, Head of the Digital and Automation Business at Siemens Healthineers, the transformative potential of AI in healthcare is explored. This article provides a concise summary of the interview, discussing AI implications, adoption challenges, and the roadmap to building trust and wider adoption.

AI’s Potential in Healthcare:

The emergence of ChatGPT, an interactive chatbot powered by machine learning, highlights AI’s paradigm-shifting potential. Despite concerns about its misuse and reliability, AI tools like ChatGPT can help clinicians digest and summarize vast amounts of textual data, enabling them to remain current with the latest scientific research and publications. This application has the potential to improve diagnosis and treatment paradigms by leveraging scientifically validated data.

Adoption Challenges:

The healthcare industry has been slow to adopt AI due to early concerns about job replacement and skepticism regarding AI’s broader value. However, as AI has proven its worth in specific areas like radiology, automating tasks and enhancing pattern recognition, perceptions have evolved. Yet, questions surrounding the contextualization of additional data, potential biases, and demonstrating broader value to clinicians, patients, and healthcare institutions remain. Overcoming these challenges is vital to gaining wider acceptance.

Building Trust and Increasing Adoption:

To increase AI adoption, AI must clearly demonstrate tangible benefits to radiologists and clinicians. AI models should provide rationale for their findings, helping clinicians understand how additional information influences diagnosis and treatment decisions. Continuous training on diverse patient populations can mitigate implicit biases. Seamless integration into the clinical workflow, where AI complements existing routines without being intrusive, is crucial. It is equally important to dispel the notion that AI replaces clinicians; instead, AI is designed to assist and support clinical decision-making.

The Future of AI in Healthcare:

The next generation of health AI lies in integrating multiple data sources through a middleware platform, enabling AI to find correlations and provide more informed diagnoses and personalized treatment decisions. This approach has the potential to scale personalized medicine, improving patient care on a broader scale. Additionally, creating accurate patient anatomical models, or “digital twins,” can transform treatment planning and noninvasive evaluation of treatment efficacy. AI’s broader value lies in its ability to provide precise diagnoses and personalized treatments for individuals while scaling those approaches to affect entire patient cohorts.


The transformative potential of AI in healthcare is undeniable. However, challenges related to trust and adoption need to be addressed for AI to reach its full potential. By clearly demonstrating tangible benefits, addressing biases, seamlessly integrating into clinical workflows, and emphasizing AI as an assistant rather than a replacement, the healthcare industry can overcome these challenges. The future of AI in healthcare lies in its ability to provide personalized and precise care, while also transforming healthcare systems into wellness-focused environments. As we navigate this path, it is crucial to strike a balance between the power of AI and the trust placed in human clinicians, ensuring that AI serves as a valuable tool in improving patient outcomes.