Background
As the available information about breast cancer is growing every day, the decision-making process for the therapy is getting more complex. ChatGPT as a transformer-based language model possesses the ability to write scientific articles and pass medical exams. But is it able to support the multidisciplinary tumor board (MDT) in the planning of the therapy of patients with breast cancer?
Material and Methods
We performed a pilot study on 10 consecutive cases of breast cancer patients discussed in MDT at our department in January 2023. Included were patients with a primary diagnosis of early breast cancer. The recommendation of MDT was compared with the recommendation of the ChatGPT for particular patients and the clinical score of the agreement was calculated.
Results
Results showed that ChatGPT provided mostly general answers regarding chemotherapy, breast surgery, radiation therapy, chemotherapy, and antibody therapy. It was able to identify risk factors for hereditary breast cancer and point out the elderly patient indicated for chemotherapy to evaluate the cost/benefit effect. ChatGPT wrongly identified the patient with Her2 1 + and 2 + (FISH negative) as in need of therapy with an antibody and called endocrine therapy “hormonal treatment”.
Conclusions
Support of artificial intelligence by finding individualized and personalized therapy for our patients in the time of rapidly expanding amount of information is looking for the ways in the clinical routine. ChatGPT has the potential to find its spot in clinical medicine, but the current version is not able to provide specific recommendations for the therapy of patients with primary breast cancer.
As the available information about breast cancer is growing every day, the decision-making process for the therapy is getting more complex. ChatGPT as a transformer-based language model possesses the ability to write scientific articles and pass medical exams. But is it able to support the multidisciplinary tumor board (MDT) in the planning of the therapy of patients with breast cancer?
We performed a pilot study on 10 consecutive cases of breast cancer patients discussed in MDT at our department in January 2023. Included were patients with a primary diagnosis of early breast cancer. The recommendation of MDT was compared with the recommendation of the ChatGPT for particular patients and the clinical score of the agreement was calculated.
Results showed that ChatGPT provided mostly general answers regarding chemotherapy, breast surgery, radiation therapy, chemotherapy, and antibody therapy. It was able to identify risk factors for hereditary breast cancer and point out the elderly patient indicated for chemotherapy to evaluate the cost/benefit effect. ChatGPT wrongly identified the patient with Her2 1+ and 2+ (FISH negative) as in need of therapy with trastuzumab and called endocrine therapy “hormonal treatment”.
Support of artificial intelligence by finding individualized and personalized therapy for our patients is unavoidable in this time of rapidly expanding amount of information. ChatGPT has the potential to find its spot in clinical medicine, but the current version is not able to provide specific recommendations for the therapy of patients with primary breast cancer.
Purpose Systematic evaluation of health apps designed to support and aid remote monitoring of patients during breast cancer treatment and aftercare.
Method A systematic search and assessment of apps was conducted using search terms: breast cancer; breast cancer therapy; and breast cancer aftercare. Evaluation criteria were user
assessments, scientifically published benefits, user-friendliness, data protection, app individualization, motivation, and financial aspects. Up to two points (P) could be awarded per
criterion. The lowest possible score was 0P and the maximum was 28P. Three examiners from different institutions independently assessed the apps according to the specified criteria.
Reference value was defined as the average value given by the examiners. Apps with > 18P were classified as “recommended”; ≥ 11–≤ 18P as “partially recommended” and ≤ 10P as “not
recommended”.
Results A total of 776 apps (n = 24 from the Apple App Store, n = 752 from the Google Play Store) were identified via search query. After applying exclusion criteria, 36 apps (n = 1
from the Apple App Store; n = 35 from the Google Play Store) were evaluated. Using the mean point values of the examiners, 20 apps were classified as not recommended and 12 as partially
recommended (≥ 11–≤ 18P). Four apps were rated partially recommended by two examiners and recommended by one examiner. Three apps were rated as recommended by all examiners.
Conclusion Only a small minority of available apps meet recommendation criteria. Use of these apps may benefit breast cancer patients.
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