OBJECTIVE: Synthesizing and evaluating inconsistent medical evidence is essential in evidence-based medicine. This study aimed to employ ChatGPT as a sophisticated scientific reasoning engine to identify conflicting clinical evidence and summarize unresolved questions to inform further research. MATERIALS AND METHODS: We evaluated ChatGPT's effectiveness in identifying conflicting evidence and investigated its principles of logical reasoning. An automated framework was developed to generate a PubMed dataset focused on controversial clinical topics. ChatGPT analyzed this dataset to identify consensus and controversy, and to formulate unsolved research questions. Expert evaluations were conducted 1) on the consensus and controversy for factual consistency, comprehensiveness, and potential harm and, 2) on the research questions for relevance, innovation, clarity, and specificity. RESULTS: The gpt-4-1106-preview model achieved a 90% recall rate in detecting inconsistent claim pairs within a ternary assertions setup. Notably, without explicit reasoning prompts, ChatGPT provided sound reasoning for the assertions between claims and hypotheses, based on an analysis grounded in relevance, specificity, and certainty. ChatGPT's conclusions of consensus and controversies in clinical literature were comprehensive and factually consistent. The research questions proposed by ChatGPT received high expert ratings. DISCUSSION: Our experiment implies that, in evaluating the relationship between evidence and claims, ChatGPT considered more detailed information beyond a straightforward assessment of sentimental orientation. This ability to process intricate information and conduct scientific reasoning regarding sentiment is noteworthy, particularly as this pattern emerged without explicit guidance or directives in prompts, highlighting ChatGPT's inherent logical reasoning capabilities. CONCLUSION: This study demonstrated ChatGPT's capacity to evaluate and interpret scientific claims. Such proficiency can be generalized to broader clinical research literature. ChatGPT effectively aids in facilitating clinical studies by proposing unresolved challenges based on analysis of existing studies. However, caution is advised as ChatGPT's outputs are inferences drawn from the input literature and could be harmful to clinical practice.

The massive amount of data generated by the Internet of Things (IoT) and the need to store that data presents a huge challenge for storage. However, meeting this challenge has also driven the development of storage technologies, especially those related to cloud storage. Although attribute-based encryption (ABE) schemes are commonly used to achieve data confidentiality and fine-grained access control in cloud storage, there is still an inherent problem with ABE schemes, namely the key escrow problem. In this paper, we propose a revocable blockchain-aided ABE with escrow-free (BC-ABE-EF) system that resolves the key escrow problem by replacing the traditional key authority with a consortium blockchain. The keys are generated between the blockchain and the data user through a secure key issuing protocol, and the blockchain cannot obtain the user's full key alone. Furthermore, utilize the decryption cloud server to schedule pre-decryption operations in cloud and introduce a group manager to update the group keys of unrecovered users and generate re-encryption keys. The security analysis shows that our scheme is secure under the Decisional Computation Diffie Hellman (DCDH) assumption. The effectiveness of the scheme is demonstrated by simulating the BC-ABE-EF scheme and comparing it based on performance analysis.