AI is great at coding, but there are some massive caveats

SEAN GLADWELL/Getty ImagesCreating and testing code at the touch of a button through generative artificial intelligence (AI) models, such as GitHub CoPilot or ChatGPT, almost seems too good to be true. So good, in fact, that there has to be a catch.

While software professionals are embracing AI as a power tool to build, launch, and update applications, there is also nervousness about its intellectual property and security implications. Is that AI-generated code scraped from someone else’s intellectual property? Does the model draw on internal corporate data that should be kept secure?

Also: How to use ChatGPT to write code

Technologists recognize that AI adoption requires attention to rights, privacy, security, productivity, and training, according to a GitLab survey of 1,001 developers and executives, conducted in June.

The majority of respondents (79%) expressed concern about AI tools having access to private information or intellectual property. The main concern was that sensitive information, such as customer data, may be exposed.

Copyright concerns top the list of concerns about using AI-generated code. Close to half of respondents (48%) cited concern that code generated using AI might not be subject to the same copyright protection as human-generated code. Another 39% were worried about security vulnerabilities with such code.

Also: 6 skills you need to become an AI prompt engineer

Still, technologists are optimistic that these issues can be worked through and they continue to forge ahead. Among respondents whose organizations are using AI in software development today, as many as 90% felt confident using AI in their daily tasks at work. In addition, 60% said they use AI daily, and 22% said they use AI several times a week. More than half (51%) rated their organization’s efforts in incorporating AI into the software development lifecycle as “very” or “extremely” successful.

AI is seen as an important investment from a software development perspective. Among respondents whose organizations are using AI or plan to in the future, 83% said they have or will have budget specifically allocated to AI for software development. Benefits cited included improved efficiency (55%), faster cycle times (44%), and increased innovation (41%).

Training and skills also emerged as a common theme in the obstacles and concerns identified by respondents. As much as 81% said they need more training to use AI at work, and 87% said organizations will need to re-skill employees to adapt to the changes AI will bring. A top area of concern was the potential to introduce a new set of skills to learn (42%), followed by a lack of the appropriate skill sets to use AI or interpret AI output (34%).

Also: 5 ways to explore the use of generative AI at work

The bottom line is that AI cannot replace human oversight and innovation. More experienced professionals “accept AI as a supportive tool for skill development, but don’t think it can completely replace the expertise, knowledge, and problem-solving of seasoned professionals like themselves,” the survey’s authors assert.

“Ultimately, it comes down to more than simply human versus machine. Leveraging the experience of human team members alongside AI is the best — and perhaps only — way organizations can fully address the concerns around security and intellectual property.”

AI might be able to generate code more quickly than a human developer, “but a human team member needs to verify that the AI-generated code is free of errors, security vulnerabilities, or copyright issues before it goes to production,” they said.

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To Make the Most of AI in Medical Billing and Coding, Health Systems Need Well-Trained Certified Coders

Can hospitals and healthcare networks generate more efficiency and savings through medical billing and coding?

In a transformative move for healthcare, Dr. Matthew Hitchcock and over 1,000 physicians at the University of Pittsburgh are harnessing the power of AI to streamline patient documentation, cutting down hours of clerical work to mere minutes. Companies like Abridge are at the forefront, offering solutions that ease doctors’ workloads and enhance patient engagement by providing accessible visit summaries. However, while the potential of AI in medical diagnosis is vast, professionals like Dr. Hitchcock remain cautious, prioritizing patient safety and regulatory compliance.

If AI can assist in streamlining patient documentation, what about medical coding? Can medical billing coders utilize these tools to enhance the process and create efficiencies across the whole operation.

Matthew Isaacson, a 15-year veteran of revenue cycle management, says there are positives to exploring AI in medical billing and coding. To do it right, certified coders should drive the train.

Matthew’s Thoughts“What is definitely being used is clinicians and revenue cycle staff work together to translate clinical encounters into billable codes for reimbursement. AI is integrated there because it drives efficiency and cost-effectiveness for these organizations. With the great resignation affecting the healthcare industry as much as any other, AI has played a crucial role in filling some of those gaps. However, it’s a double-edged sword. There are definite pros to AI’s involvement, but there are cons as well.”

The Evolution of Technology in Healthcare

“Technology is evolving in healthcare more than ever. AI should play a role because it’s driving efficiency and cost-effectiveness. But AI is being used in partnership with IT technology, and data integrity is key. If you’re going to utilize AI in healthcare, you need to ensure that your coders are incorporating appropriate codes. If that’s not happening, then AI isn’t going to be effective. It’s proven. There are growing pains. If someone is going to implement AI within their coding structure, they need to ensure that from the beginning, their coders are using the appropriate codes. Using any AI engine, a predictive model is created. It’s the garbage in, garbage out mentality. They need to ensure that the infrastructure is in place from the beginning to make sure that the AI modeling is as accurate as possible. AI has many benefits within healthcare, but it’s not the silver bullet. There must be infrastructure and a strategy in place for its deployment.”

The Importance of Certified Healthcare Billing Coders

“Using certified coders is an absolute must. It comes down to ensuring that the medical coder is certified and trained and that quality control is in place to ensure they’re using appropriate codes. If they’re not doing that, there will be garbage in and garbage out. Additionally, by not using proper coding it’s driving denials and barriers to reimbursement. It’s essential that these coders implement the appropriate codes so that the AI model can take flight.”

Article by James Kent

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